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This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.

This study experimentally validated Lockhart's viscoplastic framework for tip growth in Arabidopsis root hairs by demonstrating alignment with observed growth rates and estimating yield turgor pressure and cell wall viscosity, offering a methodology adaptable to other species and conditions.

This review discusses the data resources and computational models used in drug repositioning, highlighting their role in discovering unknown drug mechanisms and reports no new empirical results.

Keratin peptides can change hair shape gently without harsh chemicals.

This study used computational methods to identify six molecules, with jamogenin being particularly promising, that effectively bind to and inhibit the enzyme linked to male pattern hair loss, suggesting these compounds could be explored further for hair growth potential.

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 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 aims to develop an automatic machine learning-based method using the VGG-19 model to accurately classify various hair and scalp diseases.

This study assessed breeding value estimation methods for Korean Sapsaree dogs, finding varied accuracy across BLUP approaches and identifying significant genomic regions affecting traits like body height and hair length. The researchers suggest these findings can enhance breeding strategies for this culturally significant breed.

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.

This study identified m6A-related genes, particularly IGF2BP3, as significantly up-regulated in keloid patients, potentially implicating them in the condition's molecular mechanisms and suggesting targets for therapy.

This study demonstrates that combining gene expression data with a random forest algorithm provides highly accurate diagnosis of childhood growth hormone deficiency, showing potential utility in distinguishing it from non-GHD short stature.

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 this study, cell membrane remodeling with a thermoresponsive boronic acid copolymer was shown to rapidly form spheroids from cancer or cardiac cell lines under standard conditions, promising advances in tissue engineering.

In this study, applying case-only trees and random forests to a prostate cancer prevention trial revealed genotypes that may influence the efficacy of finasteride for prostate cancer prevention.

This study highlights that hydrogel formulations unexpectedly prolonged healing in trials, pointing to a need for design based on detailed pathophysiological insights rather than empirical methods, and suggests technologies like AI materials optimization and 3D bioprinting to aid their clinical use for cancer survivors.

This study presents computational modeling and experimental analysis of the HGT protein KAP8.1, identifying key structural features that may influence hair's response to environmental conditions.

This study's molecular dynamics simulations suggest that electrostatic interactions mainly stabilize peptide binding to human hair keratin, with the protein's dielectric constant significantly affecting free energy calculations.

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 significant structural and compositional differences in hair affected by alopecia areata compared to healthy hair, using a range of physico-chemical investigation methods.

In this study, researchers introduced a machine learning approach to discover new nanozymes through the DiZyme platform, enabling the accurate prediction of multiple catalytic activities, and providing a comprehensive database and assistant resources for users.

This review examines the current state of automated vitiligo detection systems, noting a lack of large, diverse datasets and consistent imaging conditions, while comparing traditional and modern machine learning approaches to improve reliability and applicability.

In this study, researchers developed de novo designed hetero-bifunctional proteins as an alternative approach for targeted protein degradation, successfully targeting BCL-xL for degradation in cells and inducing apoptosis, which may expand the range of addressable E3 ligases and disease targets.

This study found that a data-driven model using XGBoost effectively predicts individualized responses to minoxidil for androgenetic alopecia, outperforming traditional methods in accuracy and reliability.

This study reviews emerging techniques in acute burn wound therapy, noting innovations such as advanced imaging for better wound assessment and new closure strategies aimed at improving healing, graft survival, and scarring outcomes, while emphasizing the need for further research to validate these methods.

This review outlines the principles of regenerative medicine as applied to cosmetic dermatology, highlighting the need for more data and in vivo trials to standardize methods in this emerging field, while also suggesting future research directions.

This study identified 21 candidate genes related to immunoglobulin concentrations in colostrum and serum of dairy cattle, which may aid in genetic improvement for disease resistance.

This study identified and updated the annotation of 61 keratin genes in dogs and horses, improving the genome annotation in these species through RNA-seq data comparison.

This study investigated transcription factors in human trophectoderm cells during development, finding that GATA2 and GATA3 are essential for transforming stem cells into induced trophoblast stem cells, which display characteristics similar to placental progenitor cells, offering new methods for modeling placental-associated diseases.

This study demonstrated that RNA-seq can effectively expand hair follicle transcriptomic profiling in a multi-center study, offering deeper insights than blood transcriptomics alone.