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This study found that livedoid and acro-ischemic skin manifestations were significantly associated with severe COVID-19, while chilblains-like eruptions appeared in less severe cases.

This study found that dandruff-afflicted individuals had a less integrated microbial network on the scalp and hair surface compared to healthy individuals, with more positive interactions and unstable connections.

In this study, researchers used computer-generated models to demonstrate that subtle changes in hair diameter, density, and style significantly affect perceptions of age, health, and attractiveness.

This study found that non-blinded assessors in randomized clinical trials exaggerated the effects of experimental interventions by about 29% compared to blinded assessors, indicating a significant potential for observer bias in treatment evaluations.

This study found that peroxide-rich plasma-activated water caused minimal morphological damage to hair, while nitrate-rich plasma-activated water induced significant damage, especially in porous hair types.

This study reported that Tianma Gouteng decoction improved hair follicle regeneration in mice and identified its potential mechanism, primarily involving the Wnt/β-catenin signaling pathway, through a combination of network pharmacology, molecular docking, and experimental validation.

This study highlighted the effectiveness of HT-scCAT-seq as a tool for understanding gene regulation in single cells, offering insights into embryonic skin development and proposing a framework for exploring regulatory mechanisms in various biological and disease contexts.

This chapter reviews various mucocutaneous manifestations of endocrine disorders and provides illustrative images of these clinical features, but reports no new research findings.

This review synthesizes data suggesting that managing inflammation and oxidative stress may enhance the effectiveness of PRP therapy for hair regeneration, though standardizing PRP protocols remains challenging.

This chapter reviews the anatomy and structure of hair, discusses hair damage and analyses, and explores methods for processing hair changes, but presents no new research findings.

This review suggests that integrating artificial intelligence in diabetic foot management could significantly improve early diagnosis and wound healing by facilitating thermographic analysis and developing "smart dressings," though challenges in training, legal, and ethical aspects remain.

This study found that Follicular Maps, derived from trichoscopic images, remained consistent over time and unaffected by hair cycling or noncicatricial alopecia, offering a precise tool for diagnosing and monitoring hair and scalp conditions.

This study identified key trends and influential contributors in skin microbiome research from the past decade, highlighting the growing publication rates, major players like the USA, and prominent researchers such as Gallo RL.

In this study, a Monte Carlo-generated synthetic cohort analysis found that higher simulated cannabis exposure was linked to increased self-assessed hair loss severity and reduced follicular density, suggesting potential connections worth exploring in further clinical research.

This study used in vivo confocal microscopy and a ResNet34 deep learning model to classify meibomian gland images with an AUROC greater than 0.95, indicating its potential for automatic diagnosis and screening of meibomian gland dysfunction.

This study quantified relationships among various occupations in Japan, enabling comparisons with vocational aptitude test results using new numerical criteria developed from analysis of over three million survey panels.

In this study, grape seed oil was found to be the most effective herbal oil for improving gloss and reducing fractures in various hair types.

This study analyzed the economic burden of alopecia areata in Saudi Arabia, finding significant costs driven by productivity loss and drug expenses, with severe cases incurring higher expenses, emphasizing the need for better treatment protocols and access to therapies.

This study examined postmenopausal hyperandrogenism related to ovarian disease and found that specific biochemical markers can help differentiate between tumorous and non-tumorous cases, with a good level of sensitivity and specificity, supporting surgical intervention for resolution.

This study introduces a five-modal microscopy technique that allows label-free, in vivo observation and quantitative analysis of various wound healing processes.

This review discusses the growing role of colorimetric sensors in food safety, highlighting their potential to enhance detection accuracy and manageability, while also noting challenges such as environmental interference and the need for further industrial validation.

This case study highlights the novel occurrence of a genian fistula with intraoral hair follicle inclusion in a patient without prior facial surgery, emphasizing the need for a comprehensive approach combining clinical, radiologic, and histopathologic analysis for accurate diagnosis and management.

This study found that certain pyrazole derivatives may serve as effective 5α-reductase inhibitors with potential use in the treatment of androgenetic alopecia, based on molecular docking analyses.

In this study, nanoemulsion formulations were found to significantly enhance the skin retention of the antiseptic agent triclosan, with the optimal mixture being a low surfactant concentration, as demonstrated through quantitative and qualitative analysis using tape-stripping and micro CT scans.

This review examines host factors like ACE2 and TMPRSS2 in SARS-CoV-2 infection, exploring how genetic variants and advanced cellular analyses might clarify COVID-19's severity and heterogeneity; it reports no new results.

This study's simulation found that Ascorbyl Tetraisopalmitate penetrates more efficiently through the skin's stratum corneum than via the infundibulum, following different pathways and dynamics in each membrane.

This study describes the PandaOmics platform, which uses AI and bioinformatics to identify new therapeutic targets and biomarkers for various diseases, demonstrating validation in laboratory and animal studies.

This study proposed and tested an Ensemble Pre-Learned Deep Learning and Optimized Long Short-Term Memory (EPL-OLSTM) model for classifying Alopecia Areata, achieving a 93.1% accuracy in differentiating healthy from varying severity levels of AA scalp hair using specific datasets.

This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

This study developed a deep learning-based tool to quantify individual hair fibers in mice, revealing distinct hair phenotypes linked to hormonal, genetic, and age-related factors, and suggesting its potential for new diagnostic methods through hair analysis.