Hearing loss poses immense burden worldwide and early detection is crucial. The accurate models identify high-risk groups, enabling timely intervention to improve quality of life. The subtle changes in hearing often go unnoticed, presenting a challenge for early hearing loss detection. While machine learning shows promise, prior studies have not leveraged cardiovascular risk factors known to impact hearing. As hearing outcomes remain challenging to characterize associations, we evaluated a new approach to predict current hearing outcomes through machine learning models using cardiovascular risk factors. The National Health and Nutrition Examination Survey (NHANES) 2012–2018 data comprising audiometric tests and cardiovascular risk factors was utilized. Machine learning algorithms were trained to classify hearing impairment thresholds and predict pure tone average values. Key results showed light gradient boosted machine performing best in classifying mild or greater impairment (> 25 dB HL) with 80.1% accuracy. It also classified > 16 dB HL and > 40 dB HL thresholds, with accuracies exceeding 77% and 86% respectively. The study also found that CatBoost and Gradient Boosting performed well in classifying hearing loss thresholds, with test set accuracies around 0.79 and F1-scores around 0.79–0.80. A multi-layer neural network emerged as the top predictor of pure tone averages, achieving a mean absolute error of just 3.05 dB. Feature analysis identified age, gender, blood pressure and waist circumference as key associated factors. Findings offer a promising direction for a clinically applicable tool, personalized prevention strategies, and calls for prospective validation.

Objectives 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 (LMICs) have been largely under-represented 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, Journal of the American Medical Association, The 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 Application Programming Interface was then used 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 characterised the overall gender and country income category representation across the five medical journals. In addition, we computed article-level diversity metrics and contrasted their distributions across the journals.Results We studied 151 536 authors across 49 764 articles published in five top medical journals, over a 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<0.01) or was affiliated with an institution in a HIC (16.9% vs 15.5%, p<0.01). Our primary finding reveals that having a diverse team promotes further diversity, within the same dimension (ie, 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% vs 10.4% in baseline, p<0.001; based on inferred gender); conversely, papers with at least one LMIC author were more likely to also involve at least two women (49.4% vs 37.6%, p<0.001; 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.

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.

Background: Cognitive impairment poses a growing health challenge as populations age. Heavy metals are implicated as environmental risk factors, but their role is not fully understood. Machine learning can integrate multi-factorial data to predict cognitive outcomes. Objective: To develop and validate machine learning models for early prediction of cognitive impairment risk using demographics, clinical factors, and biomarkers of heavy metal exposure. Method: A retrospective analysis was conducted using 2011-2014 NHANES data. Participants aged >= 20 underwent cognitive testing. Variables included demographics, medical history, lifestyle factors, and blood and urine levels of lead, cadmium, manganese, and other metals. Machine learning algorithms were trained on 90 % of data and evaluated on 10 %. Performance was assessed using metrics like accuracy, AUC, and sensitivity. Result: A final sample of 2,933 participants was analyzed. The stacking ensemble model achieved the best performance with an AUC of 0.778 for test data, sensitivity of 0.879. Important predictors included age, gender, hypertension, education, urinary cadmium and blood manganese levels. Conclusion: Machine learning can effectively predict cognitive impairment risk using comprehensive clinical and exposure data. Incorporating heavy metal biomarkers enhanced prediction and provided insights into environmental contributions to cognitive decline. Prospective studies are needed to validate models over time.

ObjectivesPsychogenic 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.MethodsDifferent 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.ResultsEight 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.SignificanceThis 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.

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.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.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.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.

Introduction Healthcare policies and clinical decisions heavily rely on research publications from high-impact medical journals. A lack of author diversity in medical publications poses a risk to underrepresented groups. To promote equity in healthcare medical decisions, fostering collaborations within research groups is crucial. This study integrates scientometrics with network analysis to uncover intricate co-authorship networks and examine diversity and inclusion in scientific collaboration. Methods The authors’ metadata from five high-impact medical journals were collected, and a weighted graph of co-authorships was constructed. The study addresses four research questions: identifying influential authors, exploring research output communities, analyzing collaboration patterns, and examining the evolution of collaboration over time. Results Central nodes are significantly more likely to be male or from high-income countries. Further, when evaluated over time, the graph reveals concerning trends in diversity where collaboration with authors from lower income countries is not growing. All code is publicly available on GitHub. Discussion The findings underscore the need to promote diversity within research niches and question the role of gatekeepers in facilitating inclusivity. Future studies should expand the scope of network analysis and explore additional factors such as funding sources and guidelines. Conclusion Overall, this study contributes a framework for auditing diversity and inclusion in scientific collaboration, aiming to promote transparency and a more equitable medical knowledge production system. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All code and data produced are available online at

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.

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.