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This study employed multiple linear regressions to analyze hydantoin analogues and produced a model with strong predictive abilities for designing new androgen receptor modulators.

In this study, the researchers performed a comparative analysis of drug-target interaction data from multiple databases to refine drug repurposing strategies, revealing potential associations between drug characteristics and therapeutic groups, and predicting repositioning opportunities for FDA-approved drugs across major cancer types.

This review identifies 1859 plant and fungal species from the Pantanal and highlights patents, mainly from South Korea, focused on extracts with antitumoral, anti-inflammatory, and cosmetic applications, but reports no new clinical results.

This study identified 14 genes affected by copy number variants that may contribute to alopecia areata, including four genes notably involved in autophagy and chromatin remodeling.

In this case-control study, researchers did not find genome-wide significant genetic variants for trichotillomania, but cases had higher polygenic risk for psychiatric disorders and certain neuropsychiatric-associated copy number variants.

This study conducted a meta-analysis of gene expression data from alopecia areata patients, identifying 5109 differentially expressed genes and highlighting enriched pathways like JAK-STAT signaling, providing insights into the disease's pathogenesis and potential treatment targets.

This study reported that the PM-DBiGRU model enhances aspect-level sentiment classification in drug reviews, outperforming existing methods on the newly proposed SentiDrugs dataset.

This study found that cardiac fibroblasts significantly enhance the maturation of neonatal mouse and human embryonic stem cell-derived cardiomyocytes, suggesting a pivotal role in postnatal heart development and disease.

In this study, researchers observed that African spiny mice, Acomys cahirinus, are capable of regenerating skeletal muscle in dermal wound sites, unlike common mice, Mus musculus.

This review discusses 'vibe coding', a new approach that allows clinicians with minimal coding skills to utilize machine learning tools for medical research by using natural language directives to generate and refine code through AI-driven platforms.

This review summarizes the application of genome wide association studies and selection signature analyses in sheep and goat breeding in China, highlighting genomic regions that influence traits like reproductive performance and body size.

This study analyzed online patient reviews for abdominoplasty and found significant themes affecting satisfaction, highlighting that higher ratings were strongly associated with positive aesthetic outcomes, staff interactions, and postoperative care.

In this study, researchers used EHR data from two large PCORnet networks to identify a range of long COVID diagnoses, highlighting varying post-acute risks across populations in NYC and Florida.

This study analyzed pexidartinib-associated adverse events from FDA data and reported common events such as hepatic issues and systemic reactions. It highlighted sex-specific susceptibilities and reinforced the need for risk mitigation and long-term monitoring in tenosynovial giant cell tumor management.

This study used a zebrafish model and advanced mass spectrometry to identify 11 metabolites of epristeride, revealing significant effects on purine metabolism and aromatic amino acid biosynthesis, which may aid in developing anti-doping detection methods.

This study used in-silico analyses to investigate papain's potential as a fibrinolytic agent and found that certain fibrin peptides displayed strong interactions with papain, suggesting it may be beneficial for cardiovascular disease treatment.

In this study, researchers observed that dietary vitamin D intake and light exposure play a crucial role in vitamin D metabolism in mice, suggesting similar importance for human health in maintaining optimal vitamin D levels.

In this bibliometric analysis, researchers observed that precision medicine approaches, such as individualized interventions and biomarker-guided subtyping, are increasingly integrated into prediabetes research, highlighting shifts toward using multi-omics data and artificial intelligence for patient stratification and prevention strategies.

This study suggests that estimating autism likelihood as early as one month after birth may enable more precise early intervention for children with developmental support needs, potentially improving diagnosis, workflows, and reducing service wait times.

In a comparative genomic study, researchers observed significant genetic differences among the three chemical races of the alga Botryococcus braunii, leading them to propose reclassifying these races as distinct species based on their unique genomic characteristics.

This study investigated the phytochemicals in Phyla nodiflora leaf extracts and identified n-Hexadecanoic acid, stigmasterol, and beta-sitosterol as promising drug candidates for diabetes, alopecia, cancer, and anti-diuresis, advocating further research into these compounds.

This study systematically reviewed meta-analyses and Mendelian randomization studies to identify factors influencing prostate cancer risk, finding suggestive associations for physical activity, height, and smoking but no robust, convincing overlapping evidence across tested factors.

This study found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.

This study found that low-level laser therapy was the most effective non-surgical treatment for androgenetic alopecia, although the overall quality of evidence supporting various therapies was generally low.

This review found that among emerging technologies for skin anti-aging, stem cell therapies using recipient chimerism might be more beneficial than CRISPR-Cas9 and other current innovations, though further studies on safety and efficacy are needed.

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

This study found that metabolism-related genes significantly impact the prognosis and metastasis in breast cancer, and the development of prediction models may guide personalized therapeutic strategies.

This Mendelian randomization study suggests possible bidirectional causal links between alopecia and sleep characteristics, but findings require cautious interpretation due to multiple testing, highlighting the need for further research to explore underlying mechanisms and broader applicability.

In this study using the C3H/HeJ mouse model of alopecia areata, researchers found that hyperexpanded CD8⁺ T cell clones were sufficient to initiate disease, establishing a causal link between T cell clonality and pathogenicity.

This study introduces Cell Journey, a new platform for visualizing RNA velocity in 3D, which aims to better capture complex cellular transitions in single-cell datasets compared to current 2D methods.