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This study reported that genome sequencing and analysis of mink hair keratin genes reveal the amino acid composition of key proteins and offer insights into fur biosynthesis, potentially aiding conservation efforts through transgenic animal design to produce mink fur and help save endangered mink species.

This study developed a deep-learning model that accurately diagnosed alopecia areata with an accuracy of 88.92% and distinguished its activity levels with an accuracy of 83.33%, highlighting the potential for artificial intelligence in improving the diagnosis and treatment of this autoimmune hair loss condition.

This study examined wool traits in Angora rabbits using low-coverage whole genome sequencing, identifying six QTLs and a gene, FGF10, linked to fiber growth and diameter, suggesting a cost-effective approach for complex trait analysis in genomic breeding.

In this study, researchers at Johns Hopkins University found that Keratin 17 plays a signaling role in cell growth during a wound response by aiding mTOR pathway activation, beyond its structural functions.

This study found that a truncated region of the K5 promoter directs expression in stratified epithelia, particularly in epidermis, hair follicles, and tongue, potentially involving specific keratinocyte nuclear proteins in regulation.

This article reviews the classification and complexity of keratin protein groups in hair follicles, but reports no new experimental results on their transcriptional events.

This study suggests that stress intensity is highly correlated with hairfall severity, highlighting the potential of an inexpensive and accessible machine learning approach for forecasting and prevention.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

A deep learning model accurately detects early hair loss signs using scalp images.

In this study, researchers evaluated the binding of keratin-associated proteins and α-keratins to human hair fibers, finding that chemical preactivation significantly enhanced binding to natural hair, while perming altered binding characteristics and increased penetration depth.

This abstract provides a graphic illustrating that mice lacking the Sgk3 gene exhibit thin coats and abnormal hair, suggesting a role for Sgk3 kinase in hair follicle growth, but reports no new experimental findings.

In this study, researchers identified the c.296C>T (p.T99I) variant in the KRT32 gene, which co-segregates with loose anagen hair syndrome, and found it decreases binding affinity to KRT82, potentially weakening hair anchorage.

This article reviews optimal patient selection and donor site quality for successful hair transplant procedures, but reports no new clinical findings.

This article reviews current knowledge on KATP channel drug binding modes through cryogenic electron microscopy, highlighting distinct binding sites in the sulfonylurea receptor and potential mechanisms of drug action, but reports no new experimental results.

This study introduced an innovative metric for assessing college students' mental health, incorporating temporal perception and a hybrid clustering algorithm, and found it achieved over 90% accuracy, outperforming existing methods in evaluating mental health during public health challenges.

This study suggests that human keratin fiber matrix has a well-defined nano-scale grainy structure rather than being amorphous, with grain size influenced by chemical treatments, temperature, humidity, and follicle-level factors.

This article presents an algorithmic approach to diagnosing, staging, and treating pattern hair loss and discusses hair transplant decision-making, without reporting new clinical results.

This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.

This study developed a high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.

In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

This study identifies a polygenic basis for atypical recurrent flank alopecia in Cesky Fousek dogs through genome-wide association analysis and gene expression profiling, highlighting several metabolic pathways involved in the condition.

This study found that reduced cytokinin levels allow plants to adapt to low potassium conditions by enhancing root hair growth, reactive oxygen species accumulation, and expression of a key potassium transporter gene.

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 investigated the genetic factors influencing cashmere quality differences between Jiangnan cashmere goats and Changthangi pashmina goats, identifying 4,942 differentially expressed genes and highlighting 24 key genes related to hair follicle development and cashmere fiber formation.

This study analyzed word dynamics and keyword trends in androgenetic alopecia literature over a decade using natural language processing, revealing hidden patterns and linkages that could inform future research directions and policy decisions.

In this study, full-size KRT81 was expressed in both normal breast epithelial and breast cancer cells, and contributed to the migration and invasion abilities of breast cancer cells.

This study found that intradermal injection of specific oligonucleotides altered hair growth and morphology in mice by inducing a dominant mutation in the K17 gene.

In this pilot study, the Rapid Fire Hair Implanter Carousel facilitated graft placement in hair transplantation by reducing bleeding and operative time compared to the traditional manual method.

This letter reports acquired progressive kinking of hair as a condition observed following COVID-19, but it presents no new scientific findings.