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In this study, researchers developed a computational method called iEdgePathDDA that prioritizes anticancer drug candidates by analyzing changes in gene interactions, demonstrating superior performance compared to existing methods across colorectal, breast, and lung cancer datasets.

This study found that using curated biomedical databases as training examples for information extraction tasks in Genome-Wide Association Studies can outperform cost-insensitive methods, demonstrating their potential use without expert annotation.

This study found that the WNMFDDA model effectively predicts drug-disease associations, achieving high accuracy in cross-validation and confirming most candidate associations through existing databases.

In this study, researchers explored drug repurposing as a potential strategy for treating psoriasis and identified Pioglitazone, Trimipramine, and Dimetindene as promising candidates for this indication, based on molecular docking and predictive algorithms.

This study proposes an AI-based method, DRGCC, using GraphSAGE and clustering constraints to predict associations between drugs and diseases, demonstrating reliable predictive performance that may aid drug repositioning efforts, including exploring drugs for COVID-19 treatment.

This study found that securinine and ajmaline significantly inhibited hepatocellular carcinoma cell viability and induced apoptosis, with securinine showing lower toxicity to normal liver cells.

This study explores how Astragaloside A from traditional Chinese medicine may combat lung adenocarcinoma by regulating key signaling molecules like STAT3 and AKT, using techniques such as network pharmacology and molecular docking to understand its mechanisms in cellular models.

Alopecia areata is linked to immune system differences, with specific biomarkers like CXCL9 and CXCL10 being key for diagnosis and potential treatment targets.

This review characterizes breast implant illness and its autoimmune symptoms, reporting an association between breast implants and autoimmune diseases, with explantation relieving symptoms in over half of patients.

This case study suggests that obstructive sleep apnea syndrome may be hereditary in patients with connective tissue disorders due to a variant in the COL1A2 gene.

This study found a significant positive association between higher air quality index values and the incidence of alopecia areata, with a clear dose-response relationship observed among the participants.

This study identified key genes linked to immune cells and potential therapeutic compounds for alopecia areata by evaluating upregulated genes from patient datasets, highlighting T and NK cell involvement in hair follicle attack and suggesting drug candidates through molecular docking and dynamics simulations.

This study found that Punica granatum ethanolic leaf extract may alleviate skin fibrosis in rats by modulating inflammation-related pathways and reducing dermal alterations.

This study suggests that a position effect disrupting TRPS1 expression may be linked to hypertrichosis in both Ambras syndrome in humans and a similar phenotype in Koa mice.

This study found dependencies between genetic variants and various phenotypes related to fetal and early childhood growth and neurological development in healthy infants, suggesting significant gene candidates for further investigation.

This study found that long non-coding RNAs are differentially expressed in androgenetic alopecia, suggesting potential roles in its development and novel targets for prevention and treatment.

This review examines the impact of biotin, vitamin B12, and zinc on male reproduction, emphasizing their role in spermatogenic failure, but reports no new clinical results.

This article discusses the limitations of relying on literature and regulatory documents for pharmaceutical evidence, proposing public health decisions be based on independent trials like those by the Mario Negri Institute.

This study found that intrinsic feedback loops are crucial for maintaining the function of human dermal papilla cells during hair growth, highlighting the importance of autocrine signaling and niche reprogramming for advancing hair follicle biology and regenerative therapies for hair loss.

This review discusses the current understanding of the epidemiology, pathogenesis, clinical manifestations, and diagnosis of female pattern hair loss and reports no new research results.

In this study, researchers conducted a large genome-wide association meta-analysis and found 30 significant genetic loci linked to the risk of dermatophytosis, shedding light on the roles of keratin biology, skin barrier defects, immune dysfunction, and obesity in the disease.

This study identified two key epigenetic-related genes, HR and SMYD4, which may act as potential biomarkers in keloid disease, suggesting new therapeutic avenues for further research.

This article reviews strategies for serological testing in diagnosing ILD linked to systemic rheumatic diseases and introduces a potential diagnostic algorithm for pulmonologists, while noting further validation is needed.

This study found that the current approval processes by major regulators like the EMA and FDA for HPV vaccines may rely on only half of the available trial data.

This study reported that the coexistence of myasthenia gravis and primary Sjögren's syndrome is rare, and managing the progress of myasthenia gravis is crucial for treatment, while it does not adversely impact Sjögren's syndrome.

This study identified three hub genes—BMP4, POSTN, and WNT5A—that are closely associated with keloid fibroblast hyperplasia, suggesting they may serve as potential biomarkers for inhibiting this condition. Further research is necessary to fully understand their roles in keloid development.

In this bibliometric study, researchers analyzed 96 publications from 2000 to 2022 to identify trends in HIV-1 capsid inhibitor research, highlighting the USA as a leading contributor and emphasizing the importance of molecular studies and drug discovery in advancing antiretroviral therapy.

This study developed a deep learning-based method that identified 3,620,516 potential drug-disease associations, suggesting a promising tool for large-scale virtual screening in drug research.

This study reported that the Computational Analysis of Novel Drug Opportunities platform effectively uses integrated biological data, including side effects and pathways, to generate potential drug candidates for colon cancer and migraine disorders.

This study identified 12 potential candidate biomarkers associated with hair loss by analyzing the differential expression of genes, suggesting possible therapeutic targets for alopecia.