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This study found that rare damaging variants in the KRT82 gene, which affect hair shaft integrity, may contribute to the risk of alopecia areata.

This study used quantitative methods to identify new geometric and mechanical parameters of curly and kinky/coily hair, aiming to improve classification and develop better personal care products for these hair types.

This study introduced a Density-Calibrated Airtouch Protocol (DCAP) that suggests customizing the Airtouch hair coloring technique based on client hair density to optimize color blend, pigment longevity, and hair health.

This research introduces WomenCare, a web-based system using a machine learning model to predict PCOD risk by evaluating factors like age, BMI, and lifestyle habits; it aims to help women monitor their health but is not a substitute for a professional diagnosis.

This study identified a four-gene loop as a non-invasive biomarker that selectively activates in alopecia areata, providing a precise target for JAK inhibitor treatments.

This study reported advances in identifying low-abundance proteins in wool by using fractionation techniques to reduce the dominance of intermediate filament proteins, improving the understanding of the wool proteome.

This study presents a machine learning algorithm that achieved 89.5% accuracy in predicting hair health using factors like spatial-temporal images, age, and gender.

This review detailed the significant progress and challenges in the design and application of PROTACs, a novel class of cancer therapeutics, highlighting their clinical trials, design complexities, and the role of artificial intelligence in improving their efficacy and selectivity for cancer therapy.

A specific hair protein variant increases the spread of breast cancer and is linked to worse survival rates.

This study found that the main structural proteins of tree frog toe pads, which aid in their adhesive properties, are alpha keratins that have evolutionary origins in early tetrapods.

Basal cell carcinoma shows keratin patterns similar to undifferentiated hair follicle cells.

This article reviews the role of Kremen proteins as regulators in the Wnt/β-catenin signaling pathway, highlighting their significance in development and cancer, but reports no new clinical results.

In this study, researchers found that the curliness of human hair is associated with an asymmetric structure at the cellular level, particularly in the differentiation of the precortex.

This study analyzed the molecular evolution and functional divergence of the Dkk gene family, finding accelerated evolution in Aves and Reptilia and identifying functional differences that may impact hair follicle development via Wnt signaling inhibition.

Keratin patterns in hair follicles help understand hair growth and potential hair and nail disorders.

This study characterized the intron-exon organization of human keratin 15 and keratin 19 genes to aid future mutation detection analyses related to potential genetic disorders of keratinization.

This study used proteomic techniques to analyze human hair proteins, revealing keratin heterogeneity and identifying posttranslational modifications, such as cysteine trioxidation and methylation.

In this study, KRTAP28-1 variants were associated with wool fibre diameter in sheep, suggesting potential as a gene marker for reducing fibre diameter.

In this study, researchers used an evolutionary-rate-based method to identify genetic elements associated with reduced hair in mammals, finding a dichotomy between accelerated coding sequences and noncoding regulatory elements influencing hair growth.

This study explored deep learning's potential in diagnosing scalp conditions like alopecia, psoriasis, and folliculitis, using a two-dimensional Convolutional Neural Network, achieving high accuracy and precision despite challenges of a small and uneven dataset.

This study observed that DNA damage response (DDR) plays a key role in pressure overload-induced cardiomyocyte hypertrophy, with disruption of the ATM kinase pathway potentially modulating this hypertrophy in mice.

In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.

This study provides insight into the specific disulfide bond interactions between keratins and keratin associated proteins, suggesting non-random cysteine interactions crucial for stabilizing hair fiber structure.

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 found that certain genes were significantly associated with hair length in Inner Mongolia cashmere goats, potentially serving as molecular markers for different hair types.

This study introduced a multitask learning method to identify shortcut learning in clinical ML systems, revealing it's not always responsible for unfairness and emphasizing the necessity of comprehensive fairness approaches in medical AI.

AI can accurately measure hair loss severity in alopecia areata.

This study suggests that polyamines regulate CK2 enzyme activity and its subcellular distribution, as demonstrated in mouse models and cell cultures with elevated levels of ornithine decarboxylase.