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This study found that specific DNA variants in the TCHH, WNT10A, and FRAS1 genes are associated with predicting straight hair in Europeans, showing high sensitivity but low specificity, especially using a neural networks approach.

This study found that acanthosis nigricans in the knuckles correlates with higher insulin and HOMA-IR values, potentially serving as an early, accessible marker for insulin resistance even without excess weight.

This study seeks to clarify the terminology and classification of keratoacanthoma and similar squamous cell carcinoma lesions with central keratin-filled craters to aid in accurate differential diagnosis.

This preliminary study suggests that Raman micro-spectroscopy can help differentiate basal cell carcinoma from hair follicles in skin tissue sections, although some misclassification of hair follicles as carcinoma was observed.

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.

In this study, researchers used transcriptome sequencing to identify various noncoding RNAs in the skin tissues of Jiangnan cashmere goats and found that certain long noncoding RNAs may play a role in regulating cashmere fiber fineness, offering new insights for breeding programs.

A new computer tool quickly measures hair thickness differences in people with common types of hair loss.

In this study, the DIET-AI model, developed from a large dataset of over 200,000 images, demonstrated diagnostic performance for 31 skin diseases comparable to dermatologists of varying experience levels in 15 hospitals across China, supporting its potential effectiveness in clinical settings.

In this study, a machine learning framework incorporating the CatBoost algorithm accurately predicted Systemic Lupus Erythematosus in Omani patients, suggesting potential for early clinical intervention.

In this study, researchers formulated a nanosuspension of kenikir leaf extract that meets physical criteria and exhibits cytotoxic activity against MCF-7 breast cancer cells.

This study found that dermoscopy is valuable for diagnosing facial actinic keratosis in Chinese patients, with specific patterns like red pseudonetwork and follicular plugs suggesting its utility in differential diagnosis.

This study identified and sequenced several keratin cDNA clones showing distinct expression patterns in mouse epithelia, with in situ hybridization highlighting differences in keratin distribution between normal and hyperproliferative tissues.

This study revealed specific patterns and potential functions of long non-coding RNAs during the hair follicle cycle of yaks, offering insights into their sequence conservation between yaks and cashmere goats.

In this study, the researchers reported that their novel algorithm for hair restoration in dermoscopy images achieved high accuracy and texture preservation, outperforming other techniques in diagnostic accuracy and texture quality measures.

This study presents a new classification of adult human scalp hair patterns based on high-resolution photographs, which may assist hair restoration surgeons and dermatologists in determining appropriate punch size for follicular unit extraction.

This study observed that disrupting the Pnkp gene in adult mice resulted in a premature aging-like phenotype, suggesting PNKP's vital role in maintaining normal growth and survival of certain progenitor cell populations.

In this study, the researchers successfully cloned and sequenced the hairless gene cDNA of Kunming mice, revealing high conservation and functional significance among various mammalian species.

This study found that patients with alopecia areata have distinct mRNA and lncRNA expression profiles between normal and bald scalp areas, identifying differentially expressed genes and revealing potential biomarkers for diagnosis, with keratin family genes possibly playing a key role in the disease's pathogenesis.

In this study, a novel geometric medicine approach, utilizing the N-K Universal Computer, was reported to eliminate aflatoxin contamination and significantly reduce cancer rates across the "Aflatoxin Belt" through precision targeting of harmful fungi while preserving ecological balance, without observed toxicity or resistance.

This study found that Raman spectroscopy has potential for detecting biophysical differences in basal cell carcinoma compared to normal skin structures, supporting its future use in Mohs surgery.

This study reports that publicly available large language models are currently less accurate than human experts in diagnosing trichoscopic images, suggesting the need for further development and specialized training for these AI tools in trichology.

This study suggests that traditional classification systems for pattern hair loss, while useful in the past, have limitations in accuracy and reproducibility, and highlights the potential of integrating digital imaging and AI to create more precise and biologically informed classification frameworks.

This study established consensus criteria for classifying alopecia areata severity, aiding clinicians in accurate diagnosis and treatment planning.

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 developed new genetic prediction models for male pattern baldness with improved accuracy by utilizing a large set of markers and independent datasets, making them the most reliable available for this trait.

This study examined the role of long non-coding RNAs in wool fineness among Gansu alpine fine-wool sheep, identifying specific lncRNAs and target genes that may enhance wool quality.