Background: Both hormonal and genetic data reveal that the stress hormone cortisol and its regulating genes may affect the level of testosterone in humans. It is uncertain whether type 2 diabetes mellitus would manifest similarly. Furthermore, a genetic strategy to screen out the stress system genes that may contribute to testosterone decline in humans is less understood. Objectives: In this study, we aimed to elucidate the link between stress and testosterone levels, both hormonally and genetically. Method: This study comprised 37 individuals with type 2 diabetes mellitus and 50 healthy individuals. For the analysis of hormones and the targeted genes, we used the RIA system and bioinformatics expertise. Results: The patients had significantly elevated cortisol and lower testosterone readings, according to data from hormonal analyses. The bioinformatics approach reveals that SHBG was intracellularly suppressed by 2 defined stress system genes: FKB5 and CYP17. TCF4/TCF8, ATRX, and AR in skeletal muscle were inversely related to stress system genes. Furthermore, all testosterone regulated genes were positively linked with SHBG in the current study. A strong relationship between GNAS and PKA with CYP17 and FKBP5 reveals that the Gαs-cAMP/PKA signaling pathway may be one of the regulatory pathways through which the suppression of testosterone system genes happens. In conclusion, this study demonstrated that beyond stress, the key stress system genes might affect cortisol levels, which in turn affect testosterone figures via the Gαs-cAMP/PKA signaling pathway.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Artificial intelligence is increasingly being integrated into clinical diagnostics, yet its lack of transparency hinders trust and adoption among healthcare professionals. The explainable AI (XAI) has the potential to improve interpretability and reliability of AI-based decisions in clinical practice. Objective: This study evaluates the use of Explainable AI (XAI) for interpreting radiology reports to improve healthcare practitioners’ confidence and comprehension of AI-assisted diagnostics. Methods: This study employed the Indiana University chest X-ray Dataset containing 3169 textual reports and 6471 images. Textual were being classified as either normal or abnormal by using a range of machine learning approaches. This includes traditional machine learning models and ensemble methods, deep learning models (LSTM), and advanced transformer-based language models (GPT-2, T5, LLaMA-2, LLaMA-3.1). For image-based classifications, convolution neural networks (CNNs) including DenseNet121, and DenseNet169 were used. Top performing models were interpreted using Explainable AI (XAI) methods SHAP and LIME to support clinical decision making by enhancing transparency and trust in model predictions. Results: LLaMA-3.1 model achieved highest accuracy of 98% in classifying the textual radiology reports. Statistical analysis confirmed the model robustness, with Cohen’s kappa (k=0.981) indicating near perfect agreement beyond chance, both Chi-Square and Fisher’s Exact test revealing a high significant association between actual and predicted labels (p<0.0001). Although McNemar's Test yielded a non-significant result (p=0.25) suggests balance class performance. While the highest accuracy of 84% was achieved in the analysis of imaging data using the DenseNet169 and DenseNet121 models. To assess explainability, LIME and SHAP were applied to best performing models. These models consistently highlighted the medical related terms such as “opacity”, “consolidation” and “pleural” are clear indication for abnormal finding in textual reports. Conclusions: The research underscores that explainability is an essential component of any AI systems used in diagnostics and helpful in the design and implementation of AI in the healthcare sector. Such approach improves the accuracy of the diagnosis and builds confidence in health workers, who in the future will use explainable AI in clinical settings, particularly in the application of AI explainability for medical purposes.

The present study was the first comprehensive investigation of genetic mutation and expression levels of the p53 signaling genes in cutaneous melanoma through various genetic databases providing large datasets. The mutational landscape of p53 and its signaling genes was higher than expected, with TP53 followed by CDKN2A being the most mutated gene in cutaneous melanoma. Furthermore, the expression analysis showed that TP53, MDM2, CDKN2A, and TP53BP1 were overexpressed, while MDM4 and CDKN2B were under-expressed in cutaneous melanoma. Overall, TCGA data revealed that among all the other p53 signaling proteins, CDKN2A was significantly higher in both sun and non-sun-exposed healthy tissues than in melanoma. Likewise, MDM4 and TP53BP1 expressions were markedly greater in non-sun-exposed healthy tissues compared to other groups. However, CDKN2B expression was higher in the sun-exposed healthy tissues than in other tissues. In addition, various genes were expressed significantly differently among males and females. In addition, CDKN2A was highly expressed in the SK-MEL-30 skin cancer cell line, whereas, Immune cell type expression analysis revealed that the MDM4 was highly expressed in naïve B-cells. Furthermore, all six genes were significantly overexpressed in extraordinarily overweight or obese tumor tissues compared to healthy tissues. MDM2 expression and tumor stage were closely related. There were differences in gene expression across patient age groups and positive nodal status. TP53 showed a positive correlation with B cells, MDM2 with CD8+ T cells, macrophages and neutrophils, and MDM4 with neutrophils. CDKN2A/B had a non-significant correlation with all six types of immune cells. However, TP53BP1 was positively correlated with all five types of immune cells except B cells. Only TP53, MDM2, and CDKN2A had a role in cutaneous melanoma-specific tumor immunity. All TP53 and its regulating genes may be predictive for prognosis. The results of the present study need to be validated through future screening, in vivo, and in vitro studies.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have triggered a recent pandemic of respiratory disease and affected almost every country all over the world. A large amount of natural bioactive compounds are under clinical investigation for various diseases. In particular, marine natural compounds are gaining more attention in the new drug development process. The present study aimed to identify potential marine-derived inhibitors against the target proteins of COVID-19 using a computational approach. Currently, 16 marine clinical-level compounds were selected for computational screening against the 4 SARS-CoV-2 main proteases. Computational screening resulted from the best drug candidates for each target based on the binding affinity scores and amino acid interactions. Among these, five marine-derived compounds, namely, chrysophaentin A (−6.6 kcal/mol), geodisterol sulfates (−6.6 kcal/mol), hymenidin (−6.4 kcal/mol), plinabulin (−6.4 kcal/mol), and tetrodotoxin (−6.3 kcal/mol) expressed minimized binding energy and molecular interactions, such as covalent and hydrophobic interactions, with the SARS CoV-2 main protease. Using molecular dynamic studies, the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (ROG), and hydrogen bond (H-Bond) values were calculated for the SARS-CoV-2 main protease with a hymenidin docked complex. Additionally, in silico drug-likeness and pharmacokinetic property assessments of the compounds demonstrated favorable druggability. These results suggest that marine natural compounds are capable of fighting SARS-CoV-2. Further in vitro and in vivo studies need to be carried out to confirm their inhibitory potential.