examples of research topics in medical laboratory science

77 interesting medical research topics for 2024

Last updated

25 November 2023

Reviewed by

Brittany Ferri, PhD, OTR/L

Medical research is the gateway to improved patient care and expanding our available treatment options. However, finding a relevant and compelling research topic can be challenging.

Use this article as a jumping-off point to select an interesting medical research topic for your next paper or clinical study.

• How to choose a medical research topic

When choosing a research topic , it’s essential to consider a couple of things. What topics interest you? What unanswered questions do you want to address? 

During the decision-making and brainstorming process, here are a few helpful tips to help you pick the right medical research topic:

Focus on a particular field of study

The best medical research is specific to a particular area. Generalized studies are often too broad to produce meaningful results, so we advise picking a specific niche early in the process. 

Maybe a certain topic interests you, or your industry knowledge reveals areas of need.

Look into commonly researched topics

Once you’ve chosen your research field, do some preliminary research. What have other academics done in their papers and projects? 

From this list, you can focus on specific topics that interest you without accidentally creating a copycat project. This groundwork will also help you uncover any literature gaps—those may be beneficial areas for research.

Get curious and ask questions

Now you can get curious. Ask questions that start with why, how, or what. These questions are the starting point of your project design and will act as your guiding light throughout the process. 

For example: 

What impact does pollution have on children’s lung function in inner-city neighborhoods? 

Why is pollution-based asthma on the rise? 

How can we address pollution-induced asthma in young children? 

• 77 medical research topics worth exploring in 2023

Need some research inspiration for your upcoming paper or clinical study? We’ve compiled a list of 77 topical and in-demand medical research ideas. Let’s take a look. 

• Exciting new medical research topics

If you want to study cutting-edge topics, here are some exciting options:

COVID-19 and long COVID symptoms

Since 2020, COVID-19 has been a hot-button topic in medicine, along with the long-term symptoms in those with a history of COVID-19. 

Examples of COVID-19-related research topics worth exploring include:

The long-term impact of COVID-19 on cardiac and respiratory health

COVID-19 vaccination rates

The evolution of COVID-19 symptoms over time

New variants and strains of the COVID-19 virus

Changes in social behavior and public health regulations amid COVID-19

Vaccinations

Finding ways to cure or reduce the disease burden of chronic infectious diseases is a crucial research area. Vaccination is a powerful option and a great topic to research. 

Examples of vaccination-related research topics include:

mRNA vaccines for viral infections

Biomaterial vaccination capabilities

Vaccination rates based on location, ethnicity, or age

Public opinion about vaccination safety 

Artificial tissues fabrication

With the need for donor organs increasing, finding ways to fabricate artificial bioactive tissues (and possibly organs) is a popular research area. 

Examples of artificial tissue-related research topics you can study include:

The viability of artificially printed tissues

Tissue substrate and building block material studies

The ethics and efficacy of artificial tissue creation

• Medical research topics for medical students

For many medical students, research is a big driver for entering healthcare. If you’re a medical student looking for a research topic, here are some great ideas to work from:

Sleep disorders

Poor sleep quality is a growing problem, and it can significantly impact a person’s overall health. 

Examples of sleep disorder-related research topics include:

How stress affects sleep quality

The prevalence and impact of insomnia on patients with mental health conditions

Possible triggers for sleep disorder development

The impact of poor sleep quality on psychological and physical health

How melatonin supplements impact sleep quality

Alzheimer’s and dementia 

Cognitive conditions like dementia and Alzheimer’s disease are on the rise worldwide. They currently have no cure. As a result, research about these topics is in high demand. 

Examples of dementia-related research topics you could explore include:

The prevalence of Alzheimer’s disease in a chosen population

Early onset symptoms of dementia

Possible triggers or causes of cognitive decline with age

Treatment options for dementia-like conditions

The mental and physical burden of caregiving for patients with dementia

• Lifestyle habits and public health

Modern lifestyles have profoundly impacted the average person’s daily habits, and plenty of interesting topics explore its effects. 

Examples of lifestyle and public health-related research topics include:

The nutritional intake of college students

The impact of chronic work stress on overall health

The rise of upper back and neck pain from laptop use

Prevalence and cause of repetitive strain injuries (RSI)

• Controversial medical research paper topics

Medical research is a hotbed of controversial topics, content, and areas of study. 

If you want to explore a more niche (and attention-grabbing) concept, here are some controversial medical research topics worth looking into:

The benefits and risks of medical cannabis

Depending on where you live, the legalization and use of cannabis for medical conditions is controversial for the general public and healthcare providers.

Examples of medical cannabis-related research topics that might grab your attention include:

The legalization process of medical cannabis

The impact of cannabis use on developmental milestones in youth users

Cannabis and mental health diagnoses

CBD’s impact on chronic pain

Prevalence of cannabis use in young people

The impact of maternal cannabis use on fetal development 

Understanding how THC impacts cognitive function

Human genetics

The Human Genome Project identified, mapped, and sequenced all human DNA genes. Its completion in 2003 opened up a world of exciting and controversial studies in human genetics.

Examples of human genetics-related research topics worth delving into include:

Medical genetics and the incidence of genetic-based health disorders

Behavioral genetics differences between identical twins

Genetic risk factors for neurodegenerative disorders

Machine learning technologies for genetic research

Sexual health studies

Human sexuality and sexual health are important (yet often stigmatized) medical topics that need new research and analysis.

As a diverse field ranging from sexual orientation studies to sexual pathophysiology, examples of sexual health-related research topics include:

The incidence of sexually transmitted infections within a chosen population

Mental health conditions within the LGBTQIA+ community

The impact of untreated sexually transmitted infections

Access to safe sex resources (condoms, dental dams, etc.) in rural areas

• Health and wellness research topics

Human wellness and health are trendy topics in modern medicine as more people are interested in finding natural ways to live healthier lifestyles. 

If this field of study interests you, here are some big topics in the wellness space:

Gluten sensitivity

Gluten allergies and intolerances have risen over the past few decades. If you’re interested in exploring this topic, your options range in severity from mild gastrointestinal symptoms to full-blown anaphylaxis. 

Some examples of gluten sensitivity-related research topics include:

The pathophysiology and incidence of Celiac disease

Early onset symptoms of gluten intolerance

The prevalence of gluten allergies within a set population

Gluten allergies and the incidence of other gastrointestinal health conditions

Pollution and lung health

Living in large urban cities means regular exposure to high levels of pollutants. 

As more people become interested in protecting their lung health, examples of impactful lung health and pollution-related research topics include:

The extent of pollution in densely packed urban areas

The prevalence of pollution-based asthma in a set population

Lung capacity and function in young people

The benefits and risks of steroid therapy for asthma

Pollution risks based on geographical location

Plant-based diets

Plant-based diets like vegan and paleo diets are emerging trends in healthcare due to their limited supporting research. 

If you’re interested in learning more about the potential benefits or risks of holistic, diet-based medicine, examples of plant-based diet research topics to explore include:

Vegan and plant-based diets as part of disease management

Potential risks and benefits of specific plant-based diets

Plant-based diets and their impact on body mass index

The effect of diet and lifestyle on chronic disease management

Health supplements

Supplements are a multi-billion dollar industry. Many health-conscious people take supplements, including vitamins, minerals, herbal medicine, and more. 

Examples of health supplement-related research topics worth investigating include:

Omega-3 fish oil safety and efficacy for cardiac patients

The benefits and risks of regular vitamin D supplementation

Health supplementation regulation and product quality

The impact of social influencer marketing on consumer supplement practices

Analyzing added ingredients in protein powders

• Healthcare research topics

Working within the healthcare industry means you have insider knowledge and opportunity. Maybe you’d like to research the overall system, administration, and inherent biases that disrupt access to quality care. 

While these topics are essential to explore, it is important to note that these studies usually require approval and oversight from an Institutional Review Board (IRB). This ensures the study is ethical and does not harm any subjects. 

For this reason, the IRB sets protocols that require additional planning, so consider this when mapping out your study’s timeline. 

Here are some examples of trending healthcare research areas worth pursuing:

The pros and cons of electronic health records

The rise of electronic healthcare charting and records has forever changed how medical professionals and patients interact with their health data. 

Examples of electronic health record-related research topics include:

The number of medication errors reported during a software switch

Nurse sentiment analysis of electronic charting practices

Ethical and legal studies into encrypting and storing personal health data

Inequities within healthcare access

Many barriers inhibit people from accessing the quality medical care they need. These issues result in health disparities and injustices. 

Examples of research topics about health inequities include:

The impact of social determinants of health in a set population

Early and late-stage cancer stage diagnosis in urban vs. rural populations

Affordability of life-saving medications

Health insurance limitations and their impact on overall health

Diagnostic and treatment rates across ethnicities

People who belong to an ethnic minority are more likely to experience barriers and restrictions when trying to receive quality medical care. This is due to systemic healthcare racism and bias. 

As a result, diagnostic and treatment rates in minority populations are a hot-button field of research. Examples of ethnicity-based research topics include:

Cancer biopsy rates in BIPOC women

The prevalence of diabetes in Indigenous communities

Access inequalities in women’s health preventative screenings

The prevalence of undiagnosed hypertension in Black populations

• Pharmaceutical research topics

Large pharmaceutical companies are incredibly interested in investing in research to learn more about potential cures and treatments for diseases. 

If you’re interested in building a career in pharmaceutical research, here are a few examples of in-demand research topics:

Cancer treatment options

Clinical research is in high demand as pharmaceutical companies explore novel cancer treatment options outside of chemotherapy and radiation. 

Examples of cancer treatment-related research topics include:

Stem cell therapy for cancer

Oncogenic gene dysregulation and its impact on disease

Cancer-causing viral agents and their risks

Treatment efficacy based on early vs. late-stage cancer diagnosis

Cancer vaccines and targeted therapies

Immunotherapy for cancer

Pain medication alternatives

Historically, opioid medications were the primary treatment for short- and long-term pain. But, with the opioid epidemic getting worse, the need for alternative pain medications has never been more urgent. 

Examples of pain medication-related research topics include:

Opioid withdrawal symptoms and risks

Early signs of pain medication misuse

Anti-inflammatory medications for pain control

• Identify trends in your medical research with Dovetail

Are you interested in contributing life-changing research? Today’s medical research is part of the future of clinical patient care. 

As your go-to resource for speedy and accurate data analysis , we are proud to partner with healthcare researchers to innovate and improve the future of healthcare.

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Background: Hospital wastewaters contain blends of inorganic, natural constituents and contaminants that carry significant health risk when released directly into the environment. The aim of this study is to investigate the correlation between faecal indicator bacteria in diarrheagenic stools and wastewaters generated in University of Medical Sciences Teaching Hospital complex, Akure, Nigeria.Methodology: Quantification of faecal indicator bacteria was carried out on diarrheagenic faecal samples collected from 55 hospitalized patients and 68 wastewater samples from the medical laboratory science and laundry units of the hospital over of period of 12 weeks. Standard membrane filtration technique was performed using membrane intestinal enterococcus (m-ENT), membrane faecal coliform (m-FC), membrane lauryl sulphate (MLSA), eosin methylene blue (EMB) and Salmonella-Shigella (SS) agar plates, which were incubated at 37ºC for 24 hours (MLSA, EMB and SSA), 44ºC for 24 hours (m-FC); and 37ºC for 48 hours (m-ENT). Bacterial colonies on agar plates were counted and expressed as colony forming units (CFU) per 100ml of diarrheagenic stool and wastewater. Pearson’scorrelation analysis was used to determine the relationship between the level of faecal indicator bacteria in diarrheagenic stools and wastewaters at p<0.05 level of significance (and 95% confidence interval).Results: The faecal coliform counts (log 10 CFU/100ml) ranged from 1.18 to 1.54 in diarrheagenic stools, 1.32 to1.64 in laboratory wastewater and 1.08 to 2.19 in laundry wastewater. Escherichia coli count (log 10 CFU/100ml) ranged from 1.08 to 1.40 in diarrheagenic stools, 1.20 to 1.86 in laboratory wastewater and 0.30 to 1.81 in laundry wastewater. Intestinal enterococci count (log 10 CFU/100ml) ranged from 0 to 0.30 in diarrheagenic stools, 0.78 to 0.90 in laboratory wastewaters and 0.48 to 1.11 in laundry wastewaters. Pearson’s correlation co-efficient showed that all the faecal indicator bacteria count in diarrheagenic faecal samples exhibited positive correlation with those in laboratory wastewaters, but not with those from laundry wastewaters.Conclusion: The findings suggest that diarrheagenic stools should be properly disinfected after the performance of laboratory tests to prevent transmission of potential pathogens, and wastewater generated from hospitals should be treated prior to discharge into the environment, to prevent possible infections in the community. Keywords: Correlation, faecal indicator bacteria, public health, transmission, wastewater

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Research Topics & Ideas: Healthcare

100+ Healthcare Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a healthcare-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of healthcare-related research ideas and topic thought-starters across a range of healthcare fields, including allopathic and alternative medicine, dentistry, physical therapy, optometry, pharmacology and public health.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the healthcare domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: Healthcare Research Topics

• Allopathic medicine
• Alternative /complementary medicine
• Veterinary medicine
• Physical therapy/ rehab
• Optometry and ophthalmology
• Pharmacy and pharmacology
• Public health
• Examples of healthcare-related dissertations

Allopathic (Conventional) Medicine

• The effectiveness of telemedicine in remote elderly patient care
• The impact of stress on the immune system of cancer patients
• The effects of a plant-based diet on chronic diseases such as diabetes
• The use of AI in early cancer diagnosis and treatment
• The role of the gut microbiome in mental health conditions such as depression and anxiety
• The efficacy of mindfulness meditation in reducing chronic pain: A systematic review
• The benefits and drawbacks of electronic health records in a developing country
• The effects of environmental pollution on breast milk quality
• The use of personalized medicine in treating genetic disorders
• The impact of social determinants of health on chronic diseases in Asia
• The role of high-intensity interval training in improving cardiovascular health
• The efficacy of using probiotics for gut health in pregnant women
• The impact of poor sleep on the treatment of chronic illnesses
• The role of inflammation in the development of chronic diseases such as lupus
• The effectiveness of physiotherapy in pain control post-surgery

Topics & Ideas: Alternative Medicine

• The benefits of herbal medicine in treating young asthma patients
• The use of acupuncture in treating infertility in women over 40 years of age
• The effectiveness of homoeopathy in treating mental health disorders: A systematic review
• The role of aromatherapy in reducing stress and anxiety post-surgery
• The impact of mindfulness meditation on reducing high blood pressure
• The use of chiropractic therapy in treating back pain of pregnant women
• The efficacy of traditional Chinese medicine such as Shun-Qi-Tong-Xie (SQTX) in treating digestive disorders in China
• The impact of yoga on physical and mental health in adolescents
• The benefits of hydrotherapy in treating musculoskeletal disorders such as tendinitis
• The role of Reiki in promoting healing and relaxation post birth
• The effectiveness of naturopathy in treating skin conditions such as eczema
• The use of deep tissue massage therapy in reducing chronic pain in amputees
• The impact of tai chi on the treatment of anxiety and depression
• The benefits of reflexology in treating stress, anxiety and chronic fatigue
• The role of acupuncture in the prophylactic management of headaches and migraines

Topics & Ideas: Dentistry

• The impact of sugar consumption on the oral health of infants
• The use of digital dentistry in improving patient care: A systematic review
• The efficacy of orthodontic treatments in correcting bite problems in adults
• The role of dental hygiene in preventing gum disease in patients with dental bridges
• The impact of smoking on oral health and tobacco cessation support from UK dentists
• The benefits of dental implants in restoring missing teeth in adolescents
• The use of lasers in dental procedures such as root canals
• The efficacy of root canal treatment using high-frequency electric pulses in saving infected teeth
• The role of fluoride in promoting remineralization and slowing down demineralization
• The impact of stress-induced reflux on oral health
• The benefits of dental crowns in restoring damaged teeth in elderly patients
• The use of sedation dentistry in managing dental anxiety in children
• The efficacy of teeth whitening treatments in improving dental aesthetics in patients with braces
• The role of orthodontic appliances in improving well-being
• The impact of periodontal disease on overall health and chronic illnesses

Tops & Ideas: Veterinary Medicine

• The impact of nutrition on broiler chicken production
• The role of vaccines in disease prevention in horses
• The importance of parasite control in animal health in piggeries
• The impact of animal behaviour on welfare in the dairy industry
• The effects of environmental pollution on the health of cattle
• The role of veterinary technology such as MRI in animal care
• The importance of pain management in post-surgery health outcomes
• The impact of genetics on animal health and disease in layer chickens
• The effectiveness of alternative therapies in veterinary medicine: A systematic review
• The role of veterinary medicine in public health: A case study of the COVID-19 pandemic
• The impact of climate change on animal health and infectious diseases in animals
• The importance of animal welfare in veterinary medicine and sustainable agriculture
• The effects of the human-animal bond on canine health
• The role of veterinary medicine in conservation efforts: A case study of Rhinoceros poaching in Africa
• The impact of veterinary research of new vaccines on animal health

Topics & Ideas: Physical Therapy/Rehab

• The efficacy of aquatic therapy in improving joint mobility and strength in polio patients
• The impact of telerehabilitation on patient outcomes in Germany
• The effect of kinesiotaping on reducing knee pain and improving function in individuals with chronic pain
• A comparison of manual therapy and yoga exercise therapy in the management of low back pain
• The use of wearable technology in physical rehabilitation and the impact on patient adherence to a rehabilitation plan
• The impact of mindfulness-based interventions in physical therapy in adolescents
• The effects of resistance training on individuals with Parkinson’s disease
• The role of hydrotherapy in the management of fibromyalgia
• The impact of cognitive-behavioural therapy in physical rehabilitation for individuals with chronic pain
• The use of virtual reality in physical rehabilitation of sports injuries
• The effects of electrical stimulation on muscle function and strength in athletes
• The role of physical therapy in the management of stroke recovery: A systematic review
• The impact of pilates on mental health in individuals with depression
• The use of thermal modalities in physical therapy and its effectiveness in reducing pain and inflammation
• The effect of strength training on balance and gait in elderly patients

Topics & Ideas: Optometry & Opthalmology

• The impact of screen time on the vision and ocular health of children under the age of 5
• The effects of blue light exposure from digital devices on ocular health
• The role of dietary interventions, such as the intake of whole grains, in the management of age-related macular degeneration
• The use of telemedicine in optometry and ophthalmology in the UK
• The impact of myopia control interventions on African American children’s vision
• The use of contact lenses in the management of dry eye syndrome: different treatment options
• The effects of visual rehabilitation in individuals with traumatic brain injury
• The role of low vision rehabilitation in individuals with age-related vision loss: challenges and solutions
• The impact of environmental air pollution on ocular health
• The effectiveness of orthokeratology in myopia control compared to contact lenses
• The role of dietary supplements, such as omega-3 fatty acids, in ocular health
• The effects of ultraviolet radiation exposure from tanning beds on ocular health
• The impact of computer vision syndrome on long-term visual function
• The use of novel diagnostic tools in optometry and ophthalmology in developing countries
• The effects of virtual reality on visual perception and ocular health: an examination of dry eye syndrome and neurologic symptoms

Topics & Ideas: Pharmacy & Pharmacology

• The impact of medication adherence on patient outcomes in cystic fibrosis
• The use of personalized medicine in the management of chronic diseases such as Alzheimer’s disease
• The effects of pharmacogenomics on drug response and toxicity in cancer patients
• The role of pharmacists in the management of chronic pain in primary care
• The impact of drug-drug interactions on patient mental health outcomes
• The use of telepharmacy in healthcare: Present status and future potential
• The effects of herbal and dietary supplements on drug efficacy and toxicity
• The role of pharmacists in the management of type 1 diabetes
• The impact of medication errors on patient outcomes and satisfaction
• The use of technology in medication management in the USA
• The effects of smoking on drug metabolism and pharmacokinetics: A case study of clozapine
• Leveraging the role of pharmacists in preventing and managing opioid use disorder
• The impact of the opioid epidemic on public health in a developing country
• The use of biosimilars in the management of the skin condition psoriasis
• The effects of the Affordable Care Act on medication utilization and patient outcomes in African Americans

Topics & Ideas: Public Health

• The impact of the built environment and urbanisation on physical activity and obesity
• The effects of food insecurity on health outcomes in Zimbabwe
• The role of community-based participatory research in addressing health disparities
• The impact of social determinants of health, such as racism, on population health
• The effects of heat waves on public health
• The role of telehealth in addressing healthcare access and equity in South America
• The impact of gun violence on public health in South Africa
• The effects of chlorofluorocarbons air pollution on respiratory health
• The role of public health interventions in reducing health disparities in the USA
• The impact of the United States Affordable Care Act on access to healthcare and health outcomes
• The effects of water insecurity on health outcomes in the Middle East
• The role of community health workers in addressing healthcare access and equity in low-income countries
• The impact of mass incarceration on public health and behavioural health of a community
• The effects of floods on public health and healthcare systems
• The role of social media in public health communication and behaviour change in adolescents

Examples: Healthcare Dissertation & Theses

While the ideas we’ve presented above are a decent starting point for finding a healthcare-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various healthcare-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• Improving Follow-Up Care for Homeless Populations in North County San Diego (Sanchez, 2021)
• On the Incentives of Medicare’s Hospital Reimbursement and an Examination of Exchangeability (Elzinga, 2016)
• Managing the healthcare crisis: the career narratives of nurses (Krueger, 2021)
• Methods for preventing central line-associated bloodstream infection in pediatric haematology-oncology patients: A systematic literature review (Balkan, 2020)
• Farms in Healthcare: Enhancing Knowledge, Sharing, and Collaboration (Garramone, 2019)
• When machine learning meets healthcare: towards knowledge incorporation in multimodal healthcare analytics (Yuan, 2020)
• Integrated behavioural healthcare: The future of rural mental health (Fox, 2019)
• Healthcare service use patterns among autistic adults: A systematic review with narrative synthesis (Gilmore, 2021)
• Mindfulness-Based Interventions: Combatting Burnout and Compassionate Fatigue among Mental Health Caregivers (Lundquist, 2022)
• Transgender and gender-diverse people’s perceptions of gender-inclusive healthcare access and associated hope for the future (Wille, 2021)
• Efficient Neural Network Synthesis and Its Application in Smart Healthcare (Hassantabar, 2022)
• The Experience of Female Veterans and Health-Seeking Behaviors (Switzer, 2022)
• Machine learning applications towards risk prediction and cost forecasting in healthcare (Singh, 2022)
• Does Variation in the Nursing Home Inspection Process Explain Disparity in Regulatory Outcomes? (Fox, 2020)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Need more help?

If you’re still feeling a bit unsure about how to find a research topic for your healthcare dissertation or thesis, check out Topic Kickstarter service below.

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15 Comments

I need topics that will match the Msc program am running in healthcare research please

Hello Mabel,

I can help you with a good topic, kindly provide your email let’s have a good discussion on this.

Can you provide some research topics and ideas on Immunology?

Thank you to create new knowledge on research problem verse research topic

Help on problem statement on teen pregnancy

This post might be useful: https://gradcoach.com/research-problem-statement/

can you provide me with a research topic on healthcare related topics to a qqi level 5 student

Please can someone help me with research topics in public health ?

Hello I have requirement of Health related latest research issue/topics for my social media speeches. If possible pls share health issues , diagnosis, treatment.

I would like a topic thought around first-line support for Gender-Based Violence for survivors or one related to prevention of Gender-Based Violence

Please can I be helped with a master’s research topic in either chemical pathology or hematology or immunology? thanks

Can u please provide me with a research topic on occupational health and safety at the health sector

Good day kindly help provide me with Ph.D. Public health topics on Reproductive and Maternal Health, interventional studies on Health Education

may you assist me with a good easy healthcare administration study topic

May you assist me in finding a research topic on nutrition,physical activity and obesity. On the impact on children

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Current Issues, Challenges, and Future Perspectives in Clinical Laboratory Medicine

Ferdinando mannello.

1 Department of Biomolecular Sciences-DISB, University of Urbino Carlo Bo, 61029 Urbino, Italy

Mario Plebani

2 Department of Medicine-DIMED, University of Padua, 35128 Padua, Italy

Laboratory medicine has undergone a profound evolution in organizational, methodological, and cultural terms in recent decades [ 1 ]. From the organizational point of view, we are living in the era of consolidation, i.e., the formation of networks of consolidated laboratories with marked automation and integration of the various branches of laboratory medicine [ 2 ]. From a methodological point of view, the advent of high-throughput technologies has allowed us to launch a systematic approach to studying nucleic acids, proteins, and intermediate metabolites, all aspects that have considerably reduced the barriers between various branches of biology, to convey all of the information obtained (i.e., the so-called Big Data) into a new perspective of life science related to the biology of systems [ 3 ].

In this context, the “Omics” revolution, including mainly genomics, proteomics, degradomics, and metabolomics, has developed into the current major drivers of the bench-to-bedside passage of Omics without limiting the numerous other Omics that opened new and interesting perspectives in laboratory medicine and translational medicine (such as transcrittomics, mirnomics, epigenomics, interactomics, etc.) [ 4 ].

The enormous amount of data (“Big Data”) already obtained and still obtainable with Omics analyses have highlighted the professional nature of bioinformatics, opening new perspectives in studying crucial aspects of clinical laboratory medicine: the association–causality relationship; the management of results; the harmonization of data from different technological platforms; and ethical, legal, and privacy issues. Thanks to the use of Omics, clinical laboratory medicine will play a key role in significantly and substantially implementing precision medicine, in preventive screenings, in Omics diagnostics, in personalized drug treatments, and in clinical outcome monitoring.

Through the different Omics branches of clinical laboratory medicine, it will therefore be possible to develop innovative methods in diagnostics, the identification of new diagnostic and/or prognostic biomarkers, the development of innovative target-specific therapies, the design and construction of controlled clinical trials on new drugs, the drafting of new guidelines (such as those already carried out in the field of cardiovascular, hematological, and oncological diseases), as well as both the diagnostics and therapeutic treatments of several human pathologies. All of these crucial aspects are increasingly linked to the concept of well-being, including the application of Omics in laboratory medicine studies on the effects of physical exercise.

Clinical laboratory medicine will therefore change its paradigm, moving away from simple services for clinics and physicians and becoming an even more efficient reference for the diagnosis and treatment of patients [ 5 ].

The new diagnostic and therapeutic pathways offered by clinical laboratory medicine are mainly based on the three crucial aspects of appropriateness: prescription, analytics, and diagnostics. Prescriptive appropriateness provides physicians with a constant comparison with other laboratory colleagues to build the right diagnostic protocols. These joint protocols pave the way for feedback, with the best opportunities for updated investigations that the laboratory can offer to the patient, using the best choice of tests (diagnostic settings for personalized and precision medicine) [ 4 ].

Analytical appropriateness represents a fundamental part of the status of clinical laboratory medicine specialists as the search for even better technologies and possible new diagnostic tests (based on scientific evidence and surpassing the obsolete ones); this path allows us the best use of financial resources avoiding wasted costs and technologies, focusing efforts according to efficiency, expertise, and targeted epidemiological characteristics of the patient [ 5 ].

It is in this perspective that the diagnostic appropriateness must not only have an economic value for cost limitation but also an ethical value for the best diagnostic–therapeutic path of the patient.

Finally, diagnostic appropriateness is mainly aimed at improving clinical outcomes. Only in the face of a constant comparison between the treating physicians and the specialists of laboratory medicine will it be possible to understand the mantra “do the right test to the right patient at the right time and with the right specialist”: in this way, the expected results of diagnostic–therapeutic biomarkers will be obtained with the new Omic approaches of laboratory medicine [ 6 ].

If a health system with the patient at the center is oriented towards personalized and/or precision medicine, one cannot ignore appropriateness from a holistic perspective and therefore the indispensable involvement of specialists in laboratory medicine disciplines [ 5 ].

In this context, even the pharmaceutical and diagnostic industries can offer a substantial contribution to recovering efficiency and can ensure suitable results, supporting a paradigmatic shift. In fact, diagnostic appropriateness primarily means being able to choose technological innovation (both related to and not related to automation) and laboratory tests with new generation biomarkers on evidence-based medicine.

Importantly, diagnostic appropriateness is born from the definition of guidelines that identify the appropriate tests for a therapy of that type of patient with a specific pathology (and not misunderstanding appropriateness as a mere reduction in financial costs and medical prescriptions by limiting the choices of both clinicians and laboratory specialists in managing the patient’s health) [ 6 ].

The best indicator of appropriateness is the state of health that is reachable by the patient through innovation and technologies, simply evaluated in a timely and efficient manner according to a structured path of health technology assessment.

The specific application of guidelines, primary and secondary prevention interventions, initiative medicine and early diagnosis in subjects at risk, and management of chronic (pluri-pathological) patients are just some examples of appropriateness, i.e., appropriate application of health care for both healthy subjects and patients, and a correct interpretation of the holistic concept of health.

If the future of clinical laboratory medicine is precision and personalized medicine, we cannot ignore the appropriateness of diagnostic test requests and therefore the involvement of specialists in the disciplines of laboratory medicine areas in defining optimal diagnostic–therapeutic pathways for patient’s health [ 7 ].

On these bases, with great pleasure, we invite specialists from the various branches of laboratory medicine to participate in the submission of scientific work in the fields of clinical chemistry and translational medicine and from the full spectrum of clinical biochemistry and clinical laboratory medicine, promoting excellence in laboratory sciences and closely related fields and sub-specialties. We welcome contributions that will have an impact on the understanding of health and disease and on the progress in basic and applied research in clinical laboratory medicine, taking into consideration papers about all aspects of clinical chemistry and laboratory medicine, with a focus on analytical, preclinical, and clinical investigations of laboratory tests used for diagnosis, prognosis, treatment and therapy, and monitoring of disease in humans.

Welcome to the new “Clinical Laboratory Medicine” section ( https://www.mdpi.com/journal/jcm/sectioneditors/clinical_laboratory_medicine accessed on 26 January 2022).

Author Contributions

Conceptualization, M.P. and F.M.; writing—original draft preparation, F.M.; writing—review and editing, M.P. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Clinical Laboratory Science: Literature Reviews

• Literature Reviews
• How to Search
• Searching in PubMed
• Evidence Based Practice
• Helpful Links
• Write It / Cite It
• Using ChatGPT

Step 1 - Formulate a Question

You will first want to determine a topic for your review.  If you are working on an assignment, this may be provided for you or determined by the field you are studying.  Your topic may also be inspired by a friend, family member,  patient, or client you have worked with, an area you are interested in, or an area where you have seen conflicting data, results, or recommendations.  Run a simple search to see if the topic has been thoroughly explored.

Next, identify your question.  Mind mapping or brainstorming may be helpful.  It is helpful to write the question as a question rather than a statement.  Your question should also be neutral rather than biased in one direction or another.    Finally, your question should be answerable within the timeframe you have for your project and with the resources you have available to you.

Once you begin searching, you may decide your question is too broad or too narrow.  It is okay to refine your question after you have started investigating the literature.

Step 2 - Literature Search

In this step, you will find materials relevant to the subject you are exploring.  Keep in mind, not all databases are created equally.  They may have different focuses and include different types of materials.  A librarian may be very helpful in determining which databases will be most helpful for your query and in creating an effective search for the database you are searching.  The librarian can also help you determine effective keywords for your search.

When searching, be sure to utilize synonyms and alternative terms in your search.  You may miss pertinent resources if you do not use alternative terms.  Instead of searching for "child", you could search for "child AND children AND kid AND kids AND pediatric AND pediatrics AND paediatric AND paediatrics AND adolescent AND adolescents"...  You will have far more results when you combine search terms instead of searching for a single term.

Be sure you understand how to properly combine search terms.  For more information about combining search terms and other search techniques, check out the site below:

• Search Basics for the Health Sciences: Combining Search Terms

Step 3 - Data Evaluation

A Literature Matrix may assist you in this step!

Next, you will want to evaluate the data you have found to determine which literature makes a significant contribution to your understanding of the topic you are searching.

Read through the articles you have selected to include in your literature review.  Take notes, in your own words, of the pertinent details, being sure that you know which details came from which sources.

• Choose what format you will use to take notes
• Define key terms in the literature
• Note key statistics
• Don't use too many
• Do not copy direct quotes without attributing them to the original author
• Note the source including page number for easy citation later
• Note the different emphasis, strengths, and weaknesses of each study
• Identify major trends and patterns in the literature
• Identify gaps in the literature
• Note if one study is based on or follows another

From:  Mongan-Rallis H. Guidelines for writing a literature review. URL https://www.duluth.umn.edu/~hrallis/guides/researching/litreview.html. Updated April 19, 2018. Accessed January 11, 2019.

When reading through, be sure to think about the following:

• Are the author's credentials well-respected?
• Could the author's affiliations introduce bias?
• Are the author's theories supported by sound evidence/research?
• Does the tone of the study seem biased?
• Which theories are most/least convincing?
• Does the work contribute to your understanding of the topic?
• Guidelines for writing a literature review

Step 4 - Synthesize

This is the step where you put it all together.  You will discuss the findings and conclusions of the pertinent literature.

Even if your literature review is not a stand-alone paper, it should include the following structure, to establish a logical flow for your reader:

Introduction

• Avoid blanket or global statements
• Point out trends in what has been published about the topic, conflicts in the literature, gaps in the research, or an area of interest
• Explain your reasoning (point of view) for the review, explain the criteria or sequence for your literature comparisons, and explain why you left out certain key pieces of literature within the topic area
• Note specifically what you will cover in this review and what you will not cover
• "Case studies in this field have shown..."
• "Randomized controlled trials by... have shown that..."
• "Cohort studies from China show that...however, cohort studies from the United States indicate..." 
• "Studies conducted by...found that..."
• "In contrast, studies conducted by...found..."
• "One reason these studies contradict each other could be..."
• "The authors of three randomized controlled trials and two cohort studies found..."
• "Several scholars supported the idea that..."
• "Early studies in the field found that..."
• "However, studies conducted in the last five years found..."
• "In his landmark study from 1975, Smith discovered...Jones replicated Smith's study in 2018 and found..."
• etc.  
• Summarize the main points from the group of articles
• Summarize studies based on their importance within the review - space denotes significance
• Use appropriate transitions and brief "so what" summaries at the ends of groupings to aid in understanding and flow
• Summarize major contributions
• Continue the focus you had in the introduction
• Evaluate the most recent developments in the field
• Point out gaps in the literature, inconsistencies, and areas for future study
• Provide insight into the importance of the topic within the broader field of study or the profession
• If the lit review is a stand-alone paper, re-state your thesis and note how you have supported that statement with the chosen literature
• If the lit review is part of a larger research paper, lead the reader into the questions that will be addressed by your research

More Resources

For Help With Searching

• Talk to your liaison librarian
• See the guide on searching below:
• Search Basics for the Health Sciences Guide

About Literature Reviews

• From the University of Toronto: The Literature Review: A Few Tips On Conducting It
• Health Sciences Literature Review Made Easy: The Matrix Method
• Doing A Literature Review In Health And Social Care : A Practical Guide

About Writing

• ECU Writing Center
• The Elements of Style
• Scientific writing : a reader and writer's guide / by Jean-Luc Lebrun
• The Scientist's Guide to Writing : How to Write More Easily and Effectively Throughout Your Scientific Career
• Scientific writing : thinking in words / David Lindsay

A Few Literature Review Examples

• Consider sensory processing disorders in the explosive child: case report and review
• The Multidisciplinary Approach to Alzheimer's Disease and Dementia. A Narrative Review of Non-Pharmacological Treatment
• Physical activity for children with chronic disease; a narrative review and practical applications
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• Last Updated: Jan 12, 2024 1:20 PM
• URL: https://libguides.ecu.edu/ClinicalLaboratoryScience

Explore the Best Medical and Health Research Topics Ideas

Table of contents

• 1 How to Choose Medical Research Paper Topics
• 2 New Medical Research Paper Topics
• 3 Medical Research Topics for College Students
• 4 Controversial Medical Topics for Research Paper
• 5 Health Research Topics
• 6 Medicine Research Topics
• 7 Healthcare Research Topics
• 8 Public Health Research Topics
• 9 Mental Health Research Paper Topics
• 10 Anatomy Research Topics
• 11 Biomedical Research Topics
• 12 Bioethics Research Topics
• 13 Cancer Research Topics
• 14 Clinical Research Topics
• 15 Critical Care Research Topics
• 16 Pediatric Research Topics
• 17 Dental Research Topics Ideas
• 18 Dermatology Research Topics
• 19 Primary Care Research Topics
• 20 Pharmaceutical Research Topics
• 21 Medical Anthropology Research Topics
• 22 Paramedic Research Paper Topics
• 23 Surgery Research Topics
• 24 Radiology Research Paper Topics
• 25 Anatomy and Physiology Research Paper Topics
• 26 Healthcare Management Research Paper Topics
• 27 Medical Ethics Research Paper Topics
• 28 Environmental Health and Pollution Research Paper Topics
• 29 Conclusion

In such a complex and broad field as medicine, writing an original and compelling research paper is a daunting task. From investigating public care concerns to cancer treatment studies, each student decides where his interests lie. Our goal is to help students find new angles to study and focus on relevant topics. With our resources, you can write an engaging and rigorous paper.

How to Choose Medical Research Paper Topics

Choosing good research paper topics is often more challenging than the writing process itself. You need to select a captivating subject matter that will grab the reader’s attention, showcase your knowledge of a specific field, help you progress in your studies, and perhaps even inspire future research.

To accomplish that, you need to start with brainstorming, followed by thorough research. Here are some great tips to follow:

• Pick an interesting topic – The key is to pick something that you find interesting, and yet make sure it’s not too general or too narrow. It should allow you to delve deep into the subject matter and show that you’re a professional who is ready to take on a challenge when it comes to your chosen field of medicine.
• Narrow down your focus – Once you have a list of potential topics, sift through recent medical research papers to get up-to-date with the latest trends, developments, and issues in medicine and healthcare. Check out textbooks, news articles, and other relevant sources for more information related to your potential topics. If a particular condition or disease interests you (perhaps something that drew you to a career in medicine), there’s your cue for narrowing down your topic.
• Pinpoint the “why,” “how,” and “what” – Whether you are looking into nutrition research paper topics , controversial medical topics, nursing research topics, or anything in-between, ask yourself why each of them is important. How could they contribute to the available medical studies, if any? What new information could they bring to improve the future of medicine? Asking these questions will help you pick the right medical research paper topic that suits you and helps you move forward and reach your aspirations.

To help you on that quest, we’ve compiled a list of topics that you could use or that might inspire you to come up with something unique. Let’s dive in.

New Medical Research Paper Topics

Are you interested in the newest and most interesting developments in medicine? We put hours of effort into identifying the current trends in health research so we could provide you with these examples of topics. Whether you hire a research paper writing service for students or write a paper by yourself, you need an appealing topic to focus on.

• Epidemics versus pandemics
• Child health care
• Medical humanitarian missions in the developing world
• Effectiveness of mobile health clinics in rural Africa
• Homeopathic medicines – the placebo effect
• Comparative study of the efficacy of homeopathic treatments and conventional medicine in managing chronic pain
• Virus infections – causes and treatment
• Trends in COVID-19 vaccine uptake
• Advancements in the treatment of influenza
• Is medical research on animals ethical
• Vaccination – dangers versus benefits
• Artificial tissues and organs
• Rare genetic diseases
• Brain injuries
• Long-Term Effects of COVID-19
• Social behavior shifts due to COVID-19

Medical Research Topics for College Students

You don’t know where to start with your medical research paper? There are so many things you could write about that the greatest challenge is to narrow them down. This is why we decided to help.

• Antibiotics treatments
• Efficacy of mRNA vaccines against viral diseases
• Viability and function of 3D printed tissues
• Chronic diseases
• Palliative treatment
• Battling Alzheimer’s disease
• How modern lifestyle affects public health
• Professional diseases
• Sleep disorders
• Changes in physical and mental health due to aging
• Eating disorders
• Terminal diseases

Controversial Medical Topics for Research Paper

In healthcare, new discoveries can change people’s lives in the blink of an eye. This is also the reason why there are so many controversial topics in medicine, which involve anything from religion to ethics or social responsibility. Read on to discover our top controversial research topics.

• Ethical debates on artificial tissue engineering
• Public opinions on vaccination safety
• Implementing food standards
• Telehealth’s Role in Chronic Illness Management
• Gluten allergy
• Assisted suicide for terminal patients
• Testing vaccines on animals – ethical concerns
• Moral responsibilities regarding cloning
• Marijuana legalization for medical purposes
• Abortion – medical approaches
• Vegan diets – benefits and dangers
• Increased life expectancy: a burden on the healthcare system?
• Circumcision effects

Health Research Topics

Students conducting health research struggle with finding good ideas related to their medical interests. If you want to write interesting college papers, you can select a good topic for our list.

• Impact of location, ethnicity, or age on vaccination rates
• Uses of biomaterials in vaccination technology
• Deafness: communication disorders
• Household air pollution
• Diabetes – a public danger
• Coronaviruses
• Oral health assessment
• Tobacco and alcohol control
• Diseases caused by lack of physical exercise
• How urban pollution affects respiratory diseases
• Healthy diets

Medicine Research Topics

Regardless of the requirements in your research assignment, you can write about something that is both engaging and useful in your future career. Choose a topic from below.

• Causes for the increasing cancer cases
• Insulin resistance
• How terrorism affects mental health
• AIDS/HIV – latest developments
• Treating pregnant women versus non-pregnant women
• Latest innovations in medical instruments
• Genetic engineering
• Successful treatment of mental diseases
• Is autism a disease
• Natural coma versus artificial coma
• Treatments for sleep disorders and their effectiveness
• Role of melatonin supplements in sleep quality

Healthcare Research Topics

Healthcare research includes political and social aspects, besides medical. For college students who want to explore how medicine is affected by society’s values or principles, we provide examples of topics for papers. Select yours from the list below.

• Government investment in healthcare services in the EU versus the USA
• Inequalities in healthcare assistance and services
• Electronic health records systems – pros and cons
• Can asylums treat mental issues
• Health care for prison inmates
• Equipment for improving the treatment of AIDS
• Correlation between economic development and health care services across countries
• Impact of smoking on organs
• Heart attacks – causes and effects
• Breast cancer – recent developments
• Materials used in artificial tissue and their impacts

Public Health Research Topics

For current examples of public health topics, browse our list. We provide only original, researchable examples for which you can easily find supporting data and evidence.

• Public versus private hospitals
• Health Disparities in Diabetes Management Across Different Socioeconomic Groups
• Health care professionals – management principles
• Surgery failures – who is responsible
• What legal responsibilities has the hospital administration
• Patient service quality in public versus private hospitals
• What benefits do national health care systems have
• Estimated costs of cancer treatments
• Public health in developing countries
• Banning tobacco ads – importance for public health
• Government solutions to the anti-vaccine’s movement
• How the COVID-19 pandemic has changed public health regulations

Mental Health Research Paper Topics

Mental health is one of the most complex areas of medicine, where things are never as clear as with other medical issues. This increases the research potential of the field with plenty of topics left for debate.

• Mental Health Impact of Social Media on American Teenagers
• Causes of anxiety disorders
• Bulimia versus anorexia
• Childhood trauma
• Mental health public policies
• Impact of Lifestyle Factors on the Progression of Dementia in the Elderly Population
• Postpartum Depression
• Posttraumatic Stress Disorder
• Seasonal Affective Disorder
• Schizophrenia
• Stress and its effects on sleep quality
• Insomnia and its relation to mental health disorders

Anatomy Research Topics

Anatomy covers everything about the human body and how it works. If you find that intriguing and want to pay for medical research paper, start by selecting a topic.

• Causes and treatments of virus infections
• Chemotherapy: how it affects the body
• Thyroid glands – functions in the body
• Human endocrine system
• Preventative Measures and Treatments for Common Liver Diseases
• Heart diseases
• How does the human muscular system develop
• Lymphatic system – importance
• Investigating genetic diseases
• Digestive system
• Role of the Spleen in the Human Immune System and Related Disorders

Biomedical Research Topics

Biology and medicine often work together. For the newest changes in the biomedical field, check our topics.

• Comparative Efficacy of Alternative Medicine Practices in Chronic Pain Management
• Alzheimer’s disease – paths for treatment
• Vaccines and drug development in the treatment of Ebola
• Antibiotic resistance
• Biological effects caused by aging
• Air pollution effects on health
• Infectious disease past versus present
• Regenerative medicine
• Biomedical diagnostics
• Biomedical technology
• Advanced biomaterials for vaccine delivery

Bioethics Research Topics

A controversial area of medicine, bioethics is where you get the chance to add personal input to a research topic and come up with new insights. You could consider these subjects.

• Organ donation
• Alternative or complementary medicine
• Assisted suicide or the right to die
• Artificial insemination or surrogacy
• Chemical and biological warfare
• Contraception
• Environmental bioethics
• In Vitro Fertilization
• Ethical considerations in medical research on animals

Cancer Research Topics

Are you writing a paper related to cancer causes, diagnosis, treatment or effects? Look below for a hot topic that it’s easy to research and important for medical advance.

• The ability of immune system cells to fight cancer
• Computational oncology
• Metastasis affected by drug resistance
• Stem cells – applications for cancer treatment
• Tumor microenvironment
• Obesity and age in cancer occurrence
• Early cancer detection – benefits
• Artificial intelligence predicting cancer
• Hematologic malignancies
• Pathogen-related cancers
• Impact of COVID-19 on cancer treatment studies

Clinical Research Topics

Learn more about clinical medicine by conducting more in-depth research. We prepared for you a list of relevant issues to touch upon.

• Ethical concerns regarding research on human subjects
• Subject recruitment
• Budget preparation
• Human subject protection
• Clinical trials – financial support
• Clinical practices for health professionals
• Using vulnerable populations in clinical research
• Quality assurance in clinical research
• Academic clinical trials versus clinical trials units
• Data collection and management
• Evolution of clinical symptoms in COVID-19 patients

Critical Care Research Topics

Critical care is a key area in medical studies. Explore these topics in your research paper to gain more valuable knowledge in this field. You can also get in contact with nursing research paper writers .

• Obesity and asthma – clinical manifestations
• Chronic obstructive pulmonary disease
• Rhythm analysis for cardiac arrest
• Traumatic brain injury – fluid resuscitation
• Hydrocortisone for multiple trauma patients
• Care and nutrition for critically ill adults
• Diagnosis of hypersensitivity pneumonitis
• Coma and sedation scales
• Artificial airways suctioning
• Arterial puncture and arterial line
• Long-term cardiac and respiratory effects of COVID-19

Pediatric Research Topics

Any topic that refers to health care for children, pregnant women, mothers, and adolescents goes under pediatric care.

• Early Intervention Methods for Children Diagnosed with Autism Spectrum Disorder
• Preventive healthcare strategies for children
• Impact of early childhood nutrition on long-term health
• Attention deficit hyperactivity disorder (ADHD)
• Congenital heart disease in newborns
• Adolescent medicine
• Neonatal medicine
• Rare diseases in children and teenagers
• Obesity and weight fluctuations
• Behavioral sleep problems in children
• Children with anemia
• Child healthcare enhancements and innovations

Dental Research Topics Ideas

Choose a topic on oral health or dental care from this list of the most interesting topics in the field.

• How smoking affects oral health
• Children’s risk for dental caries
• Causes of Dental Anxiety and Effective Interventions for Reducing Fear in Patients
• Types of dental materials – new advances
• Bad breath bacteria
• How diabetes affects oral health
• Oral cancer
• Dental pain – types, causes
• Dental implants
• Oral health-related quality of life
• Advancements in treatments for virus infections

Dermatology Research Topics

Find the best research topic for your dermatology paper among our examples.

• Atopic dermatitis
• Contact dermatitis
• Epidemiology behind uncommon skin disorders
• Cutaneous aging
• Risk factors of melanoma skin cancer
• Acne versus rosacea
• Genetic testing for skin conditions
• Effects of cosmetic agents on skin health
• Improving skin barrier with pharmaceutical agents
• Skin manifestations of autoimmune disorders
• Study of virus effects on skin health

Primary Care Research Topics

Write a primary care paper that can demonstrate your research skills and interest in powerful scientific findings.

• Primary care for vulnerable/uninsured populations
• Interpersonal continuity in care treatment
• How primary care contributes to health systems
• Primary care delivery models
• Developments in family medicine
• Occupational/environmental health
• Pharmacotherapy approaches
• Formal allergy testing
• Oral contraception side effects
• Dietary or behavioral interventions for obesity management

Pharmaceutical Research Topics

Pharma students who need paper topics can use one from our list. We include all things related to pharmacy life.

• Drugs that can treat cancer
• Drug excretion
• Elimination rate constant
• Inflammatory stress drug treatment
• Aspirin poising
• Ibuprofen – dangers versus benefits
• Toxicodynamics
• Opioid use disorder
• Pharmacotherapy for schizophrenia
• Ketamine in depression treatment

Medical Anthropology Research Topics

Medical anthropology unites different areas of human knowledge. Find powerful ideas for a paper below.

• Cultural contexts regarding reproductive health
• Women sexuality
• Anthropological aspects of health care
• Contributions of social sciences to public health
• Euthanasia and medical ethics across cultures
• Health-related behavior in adults across cultures
• Transcultural nursing
• Forensic psychiatry
• Symptoms of Celiac Disease – a disease with no symptoms
• Nursing ethics

Paramedic Research Paper Topics

Topics for paramedic research must be based on evidence, data, statistics, or practical experience. Just like ours.

• Trends and statistics in EMS
• Disaster medicine
• Mass casualties
• Pandemics and epidemics
• Infection control
• Basic versus advanced life support
• Scene safety in EMS
• Shock management
• Motor vehicle accidents
• Challenges in medical humanitarian missions during pandemics

Surgery Research Topics

Discover all the intricacies of surgeries that save lives by writing about our topics.

• Medical malpractice and legal issues
• Methicillin-resistant Staphylococcus aureus
• Early Detection and Management Strategies for Sepsis in Hospital Settings
• Pain management
• Perioperative nursing
• Wound management
• Colorectal cancer surgery
• Breast cancer surgery
• Minimally invasive surgeries
• Vascular disease
• Changes in surgical practices during pandemics

Radiology Research Paper Topics

Find a radiology topic related to your academic interests to write a successful paper.

• Using MRI to diagnose hepatic focal lesions
• Multidetector computer tomography
• Ultrasound elastography in breast cancer
• Assessing traumatic spinal cord injuries with MRI diffusion tensor imaging
• Sonographic imaging to detect male infertility
• Role of tomography in diagnosing cancer
• Brain tumor surgery with magnetic resonance imaging
• Bacterial meningitis imaging
• Advanced imaging techniques for virus infection detection

Anatomy and Physiology Research Paper Topics

Any ideas for a medical research paper? We have included the most important topics for an anatomy and physiology paper.

• What role has the endocrine system
• Staphylococcus aureus
• Environmental factors that affect development of human muscular system
• What role has the lymphatic system
• An investigation of genetic diseases
• Explaining the aging process
• The digestive tract
• Effects of stress on cells and muscles
• Evolution of the human nervous system
• What role has the cardiovascular system
• Impact of viruses on respiratory health in urban settings

Healthcare Management Research Paper Topics

There are numerous topics you could write about when it comes to healthcare management. There’s a wide range of options to pick, from infrastructure, staff, and financial management to HR and patient management. Here are some of the top healthcare management research paper options.

Medical Ethics Research Paper Topics

Medical ethics is a field that opens the door to numerous compelling topics for research papers. Here are some of the most appealing ones you could tackle.

• Clinical research on humans
• Vaccines and immunization
• Religious beliefs in healthcare
• Euthanasia and physician-assisted suicide
• Ethical issues across cultures
• Amniocentesis or prenatal birth defect testing
• Medical malpractice and going back to work
• Racial and ethnic preferences and perceptions in organ donations
• Racial and ethnic disparities in healthcare
• Ethical concerns of AI in healthcare
• Debates on animal ethics in medical research
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Environmental Health and Pollution Research Paper Topics

• Environmental Pollutants and Respiratory Health in Urban Areas of the USA
• How environmental changes affect human health
• Long-Term Impact of PM2.5 Exposure on Lung, Heart, and Brain Function
• Health Risks of Air Pollution Across Different Life Stages
• Hospital Admissions and Air Quality in the USA
• Risk Reduction Strategies for Indoor Air Pollution from Gas Stoves
• Impact of Air Pollution on Cognitive Development and Socioeconomic Achievements
• Long-Term Health Effects of Early Childhood Exposure to Air Pollution
• Impact of Traffic Noise on Cardiovascular Health

If you need further assistance with your medical research paper, PapersOwl is here for you. Our expert writers can provide you with top-notch research and help you write an impressive paper. Contact us anytime, pick your writer, tell them more about your topic, and get a unique, plagiarism-free research paper with impeccable grammar and formatting.

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Resources for: Medical Laboratory Science

• MEDLINE (Ovid) Searches MEDLINE, which is the primary source of journal articles for the health sciences (fields of medicine, nursing, dentistry, veterinary medicine, public health, health care systems, and basic sciences). Ovid MEDLINE is optimized for advanced literature searches. Coverage is from the 1940s to the present.
• PubMed Searches MEDLINE, which is the primary source of journal articles for the health sciences (fields of medicine, nursing, dentistry, veterinary medicine, public health, health care systems, and basic sciences). Coverage is from the 1940s to the present. View this tutorial to learn how to go from a general idea to a very precise set of results of journal articles and scholarly materials.
• CINAHL Ultimate (Nursing & Allied Health) Covers nursing and allied health journal articles, book chapters, and dissertations, as well as providing summarized evidence-based resources such as care sheets and quick lessons.
• Current Protocols Current Protocols is a collection of updatable, step-by-step, reproducible laboratory methods.
• Google Scholar (Setup connection to get to PDFs) Use Google Scholar to find articles from academic publishers, professional societies, research institutes, and scholarly repositories from colleges and universities. If you are using from off-campus access, change the "Library Settings" to University of Minnesota Twin Cities. Look for the "FindIt@U of M Twin Cities" links in your Google Scholar search results to access full text and PDFs. View this tutorial to learn how to go from a general idea to a very precise set of results of journal articles and scholarly materials.
• Merck Index: An Encyclopedia of Chemicals, Drugs, and Biologicals Classic reference source for chemists with over 10,000 entries on drugs and pharmaceuticals, common organic chemicals and laboratory reagents. The entries include data such as chemical names, molecular formula, chemical structure, physical data, and literature references.
• SciFinder-n SciFinder-n is the updated version of SciFinder and is the best database for topics related to chemistry and adjacent fields. It includes journal articles, book chapters, dissertations, and patents. Find substance and reaction information as well as suppliers and chemical regulatory data. more... less... Registration is required, and you must download the Virtual Private Network (VPN) from UMN Office of Information Technology, set up new account if you don't have one and then log in to it for off-campus access.

E-Books in Medical Laboratory Science

E-books available at the University of Minnesota Libraries.  Requires authentication/log-in with current University internet ID and password.

Journals in Medical Laboratory Science

Selected list of Journals in Medical Laboratory Science and related disciplines.  Requires a current University of Minnesota internet ID and password to access.  See library catalog for more titles.

  Clinical laboratory science (Online). (1988). The Society.

Annals of clinical and laboratory science (Online). (1971). Association of Clinical Scientists.

 Laboratory medicine (Online). (1970). [American Society of Clinical Pathologists, etc.].

Archives of pathology & laboratory medicine (Online). (1976). College of American Pathologists.

Clinical chemistry. (1955). P.B. Hoeber.

  Alternatives to laboratory animals : ATLA . (1981). FRAME.

Microscopy research and technique (Online). (1992). Wiley-Liss.

Journal of clinical laboratory analysis (Online). (1987). [Alan R. Liss].

Journal of clinical microbiology . (1975). American Society for Microbiology.

Journal of clinical pathology (Online). (1947). British Medical Association.

Selected Internet Resources

Freely available selected internet resources on laboratory tests and clinical laboratory medicine

• PubChem :  PubChem  is an open chemistry database at the  National Institutes of Health (NIH) . PubChem is the world's largest collection of freely accessible chemical information. Search chemicals by name, molecular formula, structure, and other identifiers. Find chemical and physical properties, biological activities, safety and toxicity information, patents, literature citations and more.
• Medical Tests: List of common medical tests, including iwhat the tests are used for, why a doctor may order a test, how a test will feel, and what the results may mean.   
• Laboratory Tests :  Set of resources aimed at the consumer
• Testing.com (formerly LabTestsOnline.org): Testing.com is a health information digital resource designed to help people understand the many lab tests that are a vital part of health care. The site contains a library of over 400 expert-reviewed guides on specific lab tests and laboratory topics.
• Clinical Laboratory Improvement Amendments (CLIA) :  The Clinical Laboratory Improvement Amendments of 1988 (CLIA) regulations include federal standards applicable to all U.S. facilities or sites that test human specimens for health assessment or to diagnose, prevent, or treat disease.

Normal Laboratory Values (Merck Manual, Professional)

Normal Laboratory Values: Blood, Plasma, and Serum

Normal Laboratory Values: Urine

Normal Laboratory Values: CSF

Normal Laboratory Values: Stool

Normal Laboratory Values: Other

Commonly Used Panels

Bayot ML, Lopes JE, Naidoo P. Clinical Laboratory. [Updated 2022 Dec 19]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-.

Minnesota State Resources

• Environmental Laboratory

Clinical Guide to Services - List of Tests

• Newborn Screening Laboratory
• Laboratory Emergency Preparedness

• Browse Works
• Medical & Health Sciences

Medical Lab Science and Tech

Medical lab science and tech research papers/topics, covid 19 – history and scientific progress in treatment.

The coronavirus disease 19 (COVID-19) is a highly transmittable and pathogenic viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in Wuhan, China and spread around the world. Genomic analysis revealed that SARS-CoV-2 is phylogenetically related to severe acute respiratory syndrome-like (SARS-like) bat viruses, therefore bats could be the possible primary reservoir. The intermediate source of origin and transfer to humans is not known, howeve...

PRODUCTION OF LIQUID SOAP USING LEMONGRASS AS FRAGRANCE

ABSTRACT Lemon grass (Cymbogon citratus) is an abundant, locally available plant. In this work, essential oil was extracted from it using solvent extraction method.  In this work two methods of extraction was used, solvent extraction and enfleurage methods were used to extract essential oil from lemongrass. Solvent extraction method yielded 2.08% and effleurage method yielded 1.96% essential oil respectively. From the analysis solvent extraction gave the highest yield because of the less e...

A Feasibility Study on the Production of Yogurt

ABSTRACT This project work titled “A feasibility study on the production of yogurt” a case study of Fan Milk Nigeria Plc, Oregun – Ikeja has been written to p-rovide an intensive knowledge of the practical aspect of producing yogurts in addition to the theories which involves the economical and nutritional benefits of yogurts already learnt by the researchers. This study will look into the production processes with Fan Milk Nigeria Plc, Oregun – Ikeja. The coverage of the subject ma...

Prevalence and Risk Factors Associated with Trichuris trichiura among Patients Attending Health Care at AL-Shifa Health Centre in Yaqshid, District

ABSTRACT Trichuris trichiura, also known as the human whipworm, is a roundworm that causes trichuriasis in humans. It is referred to as the whipworm because it looks like a whip with wide handles at the posterior end. The main objective of this study was to determine prevalence and risk factors associated with Trichuris trichiura among patients attending health care at AL-Shifa Health Center in Yaqshid, District. During this study, 100 stool samples were examined, which were collected from pa...

Unlocking the Spectrum: A Comprehensive Overview of Spectrometry

ABSTRACT Spectrophotometry is a powerful analytical technique that plays a pivotal role in various scientific disciplines, including medicine, chemistry, biology, physics, and environmental science. This seminar presentation offers a comprehensive overview of spectrophotometry, exploring its fundamental principles, applications, and technological advancements in various sectors. The presentation begins by delving into the basic principles of spectrophotometry, elucidating the interaction of...

Effects of Botanicals and Biocontrol Agents on Growth and Aflatoxin Production by Aspergillus Flavus Infecting Maize in Some Parts of Nigeria

ABSTRACT In a comprehensive study to assess the effects of botanicals and biocontrol agents on growth and aflatoxin (AF) production by Aspergillus flavus (A. flavus) infecting maize, a total of 1143 maize samples, collected in eighteen batches of five maize varieties (yellow, white, pop, variegated and mixed) from northern and southern parts of Nigeria were investigated between June 2011 to December 2013. Samples collected from field, 414 (36.2%) and stored batches, 729 (63.8%) were cultured...

The Effect of Ocimum Tenuiflorum (Nchuanwu) Leaf Extract on Hematolgical Parameters of Immumnosuppressed Albino Rats.

ABSTRACT The effect of Ocimum tenuiflorum on hematological parameters in immunosuppressed albino rats was investigated in this study. The aqueous (AE) and methanol extract (ME) of the leaf were obtained. Acute toxicity study was done to determine the LD50 of the leaf extract. Rats of mixed sexes, aged 2- 3months, weighing 150 to 240 grams were used. The rats were divided into 8 groups A-H of four rats each. Group A served as normal control. Immunesuppression was induced using 3mg/kg bodyweig...

Relationship between Microalbuminuria and Ischemic Heart Diseases

Serum levels of proinflammatory cytokines, haptoglobin in children of various abo blood group and heamoglobin-genotype with p. falciparaum malaria in nnewi, nigeria.

ABSTRACT Malaria is characterized by marked changes in cytokine production from immune responses to infection (Jurgen, 2007). Genetics influences these variations in cytokine expression and ABO blood group and haemoglobin phenotype are genetic expressions (Deepa et al, 2011). Acute phase proteins may also be involved in cytokine induced replication of inflammatory processes (Warren (2010). This case controlled study involving children with plasmodium falciparum malaria (PFM) in Nnewi, Nigeri...

Assessment of Sensitivity and Specifity Immunochromatograophy Test And ELISA for detecting Human Immunodeficiency Virus Antibodies among Screening Patients in Khartoum State

Abstract  Human Immunodeficiency Virus ( HIV) is global and serious problem , with increase in mortality and morbidity worldwide. This was prospective , descriptive and cross sectional study aimed to assess the level of HIV Ab using the ICT and ELISA for detecting Ab and Ag (p24) .It was conducted among Screening patient in Khartoum state (National Public Health Laboratory) from 1 April to 30 June (2015) on a total of eighty nine (n=89)to compare the sensitivity and specificity of immunochro...

Measurement of Complete Blood Count (CBC) in alcohol consumer – Khartoum 2014

Abstract This is a prospective case control study to investigate the effect of alcohol consumption on complete blood count (CBC) of alcohol consumers in Khartoum State from April to July 2014.The participants were 80 apparently healthy adult males; 50 of them are alcohol consumers and 30 are non-alcoholic (control).Their age was (41 ±7.3years).A questionnaire was constructed to obtain information about the participants after an informed verbal consent from all the participants. Ethical appr...

Molecular Basis of Immunological Dysfunction in People Living with HIV and AIDS in Enugu, Nigeria

ABSTRACT Molecular basis of immunological dysfunction in people living with HIV and AIDS was studied among HIV-positive people attending clinics at the University of Nigeria Teaching Hospital Ituku-Ozalla, Annunciation Specialist Hospital Emene, Mother of Christ Specialist Hospital and Enugu State University Teaching Hospital, all in Enugu metropolis. A total of 90 subjects recruited for the study were divided into three groups: 30 diagnostically positive HIV subjects (A), 30 HIV-positive sub...

Analysis of Heavy Metals in Pleurotus Tuberregium Sclerotia,Toxicity in Blood, Bone Marrow and Some Selected Organs of Albino Rats

ABSTRACT Pleurotus tuberregium is a common mushroom which is used as food or medicine, more commonly as a vegetable soup thickener. This study investigated the presence of heavy metals in wild samples of pleurotus tuberregium sclerotia consumed within our localities, compared the degree of heavy metal contamination of the samples, investigated the presence of heavy metals in the serum of albino rats due to its consumption and the effects of its consumption on blood, bone marrow, liver and kid...

Potential for the Prediction of Prostate Cancer Risk Using Haplotypes in Exon 5 of Klk2 Gene

ABSTRACT BACKGROUND: Prostate cancer is the most common neoplasia of middle aged men .Early detection is problematic due to the lack of marker that has high sensitivity and specificity. Our aim was to genetically predict the possibility of developing prostate cancer using haplotypes in exon 5 of kLk2 gene. We evaluated polymorphisms in human glandular kallikreins 2 (KLK2) genes because a protein product of this gene is known to be increased in prostate cancer. SUBJECTS AND METHODS: Blood samp...

ABH Prevalence, Cd4 and Cd8 Levels in Secretors and Non-Secretors of ABH Antigens Among HIV Positive Individuals in Abakaliki Area of South-East Nigeria.

ABSTRACT Sixty(60)patients (31 males and 29 females) with Human immunodeficiency Virus (HIV) disease attending Ebonyi State University Teaching Hospital (EBSUTH) Abakaliki were used for the study. Sixty (60) age and sex matched apparently healthy individuals(34 males and 26 females) who were screened three months prior to study and then rescreened immediately the study started, served as the control group. Secretor status of the groups (test and control) were determined, using neutralization ...

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Popular Papers/Topics

Distribution of abo, rh (rhesus) blood grouping and hepatitis b among blood donors with national blood transfusion service kaduna, sickle cell disease, the role of nitric oxide in the immune system, design and construction of alcohol detector, causes, management symptoms, treatment, prevention of prostate cancer, cultivation of plasmodium falciparum and antiplasmodia screening of methanol extract of ocimum basilicum (scent leaf), cardiovascular disease, siwes- a technical report on student industrial work experience held at nigeria police hospital area 1 section 1 garki, abuja, dangers associated with abuse of contraceptive pills., prevalence of plasmodium and salmonella infections among pregnant women in aba, abia state, histopathological features of joint destruction in rheumatoid arthritis (ra), project proposal occult hepatitis b virus infection among hiv positive patients in ibadan, oyo state, management of jaundice in neonates, gastric juice as a diagnostic sample, breeding method for vegetatively propagated crops somatic mutations. examples; sugar cane and potato.

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Medical Laboratory Science

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Before you begin

Plan the design, build the foundation, tables and charts, finishing and printing, at the meeting, powerpoint tips & tricks, additional resources.

• Citing References
• Statistical Consulting

Congratulations! Your proposal has been accepted, and you will be presenting a poster at the upcoming meeting! Now the work begins....

What is a scientific poster, and what is it used for?

A scientific poster provides a concise description of a study or project, usually for the purpose of presentation before a live or virtual audience at a professional conference. Since you may be competing with many other presenters for the audience's attention, your goal will be to create a poster that will quickly and effectively convey the primary message in such a way that the message sticks.

Before you begin designing the poster, you'll want to identify your primary message. It may be helpful to try to summarize your central message in a single, complete sentence. For example, "We want to show that our new therapeutic modality leads to more rapid recovery from ACL injuries than the standard treatment". This sentence may not show up anywhere on your poster, but it will help focus your efforts, keep you from adding extraneous details, and serve as a guide for selecting images and graphics.

Most posters for academic/scientific purposes use a vertical layout of 2-4 columns, reading left to right, and top to bottom. Conferences usually indicate the dimensions of the space available for your poster, listed in feet.

If you are using a poster template, adding content may be as easy as writing over the dummy text with your own content, and changing the colors and box arrangement to suit your needs.

If you will be creating your poster from scratch, Microsoft PowerPoint is a popular choice for software, as it is easy to use and readily available. Open a new presentation, choosing a Blank slide layout. Your PowerPoint poster will consist of a single slide. Go to Page Setup > Slides Sized For > Custom. Set the width and height of your poster based on the space or meeting requirements.

The maximum size PowerPoint can accommodate is 56". If you need a larger poster, you can create it at half size, and then print it out at 200% scale. If you are designing it at half size, and you want the print to be, say, 100 point, design it in 54 point.

Now you're ready to add content. Start by inserting the headings for essential elements such as the title, introduction/background, aim, methods, results, discussion, and conclusion. You can always move objects around, but having them in the field early on often helps the poster to, in a sense, create itself.

A common fault of scientific posters is including too much text. Include only what is necessary for clarity, and save the detail for the accompanying handout. Though including too little text is seen less commonly, it is just as glaring an error, as it suggests a lack of content.

The text should be large enough to be legible from a distance of ~6-10 feet. When using Arial typeface, the following guidelines will get you started:

• 72 point is about 1" tall
• Headings ~ 48 pt
• Main text ~ 32 point
• Fine print, e.g., references, acknowledgements, ~24 point.

Some sources say that a sans serif typeface, such as Arial, Helvetica, or Verdana, is easier to read than a serif typeface, such as Times New Roman. Avoid unique, distinctive typefaces unless the poster itself is intended to be a creative work.

In PowerPoint, to select a background color for the poster, right-click on the slide, then click on Background > More Colors. Note the numbers in the "RGB" (red, green, blue) boxes. These can be helpful for when you want to duplicate colors throughout the poster.

Experiment with using white boxes on a colored background, or colored boxes on a white background. Both can look very nice when well done. A white or creme-colored background with black text is easy to read, though perhaps a bit boring. Another potential combination is a dark background, say dark navy or green, with white text. Avoid using red and green together, as some of your viewers may be color blind.

Images add visual interest to your poster, and help the reader navigate between sections. Images can easily be inserted in PowerPoint through Insert > Picture > From file.

You can also turn a PowerPoint slide into a jpeg, and insert it as a file:

• open the Ppt slide show. Highlight the slide you want to use.
• Click File > Save As.
• In the Save as Type drop-down menu, select JPEG. Click Save.
• Ppt will ask if you want to export every slide, or just the current slide. Select Currrent Slide only. You can now insert this as an image by selecting Insert > Picture > From File.

The ability to manipulate images in Ppt is not as great as it is in Word. For example, there is no option to change the image layout to "tight", which in Word allows the text to wrap around an image. Instead, in Ppt, you'll need to use several text boxes to simulate the word wrap effect.

Remember that images must be clear and crisp at the magnification at which they will be printed . Zoom to 100% magnification, and check the resolution before sending to the printer!

Finally, consider issues around copyright when using any image other than one that you created, especially when using images found online.

Whenever possible, present your data in a figure or chart, instead of a table. Tables are comparatively dense, and difficult to interpret on-the-fly. If you must use a table, be sure the typeface is large enough that it can be read from 6 feet away. A table can be created directly in PowerPoint by clicking on Insert > Table.

Likewise, charts can be created directly in PowerPoint, or copied/pasted from an Excel file. Creating charts in PowerPoint helps avoid formatting problems. You can re-size a chart just like an image, by pulling on the corners to shrink or enlarge. If possible, include axis labels within the chart, rather than in legends. If necessary, include a brief caption. Ideally, the reader should be able to interpret a chart/graph without needing a caption. And, as always, remember to make the chart large enough to be legible from 6 feet away.

When your poster is nearly finished, print it out on your office copier on legal or ledger size paper for a preview. Step back, and ask yourself what grabs your attention, and whether the poster seems to flow smoothly. Look for overly dominant elements, too-small text, or glaring blank spaces. Ask an impartial friend to proof the text and critique the poster.

Prior to the final printing, ALWAYS ask for a proof. Colors will often look different on the poster paper than they looked on your computer monitor. You may be very sorry if you skip this step!

Large posters are easily transported in a cardboard mailing tube. Some meetings have an address (often at the conference hotel) to which you can mail your poster in advance. For some people, that may be altogether too risky, but it has been known to work. There are also options for creating wrinkle-resistant fabric posters that are foldable, and can be tucked into a suitcase.

Bring a variety of tacks and/or tape to hang the poster. Before hanging, check to be sure you are placing it in the space assigned to you.

Prepare a small number of handouts for those who want the detail and to serve as a human surrogate for those times when your poster is left unattended.

When traveling to a meeting, bring the final poster file on a USB drive as a backup, or at a minimum, email it to yourself. If the poster gets lost or damaged in transit, most cities have a business/store that could do a rush printing.

Once the poster session itself is underway, you finally have the chance to present your work! Hopefully the utter beauty and creativity of your poster will prompt passersby to pause to admire your work, and incidentally, to read some of the fine print. Be ready to explain the highlights of your research, to engage the listeners in conversation around the topic, and to answer any questions they may have.

Adjust the Zoom settings to suit your need at the moment:

• write the text at 25-35%
• use "Fit" to view the entire poster
• switch to 100% (or 200% if you are creating it in half-size) to check the clarity of images. They may look fine at 25%, but dreadful at 100%.

Moving an element on the poster:

• Draw > Nudge will move the element one increment.
• To move in successive increments, use Control/Arrow

The grid and/or ruler can be very helpful for aligning elements, and estimating relative sizes.

To arrange objects an equal distance apart:

• hold down the Control key, and click on at least 3 objects
• Click Draw > Align or Distribute

To control the overlapping of boxes/images, click Order > Bring to Front/Back

There are abundant resources available online. Here are just a few:

• UVM Student Research Conference
• http://www.posterpresentations.com/html/free_poster_templates.html
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Open Access

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Meta-Research Article

Meta-Research Articles feature data-driven examinations of the methods, reporting, verification, and evaluation of scientific research.

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Assessing the evolution of research topics in a biological field using plant science as an example

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America, Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, Michigan, United States of America, DOE-Great Lake Bioenergy Research Center, Michigan State University, East Lansing, Michigan, United States of America

Roles Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing

Affiliation Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America

• Shin-Han Shiu, 
• Melissa D. Lehti-Shiu

• Published: May 23, 2024
• https://doi.org/10.1371/journal.pbio.3002612
• Peer Review
• Reader Comments

Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.

Citation: Shiu S-H, Lehti-Shiu MD (2024) Assessing the evolution of research topics in a biological field using plant science as an example. PLoS Biol 22(5): e3002612. https://doi.org/10.1371/journal.pbio.3002612

Academic Editor: Ulrich Dirnagl, Charite Universitatsmedizin Berlin, GERMANY

Received: October 16, 2023; Accepted: April 4, 2024; Published: May 23, 2024

Copyright: © 2024 Shiu, Lehti-Shiu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The plant science corpus data are available through Zenodo ( https://zenodo.org/records/10022686 ). The codes for the entire project are available through GitHub ( https://github.com/ShiuLab/plant_sci_hist ) and Zenodo ( https://doi.org/10.5281/zenodo.10894387 ).

Funding: This work was supported by the National Science Foundation (IOS-2107215 and MCB-2210431 to MDL and SHS; DGE-1828149 and IOS-2218206 to SHS), Department of Energy grant Great Lakes Bioenergy Research Center (DE-SC0018409 to SHS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: BERT, Bidirectional Encoder Representations from Transformers; br, brassinosteroid; ccTLD, country code Top Level Domain; c-Tf-Idf, class-based Tf-Idf; ChatGPT, Chat Generative Pretrained Transformer; ga, gibberellic acid; LOWESS, locally weighted scatterplot smoothing; MeSH, Medical Subject Heading; SHAP, SHapley Additive exPlanations; SJR, SCImago Journal Rank; Tf-Idf, Term frequency-Inverse document frequency; UMAP, Uniform Manifold Approximation and Projection

Introduction

The explosive growth of scientific data in recent years has been accompanied by a rapidly increasing volume of literature. These records represent a major component of our scientific knowledge and embody the history of conceptual and technological advances in various fields over time. Our ability to wade through these records is important for identifying relevant literature for specific topics, a crucial practice of any scientific pursuit [ 1 ]. Classifying the large body of literature into topics can provide a useful means to identify relevant literature. In addition, these topics offer an opportunity to assess how scientific fields have evolved and when major shifts in took place. However, such classification is challenging because the relevant articles in any topic or domain can number in the tens or hundreds of thousands, and the literature is in the form of natural language, which takes substantial effort and expertise to process [ 2 , 3 ]. In addition, even if one could digest all literature in a field, it would still be difficult to quantify such knowledge.

In the last several years, there has been a quantum leap in natural language processing approaches due to the feasibility of building complex deep learning models with highly flexible architectures [ 4 , 5 ]. The development of large language models such as Bidirectional Encoder Representations from Transformers (BERT; [ 6 ]) and Chat Generative Pretrained Transformer (ChatGPT; [ 7 ]) has enabled the analysis, generation, and modeling of natural language texts in a wide range of applications. The success of these applications is, in large part, due to the feasibility of considering how the same words are used in different contexts when modeling natural language [ 6 ]. One such application is topic modeling, the practice of establishing statistical models of semantic structures underlying a document collection. Topic modeling has been proposed for identifying scientific hot topics over time [ 1 ], for example, in synthetic biology [ 8 ], and it has also been applied to, for example, automatically identify topical scenes in images [ 9 ] and social network topics [ 10 ], discover gene programs highly correlated with cancer prognosis [ 11 ], capture “chromatin topics” that define cell-type differences [ 12 ], and investigate relationships between genetic variants and disease risk [ 13 ]. Here, we use topic modeling to ask how research topics in a scientific field have evolved and what major changes in the research trends have taken place, using plant science as an example.

Plant science corpora allow classification of major research topics

Plant science, broadly defined, is the study of photosynthetic species, their interactions with biotic/abiotic environments, and their applications. For modeling plant science topical evolution, we first identified a collection of plant science documents (i.e., corpus) using a text classification approach. To this end, we first collected over 30 million PubMed records and narrowed down candidate plant science records by searching for those with plant-related terms and taxon names (see Materials and methods ). Because there remained a substantial number of false positives (i.e., biomedical records mentioning plants in passing), a set of positive plant science examples from the 17 plant science journals with the highest numbers of plant science publications covering a wide range of subfields and a set of negative examples from journals with few candidate plant science records were used to train 4 types of text classification models (see Materials and methods ). The best text classification model performed well (F1 = 0.96, F1 of a naïve model = 0.5, perfect model = 1) where the positive and negative examples were clearly separated from each other based on prediction probability of the hold-out testing dataset (false negative rate = 2.6%, false positive rate = 5.2%, S1A and S1B Fig ). The false prediction rate for documents from the 17 plant science journals annotated with the Medical Subject Heading (MeSH) term “Plants” in NCBI was 11.7% (see Materials and methods ). The prediction probability distribution of positive instances with the MeSH term has an expected left-skew to lower values ( S1C Fig ) compared with the distributions of all positive instances ( S1A Fig ). Thus, this subset with the MeSH term is a skewed representation of articles from these 17 major plant science journals. To further benchmark the validity of the plant science records, we also conducted manual annotation of 100 records where the false positive and false negative rates were 14.6% and 10.6%, respectively (see Materials and methods ). Using 12 other plant science journals not included as positive examples as benchmarks, the false negative rate was 9.9% (see Materials and methods ). Considering the range of false prediction rate estimates with different benchmarks, we should emphasize that the model built with the top 17 plant science journals represents a substantial fraction of plant science publications but with biases. Applying the model to the candidate plant science record led to 421,658 positive predictions, hereafter referred to as “plant science records” ( S1D Fig and S1 Data ).

To better understand how the models classified plant science articles, we identified important terms from a more easily interpretable model (Term frequency-Inverse document frequency (Tf-Idf) model; F1 = 0.934) using Shapley Additive Explanations [ 14 ]; 136 terms contributed to predicting plant science records (e.g., Arabidopsis, xylem, seedling) and 138 terms contributed to non-plant science record predictions (e.g., patients, clinical, mice; Tf-Idf feature sheet, S1 Data ). Plant science records as well as PubMed articles grew exponentially from 1950 to 2020 ( Fig 1A ), highlighting the challenges of digesting the rapidly expanding literature. We used the plant science records to perform topic modeling, which consisted of 4 steps: representing each record as a BERT embedding, reducing dimensionality, clustering, and identifying the top terms by calculating class (i.e., topic)-based Tf-Idf (c-Tf-Idf; [ 15 ]). The c-Tf-Idf represents the frequency of a term in the context of how rare the term is to reduce the influence of common words. SciBERT [ 16 ] was the best model among those tested ( S2 Data ) and was used for building the final topic model, which classified 372,430 (88.3%) records into 90 topics defined by distinct combinations of terms ( S3 Data ). The topics contained 620 to 16,183 records and were named after the top 4 to 5 terms defining the topical areas ( Fig 1B and S3 Data ). For example, the top 5 terms representing the largest topic, topic 61 (16,183 records), are “qtl,” “resistance,” “wheat,” “markers,” and “traits,” which represent crop improvement studies using quantitative genetics.

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(A) Numbers of PubMed (magenta) and plant science (green) records between 1950 and 2020. (a, b, c) Coefficients of the exponential function, y = ae b . Data for the plot are in S1 Data . (B) Numbers of documents for the top 30 plant science topics. Each topic is designated by an index number (left) and the top 4–6 terms with the highest cTf-Idf values (right). Data for the plot are in S3 Data . (C) Two-dimensional representation of the relationships between plant science records generated by Uniform Manifold Approximation and Projection (UMAP, [ 17 ]) using SciBERT embeddings of plant science records. All topics panel: Different topics are assigned different colors. Outlier panel: UMAP representation of all records (gray) with outlier records in red. Blue dotted circles: areas with relatively high densities indicating topics that are below the threshold for inclusion in a topic. In the 8 UMAP representations on the right, records for example topics are in red and the remaining records in gray. Blue dotted circles indicate the relative position of topic 48.

https://doi.org/10.1371/journal.pbio.3002612.g001

Records with assigned topics clustered into distinct areas in a two-dimensional (2D) space ( Fig 1C , for all topics, see S4 Data ). The remaining 49,228 outlier records not assigned to any topic (11.7%, middle panel, Fig 1C ) have 3 potential sources. First, some outliers likely belong to unique topics but have fewer records than the threshold (>500, blue dotted circles, Fig 1C ). Second, some of the many outliers dispersed within the 2D space ( Fig 1C ) were not assigned to any single topic because they had relatively high prediction scores for multiple topics ( S2 Fig ). These likely represent studies across subdisciplines in plant science. Third, some outliers are likely interdisciplinary studies between plant science and other domains, such as chemistry, mathematics, and physics. Such connections can only be revealed if records from other domains are included in the analyses.

Topical clusters reveal closely related topics but with distinct key term usage

Related topics tend to be located close together in the 2D representation (e.g., topics 48 and 49, Fig 1C ). We further assessed intertopical relationships by determining the cosine similarities between topics using cTf-Idfs ( Figs 2A and S3 ). In this topic network, some topics are closely related and form topic clusters. For example, topics 25, 26, and 27 collectively represent a more general topic related to the field of plant development (cluster a , lower left in Fig 2A ). Other topic clusters represent studies of stress, ion transport, and heavy metals ( b ); photosynthesis, water, and UV-B ( c ); population and community biology (d); genomics, genetic mapping, and phylogenetics ( e , upper right); and enzyme biochemistry ( f , upper left in Fig 2A ).

(A) Graph depicting the degrees of similarity (edges) between topics (nodes). Between each topic pair, a cosine similarity value was calculated using the cTf-Idf values of all terms. A threshold similarity of 0.6 was applied to illustrate the most related topics. For the full matrix presented as a heatmap, see S4 Fig . The nodes are labeled with topic index numbers and the top 4–6 terms. The colors and width of the edges are defined based on cosine similarity. Example topic clusters are highlighted in yellow and labeled a through f (blue boxes). (B, C) Relationships between the cTf-Idf values (see S3 Data ) of the top terms for topics 26 and 27 (B) and for topics 25 and 27 (C) . Only terms with cTf-Idf ≥ 0.6 are labeled. Terms with cTf-Idf values beyond the x and y axis limit are indicated by pink arrows and cTf-Idf values. (D) The 2D representation in Fig 1C is partitioned into graphs for different years, and example plots for every 5-year period since 1975 are shown. Example topics discussed in the text are indicated. Blue arrows connect the areas occupied by records of example topics across time periods to indicate changes in document frequencies.

https://doi.org/10.1371/journal.pbio.3002612.g002

Topics differed in how well they were connected to each other, reflecting how general the research interests or needs are (see Materials and methods ). For example, topic 24 (stress mechanisms) is the most well connected with median cosine similarity = 0.36, potentially because researchers in many subfields consider aspects of plant stress even though it is not the focus. The least connected topics include topic 21 (clock biology, 0.12), which is surprising because of the importance of clocks in essentially all aspects of plant biology [ 18 ]. This may be attributed, in part, to the relatively recent attention in this area.

Examining topical relationships and the cTf-Idf values of terms also revealed how related topics differ. For example, topic 26 is closely related to topics 27 and 25 (cluster a on the lower left of Fig 2A ). Topics 26 and 27 both contain records of developmental process studies mainly in Arabidopsis ( Fig 2B ); however, topic 26 is focused on the impact of light, photoreceptors, and hormones such as gibberellic acids (ga) and brassinosteroids (br), whereas topic 27 is focused on flowering and floral development. Topic 25 is also focused on plant development but differs from topic 27 because it contains records of studies mainly focusing on signaling and auxin with less emphasis on Arabidopsis ( Fig 2C ). These examples also highlight the importance of using multiple top terms to represent the topics. The similarities in cTf-Idfs between topics were also useful for measuring the editorial scope (i.e., diverse, or narrow) of journals publishing plant science papers using a relative topic diversity measure (see Materials and methods ). For example, Proceedings of the National Academy of Sciences , USA has the highest diversity, while Theoretical and Applied Genetics has the lowest ( S4 Fig ). One surprise is the relatively low diversity of American Journal of Botany , which focuses on plant ecology, systematics, development, and genetics. The low diversity is likely due to the relatively larger number of cellular and molecular science records in PubMed, consistent with the identification of relatively few topical areas relevant to studies at the organismal, population, community, and ecosystem levels.

Investigation of the relative prevalence of topics over time reveals topical succession

We next asked whether relationships between topics reflect chronological progression of certain subfields. To address this, we assessed how prevalent topics were over time using dynamic topic modeling [ 19 ]. As shown in Fig 2D , there is substantial fluctuation in where the records are in the 2D space over time. For example, topic 44 (light, leaves, co, synthesis, photosynthesis) is among the topics that existed in 1975 but has diminished gradually since. In 1985, topic 39 (Agrobacterium-based transformation) became dense enough to be visualized. Additional examples include topics 79 (soil heavy metals), 42 (differential expression), and 82 (bacterial community metagenomics), which became prominent in approximately 2005, 2010, and 2020, respectively ( Fig 2D ). In addition, animating the document occupancy in the 2D space over time revealed a broad change in patterns over time: Some initially dense areas became sparse over time and a large number of topics in areas previously only loosely occupied at the turn of the century increased over time ( S5 Data ).

While the 2D representations reveal substantial details on the evolution of topics, comparison over time is challenging because the number of plant science records has grown exponentially ( Fig 1A ). To address this, the records were divided into 50 chronological bins each with approximately 8,400 records to make cross-bin comparisons feasible ( S6 Data ). We should emphasize that, because of the way the chronological bins were split, the number of records for each topic in each bin should be treated as a normalized value relative to all other topics during the same period. Examining this relative prevalence of topics across bins revealed a clear pattern of topic succession over time (one topic evolved into another) and the presence of 5 topical categories ( Fig 3 ). The topics were categorized based on their locally weighted scatterplot smoothing (LOWESS) fits and ordered according to timing of peak frequency ( S7 and S8 Data , see Materials and methods ). In Fig 3 , the relative decrease in document frequency does not mean that research output in a topic is dwindling. Because each row in the heatmap is normalized based on the minimum and maximum values within each topic, there still can be substantial research output in terms of numbers of publications even when the relative frequency is near zero. Thus, a reduced relative frequency of a topic reflects only a below-average growth rate compared with other topical areas.

(A-E) A heat map of relative topic frequency over time reveals 5 topical categories: (A) stable, (B) early, (C) transitional, (D) sigmoidal, and (E) rising. The x axis denotes different time bins with each bin containing a similar number of documents to account for the exponential growth of plant science records over time. The sizes of all bins except the first are drawn to scale based on the beginning and end dates. The y axis lists different topics denoted by the label and top 4 to 5 terms. In each cell, the prevalence of a topic in a time bin is colored according to the min-max normalized cTf-Idf values for that topic. Light blue dotted lines delineate different decades. The arrows left of a subset of topic labels indicate example relationships between topics in topic clusters. Blue boxes with labels a–f indicate topic clusters, which are the same as those in Fig 2 . Connecting lines indicate successional trends. Yellow circles/lines 1 – 3: 3 major transition patterns. The original data are in S5 Data .

https://doi.org/10.1371/journal.pbio.3002612.g003

The first topical category is a stable category with 7 topics mostly established before the 1980s that have since remained stable in terms of prevalence in the plant science records (top of Fig 3A ). These topics represent long-standing plant science research foci, including studies of plant physiology (topics 4, 58, and 81), genetics (topic 61), and medicinal plants (topic 53). The second category contains 8 topics established before the 1980s that have mostly decreased in prevalence since (the early category, Fig 3B ). Two examples are physiological and morphological studies of hormone action (topic 45, the second in the early category) and the characterization of protein, DNA, and RNA (topic 18, the second to last). Unlike other early topics, topic 78 (paleobotany and plant evolution studies, the last topic in Fig 3B ) experienced a resurgence in the early 2000s due to the development of new approaches and databases and changes in research foci [ 20 ].

The 33 topics in the third, transitional category became prominent in the 1980s, 1990s, or even 2000s but have clearly decreased in prevalence ( Fig 3C ). In some cases, the early and the transitional topics became less prevalent because of topical succession—refocusing of earlier topics led to newer ones that either show no clear sign of decrease (the sigmoidal category, Fig 3D ) or continue to increase in prevalence (the rising category, Fig 3E ). Consistent with the notion of topical succession, topics within each topic cluster ( Fig 2 ) were found across topic categories and/or were prominent at different time periods (indicated by colored lines linking topics, Fig 3 ). One example is topics in topic cluster b (connected with light green lines and arrows, compare Figs 2 and 3 ); the study of cation transport (topic 47, the third in the transitional category), prominent in the 1980s and early 1990s, is connected to 5 other topics, namely, another transitional topic 29 (cation channels and their expression) peaking in the 2000s and early 2010s, sigmoidal topics 24 and 28 (stress response, tolerance mechanisms) and 30 (heavy metal transport), which rose to prominence in mid-2000s, and the rising topic 42 (stress transcriptomic studies), which increased in prevalence in the mid-2010s.

The rise and fall of topics can be due to a combination of technological or conceptual breakthroughs, maturity of the field, funding constraints, or publicity. The study of transposable elements (topic 62) illustrates the effect of publicity; the rise in this field coincided with Barbara McClintock’s 1983 Nobel Prize but not with the publication of her studies in the 1950s [ 21 ]. The reduced prevalence in early 2000 likely occurred in part because analysis of transposons became a central component of genome sequencing and annotation studies, rather than dedicated studies. In addition, this example indicates that our approaches, while capable of capturing topical trends, cannot be used to directly infer major papers leading to the growth of a topic.

Three major topical transition patterns signify shifts in research trends

Beyond the succession of specific topics, 3 major transitions in the dynamic topic graph should be emphasized: (1) the relative decreasing trend of early topics in the late 1970s and early 1980s; (2) the rise of transitional topics in late 1980s; and (3) the relative decreasing trend of transitional topics in the late 1990s and early 2000s, which coincided with a radiation of sigmoidal and rising topics (yellow circles, Fig 3 ). The large numbers of topics involved in these transitions suggest major shifts in plant science research. In transition 1, early topics decreased in relative prevalence in the late 1970s to early 1980s, which coincided with the rise of transitional topics over the following decades (circle 1, Fig 3 ). For example, there was a shift from the study of purified proteins such as enzymes (early topic 48, S5A Fig ) to molecular genetic dissection of genes, proteins, and RNA (transitional topic 35, S5B Fig ) enabled by the wider adoption of recombinant DNA and molecular cloning technologies in late 1970s [ 22 ]. Transition 2 (circle 2, Fig 3 ) can be explained by the following breakthroughs in the late 1980s: better approaches to create transgenic plants and insertional mutants [ 23 ], more efficient creation of mutant plant libraries through chemical mutagenesis (e.g., [ 24 ]), and availability of gene reporter systems such as β-glucuronidase [ 25 ]. Because of these breakthroughs, molecular genetics studies shifted away from understanding the basic machinery to understanding the molecular underpinnings of specific processes, such as molecular mechanisms of flower and meristem development and the action of hormones such as auxin (topic 27, S5C Fig ); this type of research was discussed as a future trend in 1988 [ 26 ] and remains prevalent to this date. Another example is gene silencing (topic 12), which became a focal area of study along with the widespread use of transgenic plants [ 27 ].

Transition 3 is the most drastic: A large number of transitional, sigmoidal, and rising topics became prevalent nearly simultaneously at the turn of the century (circle 3, Fig 3 ). This period also coincides with a rapid increase in plant science citations ( Fig 1A ). The most notable breakthroughs included the availability of the first plant genome in 2000 [ 28 ], increasing ease and reduced cost of high-throughput sequencing [ 29 ], development of new mass spectrometry–based platforms for analyzing proteins [ 30 ], and advancements in microscopic and optical imaging approaches [ 31 ]. Advances in genomics and omics technology also led to an increase in stress transcriptomics studies (42, S5D Fig ) as well as studies in many other topics such as epigenetics (topic 11), noncoding RNA analysis (13), genomics and phylogenetics (80), breeding (41), genome sequencing and assembly (60), gene family analysis (23), and metagenomics (82 and 55).

In addition to the 3 major transitions across all topics, there were also transitions within topics revealed by examining the top terms for different time bins (heatmaps, S5 Fig ). Taken together, these observations demonstrate that knowledge about topical evolution can be readily revealed through topic modeling. Such knowledge is typically only available to experts in specific areas and is difficult to summarize manually, as no researcher has a command of the entire plant science literature.

Analysis of taxa studied reveals changes in research trends

Changes in research trends can also be illustrated by examining changes in the taxa being studied over time ( S9 Data ). There is a strong bias in the taxa studied, with the record dominated by research models and economically important taxa ( S6 Fig ). Flowering plants (Magnoliopsida) are found in 93% of records ( S6A Fig ), and the mustard family Brassicaceae dominates at the family level ( S6B Fig ) because the genus Arabidopsis contributes to 13% of plant science records ( Fig 4A ). When examining the prevalence of taxa being studied over time, clear patterns of turnover emerged similar to topical succession ( Figs 4B , S6C, and S6D ; Materials and methods ). Given that Arabidopsis is mentioned in more publications than other species we analyzed, we further examined the trends for Arabidopsis publications. The increase in the normalized number (i.e., relative to the entire plant science corpus) of Arabidopsis records coincided with advocacy of its use as a model system in the late 1980s [ 32 ]. While it remains a major plant model, there has been a decrease in overall Arabidopsis publications relative to all other plant science publications since 2011 (blue line, normalized total, Fig 4C ). Because the same chronological bins, each with same numbers of records, from the topic-over-time analysis ( Fig 3 ) were used, the decrease here does not mean that there were fewer Arabidopsis publications—in fact, the number of Arabidopsis papers has remained steady since 2011. This decrease means that Arabidopsis-related publications represent a relatively smaller proportion of plant science records. Interestingly, this decrease took place much earlier (approximately 2005) and was steeper in the United States (red line, Fig 4C ) than in all countries combined (blue line, Fig 4C ).

(A) Percentage of records mentioning specific genera. (B) Change in the prevalence of genera in plant science records over time. (C) Changes in the normalized numbers of all records (blue) and records from the US (red) mentioning Arabidopsis over time. The lines are LOWESS fits with fraction parameter = 0.2. (D) Topical over (red) and under (blue) representation among 5 genera with the most plant science records. LLR: log 2 likelihood ratios of each topic in each genus. Gray: topic-species combination not significantly enriched at the 5% level based on enrichment p -values adjusted for multiple testing with the Benjamini–Hochberg method [ 33 ]. The data used for plotting are in S9 Data . The statistics for all topics are in S10 Data .

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Assuming that the normalized number of publications reflects the relative intensity of research activities, one hypothesis for the relative decrease in focus on Arabidopsis is that advances in, for example, plant transformation, genetic manipulation, and genome research have allowed the adoption of more previously nonmodel taxa. Consistent with this, there was a precipitous increase in the number of genera being published in the mid-90s to early 2000s during which approaches for plant transgenics became established [ 34 ], but the number has remained steady since then ( S7A Fig ). The decrease in the proportion of Arabidopsis papers is also negatively correlated with the timing of an increase in the number of draft genomes ( S7B Fig and S9 Data ). It is plausible that genome availability for other species may have contributed to a shift away from Arabidopsis. Strikingly, when we analyzed US National Science Foundation records, we found that the numbers of funded grants mentioning Arabidopsis ( S7C Fig ) have risen and fallen in near perfect synchrony with the normalized number of Arabidopsis publication records (red line, Fig 4C ). This finding likely illustrates the impact of funding on Arabidopsis research.

By considering both taxa information and research topics, we can identify clear differences in the topical areas preferred by researchers using different plant taxa ( Fig 4D and S10 Data ). For example, studies of auxin/light signaling, the circadian clock, and flowering tend to be carried out in Arabidopsis, while quantitative genetic studies of disease resistance tend to be done in wheat and rice, glyphosate research in soybean, and RNA virus research in tobacco. Taken together, joint analyses of topics and species revealed additional details about changes in preferred models over time, and the preferred topical areas for different taxa.

Countries differ in their contributions to plant science and topical preference

We next investigated whether there were geographical differences in topical preference among countries by inferring country information from 330,187 records (see Materials and methods ). The 10 countries with the most records account for 73% of the total, with China and the US contributing to approximately 18% each ( Fig 5A ). The exponential growth in plant science records (green line, Fig 1A ) was in large part due to the rapid rise in annual record numbers in China and India ( Fig 5B ). When we examined the publication growth rates using the top 17 plant science journals, the general patterns remained the same ( S7D Fig ). On the other hand, the US, Japan, Germany, France, and Great Britain had slower rates of growth compared with all non-top 10 countries. The rapid increase in records from China and India was accompanied by a rapid increase in metrics measuring journal impact ( Figs 5C and S8 and S9 Data ). For example, using citation score ( Fig 5C , see Materials and methods ), we found that during a 22-year period China (dark green) and India (light green) rapidly approached the global average (y = 0, yellow), whereas some of the other top 10 countries, particularly the US (red) and Japan (yellow green), showed signs of decrease ( Fig 5C ). It remains to be determined whether these geographical trends reflect changes in priority, investment, and/or interest in plant science research.

(A) Numbers of plant science records for countries with the 10 highest numbers. (B) Percentage of all records from each of the top 10 countries from 1980 to 2020. (C) Difference in citation scores from 1999 to 2020 for the top 10 countries. (D) Shown for each country is the relationship between the citation scores averaged from 1999 to 2020 and the slope of linear fit with year as the predictive variable and citation score as the response variable. The countries with >400 records and with <10% missing impact values are included. Data used for plots (A–D) are in S11 Data . (E) Correlation in topic enrichment scores between the top 10 countries. PCC, Pearson’s correlation coefficient, positive in red, negative in blue. Yellow rectangle: countries with more similar topical preferences. (F) Enrichment scores (LLR, log likelihood ratio) of selected topics among the top 10 countries. Red: overrepresentation, blue: underrepresentation. Gray: topic-country combination that is not significantly enriched at the 5% level based on enrichment p -values adjusted for multiple testing with the Benjamini–Hochberg method (for all topics and plotting data, see S12 Data ).

https://doi.org/10.1371/journal.pbio.3002612.g005

Interestingly, the relative growth/decline in citation scores over time (measured as the slope of linear fit of year versus citation score) was significantly and negatively correlated with average citation score ( Fig 5D ); i.e., countries with lower overall metrics tended to experience the strongest increase in citation scores over time. Thus, countries that did not originally have a strong influence on plant sciences now have increased impact. These patterns were also observed when using H-index or journal rank as metrics ( S8 Fig and S11 Data ) and were not due to increased publication volume, as the metrics were normalized against numbers of records from each country (see Materials and methods ). In addition, the fact that different metrics with different caveats and assumptions yielded consistent conclusions indicates the robustness of our observations. We hypothesize that this may be a consequence of the ease in scientific communication among geographically isolated research groups. It could also be because of the prevalence of online journals that are open access, which makes scientific information more readily accessible. Or it can be due to the increasing international collaboration. In any case, the causes for such regression toward the mean are not immediately clear and should be addressed in future studies.

We also assessed how the plant research foci of countries differ by comparing topical preference (i.e., the degree of enrichment of plant science records in different topics) between countries. For example, Italy and Spain cluster together (yellow rectangle, Fig 5E ) partly because of similar research focusing on allergens (topic 0) and mycotoxins (topic 54) and less emphasis on gene family (topic 23) and stress tolerance (topic 28) studies ( Fig 5F , for the fold enrichment and corrected p -values of all topics, see S12 Data ). There are substantial differences in topical focus between countries ( S9 Fig ). For example, research on new plant compounds associated with herbal medicine (topic 69) is a focus in China but not in the US, but the opposite is true for population genetics and evolution (topic 86) ( Fig 5F ). In addition to revealing how plant science research has evolved over time, topic modeling provides additional insights into differences in research foci among different countries, which are informative for science policy considerations.

In this study, topic modeling revealed clear transitions among research topics, which represent shifts in research trends in plant sciences. One limitation of our study is the bias in the PubMed-based corpus. The cellular, molecular, and physiological aspects of plant sciences are well represented, but there are many fewer records related to evolution, ecology, and systematics. Our use of titles/abstracts from the top 17 plant science journals as positive examples allowed us to identify papers we typically see in these journals, but this may have led to us missing “outlier” articles, which may be the most exciting. Another limitation is the need to assign only one topic to a record when a study is interdisciplinary and straddles multiple topics. Furthermore, a limited number of large, inherently heterogeneous topics were summarized to provide a more concise interpretation, which undoubtedly underrepresents the diversity of plant science research. Despite these limitations, dynamic topic modeling revealed changes in plant science research trends that coincide with major shifts in biological science. While we were interested in identifying conceptual advances, our approach can identify the trend but the underlying causes for such trends, particularly key records leading to the growth in certain topics, still need to be identified. It also remains to be determined which changes in research trends lead to paradigm shifts as defined by Kuhn [ 35 ].

The key terms defining the topics frequently describe various technologies (e.g., topic 38/39: transformation, 40: genome editing, 59: genetic markers, 65: mass spectrometry, 69: nuclear magnetic resonance) or are indicative of studies enabled through molecular genetics and omics technologies (e.g., topic 8/60: genome, 11: epigenetic modifications, 18: molecular biological studies of macromolecules, 13: small RNAs, 61: quantitative genetics, 82/84: metagenomics). Thus, this analysis highlights how technological innovation, particularly in the realm of omics, has contributed to a substantial number of research topics in the plant sciences, a finding that likely holds for other scientific disciplines. We also found that the pattern of topic evolution is similar to that of succession, where older topics have mostly decreased in relative prevalence but appear to have been superseded by newer ones. One example is the rise of transcriptome-related topics and the correlated, reduced focus on regulation at levels other than transcription. This raises the question of whether research driven by technology negatively impacts other areas of research where high-throughput studies remain challenging.

One observation on the overall trends in plant science research is the approximately 10-year cycle in major shifts. One hypothesis is related to not only scientific advances but also to the fashion-driven aspect of science. Nonetheless, given that there were only 3 major shifts and the sample size is small, it is difficult to speculate as to why they happened. By analyzing the country of origin, we found that China and India have been the 2 major contributors to the growth in the plant science records in the last 20 years. Our findings also show an equalizing trend in global plant science where countries without a strong plant science publication presence have had an increased impact over the last 20 years. In addition, we identified significant differences in research topics between countries reflecting potential differences in investment and priorities. Such information is important for discerning differences in research trends across countries and can be considered when making policy decisions about research directions.

Materials and methods

Collection and preprocessing of a candidate plant science corpus.

For reproducibility purposes, a random state value of 20220609 was used throughout the study. The PubMed baseline files containing citation information ( ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/ ) were downloaded on November 11, 2021. To narrow down the records to plant science-related citations, a candidate citation was identified as having, within the titles and/or abstracts, at least one of the following words: “plant,” “plants,” “botany,” “botanical,” “planta,” and “plantarum” (and their corresponding upper case and plural forms), or plant taxon identifiers from NCBI Taxonomy ( https://www.ncbi.nlm.nih.gov/taxonomy ) or USDA PLANTS Database ( https://plants.sc.egov.usda.gov/home ). Note the search terms used here have nothing to do with the values of the keyword field in PubMed records. The taxon identifiers include all taxon names including and at taxonomic levels below “Viridiplantae” till the genus level (species names not used). This led to 51,395 search terms. After looking for the search terms, qualified entries were removed if they were duplicated, lacked titles and/or abstracts, or were corrections, errata, or withdrawn articles. This left 1,385,417 citations, which were considered the candidate plant science corpus (i.e., a collection of texts). For further analysis, the title and abstract for each citation were combined into a single entry. Text was preprocessed by lowercasing, removing stop-words (i.e., common words), removing non-alphanumeric and non-white space characters (except Greek letters, dashes, and commas), and applying lemmatization (i.e., grouping inflected forms of a word as a single word) for comparison. Because lemmatization led to truncated scientific terms, it was not included in the final preprocessing pipeline.

Definition of positive/negative examples

Upon closer examination, a large number of false positives were identified in the candidate plant science records. To further narrow down citations with a plant science focus, text classification was used to distinguish plant science and non-plant science articles (see next section). For the classification task, a negative set (i.e., non-plant science citations) was defined as entries from 7,360 journals that appeared <20 times in the filtered data (total = 43,329, journal candidate count, S1 Data ). For the positive examples (i.e., true plant science citations), 43,329 plant science citations (positive examples) were sampled from 17 established plant science journals each with >2,000 entries in the filtered dataset: “Plant physiology,” “Frontiers in plant science,” “Planta,” “The Plant journal: for cell and molecular biology,” “Journal of experimental botany,” “Plant molecular biology,” “The New phytologist,” “The Plant cell,” “Phytochemistry,” “Plant & cell physiology,” “American journal of botany,” “Annals of botany,” “BMC plant biology,” “Tree physiology,” “Molecular plant-microbe interactions: MPMI,” “Plant biology,” and “Plant biotechnology journal” (journal candidate count, S1 Data ). Plant biotechnology journal was included, but only 1,894 records remained after removal of duplicates, articles with missing info, and/or withdrawn articles. The positive and negative sets were randomly split into training and testing subsets (4:1) while maintaining a 1:1 positive-to-negative ratio.

Text classification based on Tf and Tf-Idf

Instead of using the preprocessed text as features for building classification models directly, text embeddings (i.e., representations of texts in vectors) were used as features. These embeddings were generated using 4 approaches (model summary, S1 Data ): Term-frequency (Tf), Tf-Idf [ 36 ], Word2Vec [ 37 ], and BERT [ 6 ]. The Tf- and Tf-Idf-based features were generated with CountVectorizer and TfidfVectorizer, respectively, from Scikit-Learn [ 38 ]. Different maximum features (1e4 to 1e5) and n-gram ranges (uni-, bi-, and tri-grams) were tested. The features were selected based on the p- value of chi-squared tests testing whether a feature had a higher-than-expected value among the positive or negative classes. Four different p- value thresholds were tested for feature selection. The selected features were then used to retrain vectorizers with the preprocessed training texts to generate feature values for classification. The classification model used was XGBoost [ 39 ] with 5 combinations of the following hyperparameters tested during 5-fold stratified cross-validation: min_child_weight = (1, 5, 10), gamma = (0.5, 1, 1.5, 2.5), subsample = (0.6, 0.8, 1.0), colsample_bytree = (0.6, 0.8, 1.0), and max_depth = (3, 4, 5). The rest of the hyperparameters were held constant: learning_rate = 0.2, n_estimators = 600, objective = binary:logistic. RandomizedSearchCV from Scikit-Learn was used for hyperparameter tuning and cross-validation with scoring = F1-score.

Because the Tf-Idf model had a relatively high model performance and was relatively easy to interpret (terms are frequency-based, instead of embedding-based like those generated by Word2Vec and BERT), the Tf-Idf model was selected as input to SHapley Additive exPlanations (SHAP; [ 14 ]) to assess the importance of terms. Because the Tf-Idf model was based on XGBoost, a tree-based algorithm, the TreeExplainer module in SHAP was used to determine a SHAP value for each entry in the training dataset for each Tf-Idf feature. The SHAP value indicates the degree to which a feature positively or negatively affects the underlying prediction. The importance of a Tf-Idf feature was calculated as the average SHAP value of that feature among all instances. Because a Tf-Idf feature is generated based on a specific term, the importance of the Tf-Idf feature indicates the importance of the associated term.

Text classification based on Word2Vec

The preprocessed texts were first split into train, validation, and test subsets (8:1:1). The texts in each subset were converted to 3 n-gram lists: a unigram list obtained by splitting tokens based on the space character, or bi- and tri-gram lists built with Gensim [ 40 ]. Each n-gram list of the training subset was next used to fit a Skip-gram Word2Vec model with vector_size = 300, window = 8, min_count = (5, 10, or 20), sg = 1, and epochs = 30. The Word2Vec model was used to generate word embeddings for train, validate, and test subsets. In the meantime, a tokenizer was trained with train subset unigrams using Tensorflow [ 41 ] and used to tokenize texts in each subset and turn each token into indices to use as features for training text classification models. To ensure all citations had the same number of features (500), longer texts were truncated, and shorter ones were zero-padded. A deep learning model was used to train a text classifier with an input layer the same size as the feature number, an attention layer incorporating embedding information for each feature, 2 bidirectional Long-Short-Term-Memory layers (15 units each), a dense layer (64 units), and a final, output layer with 2 units. During training, adam, accuracy, and sparse_categorical_crossentropy were used as the optimizer, evaluation metric, and loss function, respectively. The training process lasted 30 epochs with early stopping if validation loss did not improve in 5 epochs. An F1 score was calculated for each n-gram list and min_count parameter combination to select the best model (model summary, S1 Data ).

Text classification based on BERT models

Two pretrained models were used for BERT-based classification: DistilBERT (Hugging face repository [ 42 ] model name and version: distilbert-base-uncased [ 43 ]) and SciBERT (allenai/scibert-scivocab-uncased [ 16 ]). In both cases, tokenizers were retrained with the training data. BERT-based models had the following architecture: the token indices (512 values for each token) and associated masked values as input layers, pretrained BERT layer (512 × 768) excluding outputs, a 1D pooling layer (768 units), a dense layer (64 units), and an output layer (2 units). The rest of the training parameters were the same as those for Word2Vec-based models, except training lasted for 20 epochs. Cross-validation F1-scores for all models were compared and used to select the best model for each feature extraction method, hyperparameter combination, and modeling algorithm or architecture (model summary, S1 Data ). The best model was the Word2Vec-based model (min_count = 20, window = 8, ngram = 3), which was applied to the candidate plant science corpus to identify a set of plant science citations for further analysis. The candidate plant science records predicted as being in the positive class (421,658) by the model were collectively referred to as the “plant science corpus.”

Plant science record classification

In PubMed, 1,384,718 citations containing “plant” or any plant taxon names (from the phylum to genus level) were considered candidate plant science citations. To further distinguish plant science citations from those in other fields, text classification models were trained using titles and abstracts of positive examples consisting of citations from 17 plant science journals, each with >2,000 entries in PubMed, and negative examples consisting of records from journals with fewer than 20 entries in the candidate set. Among 4 models tested the best model (built with Word2Vec embeddings) had a cross validation F1 of 0.964 (random guess F1 = 0.5, perfect model F1 = 1, S1 Data ). When testing the model using 17,330 testing set citations independent from the training set, the F1 remained high at 0.961.

We also conducted another analysis attempting to use the MeSH term “Plants” as a benchmark. Records with the MeSH term “Plants” also include pharmaceutical studies of plants and plant metabolites or immunological studies of plants as allergens in journals that are not generally considered plant science journals (e.g., Acta astronautica , International journal for parasitology , Journal of chromatography ) or journals from local scientific societies (e.g., Acta pharmaceutica Hungarica , Huan jing ke xue , Izvestiia Akademii nauk . Seriia biologicheskaia ). Because we explicitly labeled papers from such journals as negative examples, we focused on 4,004 records with the “Plants” MeSH term published in the 17 plant science journals that were used as positive instances and found that 88.3% were predicted as the positive class. Thus, based on the MeSH term, there is an 11.7% false prediction rate.

We also enlisted 5 plant science colleagues (3 advanced graduate students in plant biology and genetic/genome science graduate programs, 1 postdoctoral breeder/quantitative biologist, and 1 postdoctoral biochemist/geneticist) to annotate 100 randomly selected abstracts as a reviewer suggested. Each record was annotated by 2 colleagues. Among 85 entries where the annotations are consistent between annotators, 48 were annotated as negative but with 7 predicted as positive (false positive rate = 14.6%) and 37 were annotated as positive but with 4 predicted as negative (false negative rate = 10.8%). To further benchmark the performance of the text classification model, we identified another 12 journals that focus on plant science studies to use as benchmarks: Current opinion in plant biology (number of articles: 1,806), Trends in plant science (1,723), Functional plant biology (1,717), Molecular plant pathology (1,573), Molecular plant (1,141), Journal of integrative plant biology (1,092), Journal of plant research (1,032), Physiology and molecular biology of plants (830), Nature plants (538), The plant pathology journal (443). Annual review of plant biology (417), and The plant genome (321). Among the 12,611 candidate plant science records, 11,386 were predicted as positive. Thus, there is a 9.9% false negative rate.

Global topic modeling

BERTopic [ 15 ] was used for preliminary topic modeling with n-grams = (1,2) and with an embedding initially generated by DistilBERT, SciBERT, or BioBERT (dmis-lab/biobert-base-cased-v1.2; [ 44 ]). The embedding models converted preprocessed texts to embeddings. The topics generated based on the 3 embeddings were similar ( S2 Data ). However, SciBERT-, BioBERT-, and distilBERT-based embedding models had different numbers of outlier records (268,848, 293,790, and 323,876, respectively) with topic index = −1. In addition to generating the fewest outliers, the SciBERT-based model led to the highest number of topics. Therefore, SciBERT was chosen as the embedding model for the final round of topic modeling. Modeling consisted of 3 steps. First, document embeddings were generated with SentenceTransformer [ 45 ]. Second, a clustering model to aggregate documents into clusters using hdbscan [ 46 ] was initialized with min_cluster_size = 500, metric = euclidean, cluster_selection_method = eom, min_samples = 5. Third, the embedding and the initialized hdbscan model were used in BERTopic to model topics with neighbors = 10, nr_topics = 500, ngram_range = (1,2). Using these parameters, 90 topics were identified. The initial topic assignments were conservative, and 241,567 records were considered outliers (i.e., documents not assigned to any of the 90 topics). After assessing the prediction scores of all records generated from the fitted topic models, the 95-percentile score was 0.0155. This score was used as the threshold for assigning outliers to topics: If the maximum prediction score was above the threshold and this maximum score was for topic t , then the outlier was assigned to t . After the reassignment, 49,228 records remained outliers. To assess if some of the outliers were not assigned because they could be assigned to multiple topics, the prediction scores of the records were used to put records into 100 clusters using k- means. Each cluster was then assessed to determine if the outlier records in a cluster tended to have higher prediction scores across multiple topics ( S2 Fig ).

Topics that are most and least well connected to other topics

The most well-connected topics in the network include topic 24 (stress mechanisms, median cosine similarity = 0.36), topic 42 (genes, stress, and transcriptomes, 0.34), and topic 35 (molecular genetics, 0.32, all t test p -values < 1 × 10 −22 ). The least connected topics include topic 0 (allergen research, median cosine similarity = 0.12), topic 21 (clock biology, 0.12), topic 1 (tissue culture, 0.15), and topic 69 (identification of compounds with spectroscopic methods, 0.15; all t test p- values < 1 × 10 −24 ). Topics 0, 1, and 69 are specialized topics; it is surprising that topic 21 is not as well connected as explained in the main text.

Analysis of documents based on the topic model

Topical diversity among top journals with the most plant science records

Using a relative topic diversity measure (ranging from 0 to 10), we found that there was a wide range of topical diversity among 20 journals with the largest numbers of plant science records ( S3 Fig ). The 4 journals with the highest relative topical diversities are Proceedings of the National Academy of Sciences , USA (9.6), Scientific Reports (7.1), Plant Physiology (6.7), and PLOS ONE (6.4). The high diversities are consistent with the broad, editorial scopes of these journals. The 4 journals with the lowest diversities are American Journal of Botany (1.6), Oecologia (0.7), Plant Disease (0.7), and Theoretical and Applied Genetics (0.3), which reflects their discipline-specific focus and audience of classical botanists, ecologists, plant pathologists, and specific groups of geneticists.

Dynamic topic modeling

The codes for dynamic modeling were based on _topic_over_time.py in BERTopics and modified to allow additional outputs for debugging and graphing purposes. The plant science citations were binned into 50 subsets chronologically (for timestamps of bins, see S5 Data ). Because the numbers of documents increased exponentially over time, instead of dividing them based on equal-sized time intervals, which would result in fewer records at earlier time points and introduce bias, we divided them into time bins of similar size (approximately 8,400 documents). Thus, the earlier time subsets had larger time spans compared with later time subsets. If equal-size time intervals were used, the numbers of documents between the intervals would differ greatly; the earlier time points would have many fewer records, which may introduce bias. Prior to binning the subsets, the publication dates were converted to UNIX time (timestamp) in seconds; the plant science records start in 1917-11-1 (timestamp = −1646247600.0) and end in 2021-1-1 (timestamp = 1609477201). The starting dates and corresponding timestamps for the 50 subsets including the end date are in S6 Data . The input data included the preprocessed texts, topic assignments of records from global topic modeling, and the binned timestamps of records. Three additional parameters were set for topics_over_time, namely, nr_bin = 50 (number of bins), evolution_tuning = True, and global_tuning = False. The evolution_tuning parameter specified that averaged c-Tf-Idf values for a topic be calculated in neighboring time bins to reduce fluctuation in c-Tf-Idf values. The global_tuning parameter was set to False because of the possibility that some nonexisting terms could have a high c-Tf-Idf for a time bin simply because there was a high global c-Tf-Idf value for that term.

The binning strategy based on similar document numbers per bin allowed us to increase signal particularly for publications prior to the 90s. This strategy, however, may introduce more noise for bins with smaller time durations (i.e., more recent bins) because of publication frequencies (there can be seasonal differences in the number of papers published, biased toward, e.g., the beginning of the year or the beginning of a quarter). To address this, we examined the relative frequencies of each topic over time ( S7 Data ), but we found that recent time bins had similar variances in relative frequencies as other time bins. We also moderated the impact of variation using LOWESS (10% to 30% of the data points were used for fitting the trend lines) to determine topical trends for Fig 3 . Thus, the influence of the noise introduced via our binning strategy is expected to be minimal.

Topic categories and ordering

The topics were classified into 5 categories with contrasting trends: stable, early, transitional, sigmoidal, and rising. To define which category a topic belongs to, the frequency of documents over time bins for each topic was analyzed using 3 regression methods. We first tried 2 forecasting methods: recursive autoregressor (the ForecasterAutoreg class in the skforecast package) and autoregressive integrated moving average (ARIMA implemented in the pmdarima package). In both cases, the forecasting results did not clearly follow the expected trend lines, likely due to the low numbers of data points (relative frequency values), which resulted in the need to extensively impute missing data. Thus, as a third approach, we sought to fit the trendlines with the data points using LOWESS (implemented in the statsmodels package) and applied additional criteria for assigning topics to categories. When fitting with LOWESS, 3 fraction parameters (frac, the fraction of the data used when estimating each y-value) were evaluated (0.1, 0.2, 0.3). While frac = 0.3 had the smallest errors for most topics, in situations where there were outliers, frac = 0.2 or 0.1 was chosen to minimize mean squared errors ( S7 Data ).

The topics were classified into 5 categories based on the slopes of the fitted line over time: (1) stable: topics with near 0 slopes over time; (2) early: topics with negative (<−0.5) slopes throughout (with the exception of topic 78, which declined early on but bounced back by the late 1990s); (3) transitional: early positive (>0.5) slopes followed by negative slopes at later time points; (4) sigmoidal: early positive slopes followed by zero slopes at later time points; and (5) rising: continuously positive slopes. For each topic, the LOWESS fits were also used to determine when the relative document frequency reached its peak, first reaching a threshold of 0.6 (chosen after trial and error for a range of 0.3 to 0.9), and the overall trend. The topics were then ordered based on (1) whether they belonged to the stable category or not; (2) whether the trends were decreasing, stable, or increasing; (3) the time the relative document frequency first reached 0.6; and (4) the time that the overall peak was reached ( S8 Data ).

Taxa information

To identify a taxon or taxa in all plant science records, NCBI Taxonomy taxdump datasets were downloaded from the NCBI FTP site ( https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/new_taxdump/ ) on September 20, 2022. The highest-level taxon was Viridiplantae, and all its child taxa were parsed and used as queries in searches against the plant science corpus. In addition, a species-over-time analysis was conducted using the same time bins as used for dynamic topic models. The number of records in different time bins for top taxa are in the genus, family, order, and additional species level sheet in S9 Data . The degree of over-/underrepresentation of a taxon X in a research topic T was assessed using the p -value of a Fisher’s exact test for a 2 × 2 table consisting of the numbers of records in both X and T, in X but not T, in T but not X, and in neither ( S10 Data ).

For analysis of plant taxa with genome information, genome data of taxa in Viridiplantae were obtained from the NCBI Genome data-hub ( https://www.ncbi.nlm.nih.gov/data-hub/genome ) on October 28, 2022. There were 2,384 plant genome assemblies belonging to 1,231 species in 559 genera (genome assembly sheet, S9 Data ). The date of the assembly was used as a proxy for the time when a genome was sequenced. However, some species have updated assemblies and have more recent data than when the genome first became available.

Taxa being studied in the plant science records

Flowering plants (Magnoliopsida) are found in 93% of records, while most other lineages are discussed in <1% of records, with conifers and related species being exceptions (Acrogynomsopermae, 3.5%, S6A Fig ). At the family level, the mustard (Brassicaceae), grass (Poaceae), pea (Fabaceae), and nightshade (Solanaceae) families are in 51% of records ( S6B Fig ). The prominence of the mustard family in plant science research is due to the Brassica and Arabidopsis genera ( Fig 4A ). When examining the prevalence of taxa being studied over time, clear patterns of turnovers emerged ( Figs 4B , S6C, and S6D ). While the study of monocot species (Liliopsida) has remained steady, there was a significant uptick in the prevalence of eudicot (eudicotyledon) records in the late 90s ( S6C Fig ), which can be attributed to the increased number of studies in the mustard, myrtle (Myrtaceae), and mint (Lamiaceae) families among others ( S6D Fig ). At the genus level, records mentioning Gossypium (cotton), Phaseolus (bean), Hordeum (wheat), and Zea (corn), similar to the topics in the early category, were prevalent till the 1980s or 1990s but have mostly decreased in number since ( Fig 4B ). In contrast, Capsicum , Arabidopsis , Oryza , Vitus , and Solanum research has become more prevalent over the last 20 years.

Geographical information for the plant science corpus

The geographical information (country) of authors in the plant science corpus was obtained from the address (AD) fields of first authors in Medline XML records accessible through the NCBI EUtility API ( https://www.ncbi.nlm.nih.gov/books/NBK25501/ ). Because only first author affiliations are available for records published before December 2014, only the first author’s location was considered to ensure consistency between records before and after that date. Among the 421,658 records in the plant science corpus, 421,585 had Medline records and 421,276 had unique PMIDs. Among the records with unique PMIDs, 401,807 contained address fields. For each of the remaining records, the AD field content was split into tokens with a “,” delimiter, and the token likely containing geographical info (referred to as location tokens) was selected as either the last token or the second to last token if the last token contained “@” indicating the presence of an email address. Because of the inconsistency in how geographical information was described in the location tokens (e.g., country, state, city, zip code, name of institution, and different combinations of the above), the following 4 approaches were used to convert location tokens into countries.

The first approach was a brute force search where full names and alpha-3 codes of current countries (ISO 3166–1), current country subregions (ISO 3166–2), and historical country (i.e., country that no longer exists, ISO 3166–3) were used to search the address fields. To reduce false positives using alpha-3 codes, a space prior to each code was required for the match. The first approach allowed the identification of 361,242, 16,573, and 279,839 records with current country, historical country, and subregion information, respectively. The second method was the use of a heuristic based on common address field structures to identify “location strings” toward the end of address fields that likely represent countries, then the use of the Python pycountry module to confirm the presence of country information. This approach led to 329,025 records with country information. The third approach was to parse first author email addresses (90,799 records), recover top-level domain information, and use country code Top Level Domain (ccTLD) data from the ISO 3166 Wikipedia page to define countries (72,640 records). Only a subset of email addresses contains country information because some are from companies (.com), nonprofit organizations (.org), and others. Because a large number of records with address fields still did not have country information after taking the above 3 approaches, another approach was implemented to query address fields against a locally installed Nominatim server (v.4.2.3, https://github.com/mediagis/nominatim-docker ) using OpenStreetMap data from GEOFABRIK ( https://www.geofabrik.de/ ) to find locations. Initial testing indicated that the use of full address strings led to false positives, and the computing resource requirement for running the server was high. Thus, only location strings from the second approach that did not lead to country information were used as queries. Because multiple potential matches were returned for each query, the results were sorted based on their location importance values. The above steps led to an additional 72,401 records with country information.

Examining the overlap in country information between approaches revealed that brute force current country and pycountry searches were consistent 97.1% of the time. In addition, both approaches had high consistency with the email-based approach (92.4% and 93.9%). However, brute force subregion and Nominatim-based predictions had the lowest consistencies with the above 3 approaches (39.8% to 47.9%) and each other. Thus, a record’s country information was finalized if the information was consistent between any 2 approaches, except between the brute force subregion and Nominatim searches. This led to 330,328 records with country information.

Topical and country impact metrics

To determine annual country impact, impact scores were determined in the same way as that for annual topical impact, except that values for different countries were calculated instead of topics ( S8 Data ).

Topical preferences by country

To determine topical preference for a country C , a 2 × 2 table was established with the number of records in topic T from C , the number of records in T but not from C , the number of non- T records from C , and the number of non- T records not from C . A Fisher’s exact test was performed for each T and C combination, and the resulting p -values were corrected for multiple testing with the Bejamini–Hochberg method (see S12 Data ). The preference of T in C was defined as the degree of enrichment calculated as log likelihood ratio of values in the 2 × 2 table. Topic 5 was excluded because >50% of the countries did not have records for this topic.

The top 10 countries could be classified into a China–India cluster, an Italy–Spain cluster, and remaining countries (yellow rectangles, Fig 5E ). The clustering of Italy and Spain is partly due to similar research focusing on allergens (topic 0) and mycotoxins (topic 54) and less emphasis on gene family (topic 23) and stress tolerance (topic 28) studies ( Figs 5F and S9 ). There are also substantial differences in topical focus between countries. For example, plant science records from China tend to be enriched in hyperspectral imaging and modeling (topic 9), gene family studies (topic 23), stress biology (topic 28), and research on new plant compounds associated with herbal medicine (topic 69), but less emphasis on population genetics and evolution (topic 86, Fig 5F ). In the US, there is a strong focus on insect pest resistance (topic 75), climate, community, and diversity (topic 83), and population genetics and evolution but less focus on new plant compounds. In summary, in addition to revealing how plant science research has evolved over time, topic modeling provides additional insights into differences in research foci among different countries.

Supporting information

S1 fig. plant science record classification model performance..

(A–C) Distributions of prediction probabilities (y_prob) of (A) positive instances (plant science records), (B) negative instances (non-plant science records), and (C) positive instances with the Medical Subject Heading “Plants” (ID = D010944). The data are color coded in blue and orange if they are correctly and incorrectly predicted, respectively. The lower subfigures contain log10-transformed x axes for the same distributions as the top subfigure for better visualization of incorrect predictions. (D) Prediction probability distribution for candidate plant science records. Prediction probabilities plotted here are available in S13 Data .

https://doi.org/10.1371/journal.pbio.3002612.s001

S2 Fig. Relationships between outlier clusters and the 90 topics.

(A) Heatmap demonstrating that some outlier clusters tend to have high prediction scores for multiple topics. Each cell shows the average prediction score of a topic for records in an outlier cluster. (B) Size of outlier clusters.

https://doi.org/10.1371/journal.pbio.3002612.s002

S3 Fig. Cosine similarities between topics.

(A) Heatmap showing cosine similarities between topic pairs. Top-left: hierarchical clustering of the cosine similarity matrix using the Ward algorithm. The branches are colored to indicate groups of related topics. (B) Topic labels and names. The topic ordering was based on hierarchical clustering of topics. Colored rectangles: neighboring topics with >0.5 cosine similarities.

https://doi.org/10.1371/journal.pbio.3002612.s003

S4 Fig. Relative topical diversity for 20 journals.

The 20 journals with the most plant science records are shown. The journal names were taken from the journal list in PubMed ( https://www.nlm.nih.gov/bsd/serfile_addedinfo.html ).

https://doi.org/10.1371/journal.pbio.3002612.s004

S5 Fig. Topical frequency and top terms during different time periods.

(A-D) Different patterns of topical frequency distributions for example topics (A) 48, (B) 35, (C) 27, and (D) 42. For each topic, the top graph shows the frequency of topical records in each time bin, which are the same as those in Fig 3 (green line), and the end date for each bin is indicated. The heatmap below each line plot depicts whether a term is among the top terms in a time bin (yellow) or not (blue). Blue dotted lines delineate different decades (see S5 Data for the original frequencies, S6 Data for the LOWESS fitted frequencies and the top terms for different topics/time bins).

https://doi.org/10.1371/journal.pbio.3002612.s005

S6 Fig. Prevalence of records mentioning different taxonomic groups in Viridiplantae.

(A, B) Percentage of records mentioning specific taxa at the ( A) major lineage and (B) family levels. (C, D) The prevalence of taxon mentions over time at the (C) major lineage and (E) family levels. The data used for plotting are available in S9 Data .

https://doi.org/10.1371/journal.pbio.3002612.s006

S7 Fig. Changes over time.

(A) Number of genera being mentioned in plant science records during different time bins (the date indicates the end date of that bin, exclusive). (B) Numbers of genera (blue) and organisms (salmon) with draft genomes available from National Center of Biotechnology Information in different years. (C) Percentage of US National Science Foundation (NSF) grants mentioning the genus Arabidopsis over time with peak percentage and year indicated. The data for (A–C) are in S9 Data . (D) Number of plant science records in the top 17 plant science journals from the USA (red), Great Britain (GBR) (orange), India (IND) (light green), and China (CHN) (dark green) normalized against the total numbers of publications of each country over time in these 17 journals. The data used for plotting can be found in S11 Data .

https://doi.org/10.1371/journal.pbio.3002612.s007

S8 Fig. Change in country impact on plant science over time.

(A, B) Difference in 2 impact metrics from 1999 to 2020 for the 10 countries with the highest number of plant science records. (A) H-index. (B) SCImago Journal Rank (SJR). (C, D) Plots show the relationships between the impact metrics (H-index in (C) , SJR in (D) ) averaged from 1999 to 2020 and the slopes of linear fits with years as the predictive variable and impact metric as the response variable for different countries (A3 country codes shown). The countries with >400 records and with <10% missing impact values are included. The data used for plotting can be found in S11 Data .

https://doi.org/10.1371/journal.pbio.3002612.s008

S9 Fig. Country topical preference.

Enrichment scores (LLR, log likelihood ratio) of topics for each of the top 10 countries. Red: overrepresentation, blue: underrepresentation. The data for plotting can be found in S12 Data .

https://doi.org/10.1371/journal.pbio.3002612.s009

S1 Data. Summary of source journals for plant science records, prediction models, and top Tf-Idf features.

Sheet–Candidate plant sci record j counts: Number of records from each journal in the candidate plant science corpus (before classification). Sheet—Plant sci record j count: Number of records from each journal in the plant science corpus (after classification). Sheet–Model summary: Model type, text used (txt_flag), and model parameters used. Sheet—Model performance: Performance of different model and parameter combinations on the validation data set. Sheet–Tf-Idf features: The average SHAP values of Tf-Idf (Term frequency-Inverse document frequency) features associated with different terms. Sheet–PubMed number per year: The data for PubMed records in Fig 1A . Sheet–Plant sci record num per yr: The data for the plant science records in Fig 1A .

https://doi.org/10.1371/journal.pbio.3002612.s010

S2 Data. Numbers of records in topics identified from preliminary topic models.

Sheet–Topics generated with a model based on BioBERT embeddings. Sheet–Topics generated with a model based on distilBERT embeddings. Sheet–Topics generated with a model based on SciBERT embeddings.

https://doi.org/10.1371/journal.pbio.3002612.s011

S3 Data. Final topic model labels and top terms for topics.

Sheet–Topic label: The topic index and top 10 terms with the highest cTf-Idf values. Sheets– 0 to 89: The top 50 terms and their c-Tf-Idf values for topics 0 to 89.

https://doi.org/10.1371/journal.pbio.3002612.s012

S4 Data. UMAP representations of different topics.

For a topic T , records in the UMAP graph are colored red and records not in T are colored gray.

https://doi.org/10.1371/journal.pbio.3002612.s013

S5 Data. Temporal relationships between published documents projected onto 2D space.

The 2D embedding generated with UMAP was used to plot document relationships for each year. The plots from 1975 to 2020 were compiled into an animation.

https://doi.org/10.1371/journal.pbio.3002612.s014

S6 Data. Timestamps and dates for dynamic topic modeling.

Sheet–bin_timestamp: Columns are: (1) order index; (2) bin_idx–relative positions of bin labels; (3) bin_timestamp–UNIX time in seconds; and (4) bin_date–month/day/year. Sheet–Topic frequency per timestamp: The number of documents in each time bin for each topic. Sheets–LOWESS fit 0.1/0.2/0.3: Topic frequency per timestamp fitted with the fraction parameter of 0.1, 0.2, or 0.3. Sheet—Topic top terms: The top 5 terms for each topic in each time bin.

https://doi.org/10.1371/journal.pbio.3002612.s015

S7 Data. Locally weighted scatterplot smoothing (LOWESS) of topical document frequencies over time.

There are 90 scatter plots, one for each topic, where the x axis is time, and the y axis is the document frequency (blue dots). The LOWESS fit is shown as orange points connected with a green line. The category a topic belongs to and its order in Fig 3 are labeled on the top left corner. The data used for plotting are in S6 Data .

https://doi.org/10.1371/journal.pbio.3002612.s016

S8 Data. The 4 criteria used for sorting topics.

Peak: the time when the LOWESS fit of the frequencies of a topic reaches maximum. 1st_reach_thr: the time when the LOWESS fit first reaches a threshold of 60% maximal frequency (peak value). Trend: upward (1), no change (0), or downward (−1). Stable: whether a topic belongs to the stable category (1) or not (0).

https://doi.org/10.1371/journal.pbio.3002612.s017

S9 Data. Change in taxon record numbers and genome assemblies available over time.

Sheet–Genus: Number of records mentioning a genus during different time periods (in Unix timestamp) for the top 100 genera. Sheet–Genus: Number of records mentioning a family during different time periods (in Unix timestamp) for the top 100 families. Sheet–Genus: Number of records mentioning an order during different time periods (in Unix timestamp) for the top 20 orders. Sheet–Species levels: Number of records mentioning 12 selected taxonomic levels higher than the order level during different time periods (in Unix timestamp). Sheet–Genome assembly: Plant genome assemblies available from NCBI as of October 28, 2022. Sheet–Arabidopsis NSF: Absolute and normalized numbers of US National Science Foundation funded proposals mentioning Arabidopsis in proposal titles and/or abstracts.

https://doi.org/10.1371/journal.pbio.3002612.s018

S10 Data. Taxon topical preference.

Sheet– 5 genera LLR: The log likelihood ratio of each topic in each of the top 5 genera with the highest numbers of plant science records. Sheets– 5 genera: For each genus, the columns are: (1) topic; (2) the Fisher’s exact test p -value (Pvalue); (3–6) numbers of records in topic T and in genus X (n_inT_inX), in T but not in X (n_inT_niX), not in T but in X (n_niT_inX), and not in T and X (n_niT_niX) that were used to construct 2 × 2 tables for the tests; and (7) the log likelihood ratio generated with the 2 × 2 tables. Sheet–corrected p -value: The 4 values for generating LLRs were used to conduct Fisher’s exact test. The p -values obtained for each country were corrected for multiple testing.

https://doi.org/10.1371/journal.pbio.3002612.s019

S11 Data. Impact metrics of countries in different years.

Sheet–country_top25_year_count: number of total publications and publications per year from the top 25 countries with the most plant science records. Sheet—country_top25_year_top17j: number of total publications and publications per year from the top 25 countries with the highest numbers of plant science records in the 17 plant science journals used as positive examples. Sheet–prank: Journal percentile rank scores for countries (3-letter country codes following https://www.iban.com/country-codes ) in different years from 1999 to 2020. Sheet–sjr: Scimago Journal rank scores. Sheet–hidx: H-Index scores. Sheet–cite: Citation scores.

https://doi.org/10.1371/journal.pbio.3002612.s020

S12 Data. Topical enrichment for the top 10 countries with the highest numbers of plant science publications.

Sheet—Log likelihood ratio: For each country C and topic T, it is defined as log((a/b)/(c/d)) where a is the number of papers from C in T, b is the number from C but not in T, c is the number not from C but in T, d is the number not from C and not in T. Sheet: corrected p -value: The 4 values, a, b, c, and d, were used to conduct Fisher’s exact test. The p -values obtained for each country were corrected for multiple testing.

https://doi.org/10.1371/journal.pbio.3002612.s021

S13 Data. Text classification prediction probabilities.

This compressed file contains the PubMed ID (PMID) and the prediction probabilities (y_pred) of testing data with both positive and negative examples (pred_prob_testing), plant science candidate records with the MeSH term “Plants” (pred_prob_candidates_with_mesh), and all plant science candidate records (pred_prob_candidates_all). The prediction probability was generated using the Word2Vec text classification models for distinguishing positive (plant science) and negative (non-plant science) records.

https://doi.org/10.1371/journal.pbio.3002612.s022

Acknowledgments

We thank Maarten Grootendorst for discussions on topic modeling. We also thank Stacey Harmer, Eva Farre, Ning Jiang, and Robert Last for discussion on their respective research fields and input on how to improve this study and Rudiger Simon for the suggestion to examine differences between countries. We also thank Mae Milton, Christina King, Edmond Anderson, Jingyao Tang, Brianna Brown, Kenia Segura Abá, Eleanor Siler, Thilanka Ranaweera, Huan Chen, Rajneesh Singhal, Paulo Izquierdo, Jyothi Kumar, Daniel Shiu, Elliott Shiu, and Wiggler Catt for their good ideas, personal and professional support, collegiality, fun at parties, as well as the trouble they have caused, which helped us improve as researchers, teachers, mentors, and parents.

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Engineered CAR T cells repress signs and symptoms of allergic asthma in mice for a year

by Bob Yirka , Medical Xpress

A team of molecular oncologists at Tsinghua University's State Key Laboratory of Molecular Oncology, in China, has found that engineered, long-lived and multifunctional T cells repress signs and symptoms of allergic asthma in mice for up to a year.

In their study , published in the journal Nature Immunology , the group engineered CAR T cells to reduce the functionality of interleukins associated with type 2 high-signature asthma .

Bart Lambrecht and Hamida Hammad, with the VIB-UGent Center for Inflammation Research, in Belgium, have published a New & Views piece in the same journal issue outlining the work done by the team.

Asthma is a condition in which airways become inflamed, narrow and swollen—as a reaction, the lungs produce excess amounts of mucus, making it difficult to breathe. The condition is typically treated with inhalers that reduce inflammation. Medical workers and patients would prefer a better treatment option. In this new effort, the team in China may have found one.

The researchers focused on reducing symptoms for type 2 high-signature asthma, which is typically associated with interleukin-5-driven eosinophilia, which drives increases in mucus production.

Engineering chimeric antigen receptor (CAR) T cells is most often associated with combating cancer, which is where the workers on this team usually focus their efforts. But they noted that such engineering efforts could likely help with other conditions, such as asthma.

To that end, they engineered CAR T 5T IF and 4T IF cells to make them secrete a IL-4 mutein known to block IL-4 and IL-13 signaling. They then injected the results into mice with induced human-like asthma.

The team then monitored the health of the test mice for up to a year—they found that 5T IF and 4T IF cells persisted in the bodies of the mice and that their presence resulted in continued reductions in asthma symptoms.

Bart N. Lambrecht et al, CAR T cells put the brakes on asthma, Nature Immunology (2024). DOI: 10.1038/s41590-024-01851-8

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