Does diversity beget diversity? A scientometric analysis of over 150,000 studies and 49,000 authors published in high-impact medical journals between 2007 and 2022

Background

Health research that significantly impacts global clinical practice and policy is often published in high-impact factor (IF) medical journals. These outlets play a pivotal role in the worldwide dissemination of novel medical knowledge. However, researchers identifying as women and those affiliated with institutions in low- and middle-income countries (LMIC) have been largely underrepresented in high-IF journals across multiple fields of medicine. To evaluate disparities in gender and geographical representation among authors who have published in any of five top general medical journals, we conducted scientometric analyses using a large-scale dataset extracted from the New England Journal of Medicine (NEJM), Journal of the American Medical Association (JAMA), The British Medical Journal (BMJ), The Lancet, and Nature Medicine.

Methods

Author metadata from all articles published in the selected journals between 2007 and 2022 were collected using the DimensionsAI platform. The Genderize.io API was then utilized to infer each author's likely gender based on their extracted first name. The World Bank country classification was used to map countries associated with researcher affiliations to the LMIC or the high-income country (HIC) category. We characterized the overall gender and country income category representation across the medical journals. In addition, we computed article-level diversity metrics and contrasted their distributions across the journals.

Findings

We studied 151,536 authors across 49,764 articles published in five top medical journals, over a long period spanning 15 years. On average, approximately one-third (33.1%) of the authors of a given paper were inferred to be women; this result was consistent across the journals we studied. Further, 86.6% of the teams were exclusively composed of HIC authors; in contrast, only 3.9% were exclusively composed of LMIC authors. The probability of serving as the first or last author was significantly higher if the author was inferred to be a man (18.1% vs 16.8%, P < .01) or was affiliated with an institution in a HIC (16.9% vs 15.5%, P < .01). Our primary finding reveals that having a diverse team promotes further diversity, within the same dimension (i.e., gender or geography) and across dimensions. Notably, papers with at least one woman among the authors were more likely to also involve at least two LMIC authors (11.7% versus 10.4% in baseline, P < .001; based on inferred gender); conversely, papers with at least one LMIC author were more likely to also involve at least two women (49.4% versus 37.6%, P < .001; based on inferred gender).

Conclusion

We provide a scientometric framework to assess authorship diversity. Our research suggests that the inclusiveness of high-impact medical journals is limited in terms of both gender and geography. We advocate for medical journals to adopt policies and practices that promote greater diversity and collaborative research. In addition, our findings offer a first step towards understanding the composition of teams conducting medical research globally and an opportunity for individual authors to reflect on their own collaborative research practices and possibilities to cultivate more diverse partnerships in their work.

A systems biology approach for discovering the cellular and molecular aspects of psychogenic non-epileptic seizure

Objectives

Psychogenic non-epileptic seizure (PNES) is the most common non-epileptic disorder in patients referring to epilepsy centers. Contrary to common beliefs about the disease's harmlessness, the death rate of PNES patients is similar to drug-resistant epilepsy. Meanwhile, the molecular pathomechanism of PNES is unknown with very limited related research. Thus, the aim of this in silico study was to find different proteins and hormones associated with PNES via a systems biology approach.

Methods

Different bioinformatics databases and literature review were used to find proteins associated with PNES. The protein-hormone interaction network of PNES was constructed to discover its most influential compartments. The pathways associated with PNES pathomechanism were found by enrichment analysis of the identified proteins. Besides, the relationship between PNES-related molecules and psychiatric diseases was discovered, and the brain regions that could express altered levels of blood proteins were discovered.

Results

Eight genes and three hormones were found associated with PNES through the review process. Proopiomelanocortin (POMC), neuropeptide Y (NPY), cortisol, norepinephrine, and brain-derived neurotrophic factor (BDNF) were identified to have a high impact on the disease pathogenesis network. Moreover, activation of Janus kinase-signaling transducer and activator of transcription (JAK-STAT) and JAK, as well as signaling of growth hormone receptor, phosphatidylinositol 3-kinase /protein kinase B (PI3K/AKT), and neurotrophin were found associated with PNES molecular mechanism. Several psychiatric diseases such as depression, schizophrenia, and alcohol-related disorders were shown to be associated with PNES predominantly through signaling molecules.

Significance

This study was the first to gather the biochemicals associated with PNES. Multiple components and pathways and several psychiatric diseases associated with PNES, and some brain regions that could be altered during PNES were suggested, which should be confirmed in further studies. Altogether, these findings could be used in future molecular research on PNES patients.

FKBP5 blockade may provide a new horizon for the treatment of stress-associated disorders; an in-silico study

OBJECTIVE

We searched for, from the FDA (Food and Drug Administration-USA)-approved drugs, inhibitors of FKBP5 with tolerable adverse effect profiles (e.g., mild headache, sedation, etc.) and with the ability to cross the blood brain barrier (BBB), using bio-informatics tools (in-silico). This may pave the road for designing clinical trials of such drugs in patients with functional seizures (FS) and other stress-associated disorders.

METHODS

Several databases were used to find all the approved drugs that potentially have interactions with FKBP51 protein [i.e., CTD gene-chemical interaction section of FKBP51 protein of Harmonizome of Mayaanlab, DrugCenteral database, PDID (Protein Drug Interaction Database), DGIdb (the Drug Gene Interaction database)]. Other databases were also searched [e.g., clinicaltrials.gov; DRUGBANK (the FASTA format of the FKBP51 protein was imported to the target sequencing section of the database to find the associated drugs), and the STITCH database (to find the related chemical interaction molecules)].

RESULTS

After a comprehensive search of the designated databases, 28 unique and approved drugs were identified. Fluticasone propionate and Mifepristone and Ponatinib, Mirtazapine, Clozapine, Enzalutamide, Sertraline, Prednisolone, Fluoxetine, Dexamethasone, Clomipramine, Duloxetine, Citalopram, Chlorpromazine, Nefazodone, and Escitalopram are inhibitors of FKBP5 and have BBB permeability.

SIGNIFICANCE

While the current in-silico repurposing study could identify potential drugs (that are already approved and are widely available) for designing clinical trials in patients with stress-associated disorders (e.g., FS), any future clinical trial should consider the pharmacological profile of the desired drug and also the characteristics and comorbidities of the patients in order to foster a success.

The genetic link between systemic autoimmune disorders and temporal lobe epilepsy: A bioinformatics study

OBJECTIVE

We aimed to explore the underlying pathomechanisms of the comorbidity between three common systemic autoimmune disorders (SADs) [i.e., insulin-dependent diabetes mellitus (IDDM), systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA)] and temporal lobe epilepsy (TLE), using bioinformatics tools. We hypothesized that there are shared genetic variations among these four conditions.

METHODS

Different databases (DisGeNET, Harmonizome, and Enrichr) were searched to find TLE-associated genes with variants; their single nucleotide polymorphisms (SNPs) were gathered from the literature. We also did a separate literature search using PubMed with the following keywords for original articles: "TLE" or "Temporal lobe epilepsy" AND "genetic variation," "single nucleotide polymorphism," "SNP," or "genetic polymorphism." In the next step, the SNPs associated with TLE were searched in the LitVar database to find the shared gene variations with RA, SLE, and IDDM.

RESULTS

Ninety unique SNPs were identified to be associated with TLE. LitVar search identified two SNPs that were shared between TLE and all three SADs (i.e., IDDM, SLE, and RA). The first SNP was rs16944 on the Interleukin-1β (IL-1β) gene. The second genetic variation was ε4 variation of apolipoprotein E (APOE) gene.

SIGNIFICANCE

The shared genetic variations (i.e., rs16944 on the IL-1β gene and ε4 variation of the APOE gene) may explain the underlying pathomechanisms of the comorbidity between three common SADs (i.e., IDDM, SLE, and RA) and TLE. Exploring such shared genetic variations may help find targeted therapies for patients with TLE, especially those with drug-resistant seizures who also have comorbid SADs.

Identification of hub pathways and drug candidates in gastric cancer through systems biology

Gastric cancer is the fourth cause of cancer death globally, and gastric adenocarcinoma is its most common type. Efforts for the treatment of gastric cancer have increased its median survival rate by only seven months. Due to the relatively low response of gastric cancer to surgery and adjuvant therapy, as well as the complex role of risk factors in its incidences, such as protein-pomp inhibitors (PPIs) and viral and bacterial infections, we aimed to study the pathological pathways involved in gastric cancer development and investigate possible medications by systems biology and bioinformatics tools. In this study, the protein-protein interaction network was analyzed based on microarray data, and possible effective compounds were discovered. Non-coding RNA versus coding RNA interaction network and gene-disease network were also reconstructed to better understand the underlying mechanisms. It was found that compounds such as amiloride, imatinib, omeprazole, troglitazone, pantoprazole, and fostamatinib might be effective in gastric cancer treatment. In a gene-disease network, it was indicated that diseases such as liver carcinoma, breast carcinoma, liver fibrosis, prostate cancer, ovarian carcinoma, and lung cancer were correlated with gastric adenocarcinoma through specific genes, including hgf, mt2a, mmp2, fbn1, col1a1, and col1a2. It was shown that signaling pathways such as cell cycle, cell division, and extracellular matrix organization were overexpressed, while digestion and ion transport pathways were underexpressed. Based on a multilevel systems biology analysis, hub genes in gastric adenocarcinoma showed participation in the pathways such as focal adhesion, platelet activation, gastric acid secretion, HPV infection, and cell cycle. PPIs are hypothesized to have a therapeutic effect on patients with gastric cancer. Fostamatinib seems a potential therapeutic drug in gastric cancer due to its inhibitory effect on two survival genes. However, these findings should be confirmed through experimental investigations.

Machine learning applications to differentiate comorbid functional seizures and epilepsy from pure functional seizures

PURPOSE

We have utilized different methods in machine learning (ML) to develop the best algorithm to differentiate comorbid functional seizures (FS) and epilepsy from those who have pure FS.

METHODS

This was a retrospective study of an electronic database of patients with seizures. All patients with a diagnosis of FS (with or without comorbid epilepsy) were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2021. We arbitrarily selected 14 features that are important in making the diagnosis of patients with seizures and also are easily obtainable during history taking. Pytorch and Scikit-learn packages were used to construct various models including random forest classifier, decision tree classifier, support vector classifier, k-nearest neighbor, and TabNet classifier.

RESULTS

Three hundred and two patients had FS (82.5%), while 64 patients had FS and comorbid epilepsy (17.5%). The "TabNet classifier" could provide the best sensitivity (90%) and specificity (74%) measures (accuracy of 76%) to help differentiate patients with FS from those with FS and comorbid epilepsy.

CONCLUSION

These satisfactory differentiating measures suggest that the current algorithm could be used in clinical practice to help with the difficult task of distinguishing patients with FS from those with FS and comorbid epilepsy. Based on the results of the current study, we have developed an Application (SeiDx). This App is freely accessible at the following address: https://drive.google.com/file/d/1rAgBXKNPW9bmUCDioaGHHzLBQgzZ-HZ2/view. This App should be validated in a prospective assessment.