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This study reports that a clinical prognostic model based on immune-related genes improved survival prediction for patients with clear cell renal cell carcinoma, also identifying potential drugs targeting the gene DOCK8.

This report discusses laser hair removal for acne keloidalis nuchae, noting generally favorable outcomes but variability in lesion responsiveness and a need for standardized assessment criteria; it reports no new results.

In this retrospective study, researchers developed machine learning models to predict mortality risk among 7115 COVID-19 patients in Iran, finding that the random forests model performed best with 96% accuracy and identified factors like intubation and SpO2 as significant predictors.

This study found that self-supervised pretraining significantly improves accuracy in medical image classifiers for dermatology and chest X-ray tasks, outperforming supervised baselines and showing robustness to distribution shifts with limited labeled data.

This study developed and tested a new algorithm for analyzing coat and skin coloration in laboratory animals, using digital images and hierarchical color clustering, which effectively quantified color proportions and tracked changes over time without specialized software.

This study found that faster algorithms for inferring ancestry in genomic data can better capture historical and functional insights into genome variation than traditional methods in large datasets like the UK Biobank.

Machine learning is effective in predicting gene functions and their relationships with diseases.

This study developed a new neural network model (AA-GAN-AB-MTEDeep) to enhance Alopecia Areata classification using synthetic scalp images, achieving an accuracy of 96.94%.

This study utilized U-Net's image-processing capabilities to achieve 92% accuracy in segmenting individual hair strands, enhancing early detection and reliable identification of hair fall areas, which assists in addressing challenges of subtle hair thinning that are difficult to see otherwise.

This study suggests that nephronectin (NPNT) could serve as a novel prognostic marker for poor prognosis in a subgroup of breast cancer patients, associated with specific NPNT staining patterns.

This article describes a new instrument, the KD spreader, designed to help novice physicians perform follicular unit extraction hair transplantation with reduced trauma and operator fatigue, but it does not report new clinical results.

This study found that expression and variants of the KRT84 gene are associated with important wool traits in Gansu Alpine Fine-wool sheep, suggesting its potential use as a genetic marker for wool trait selection.

This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.

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.

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 found that a deep learning model can potentially improve the diagnosis and staging of alopecia areata with high accuracy and reliability.

This study reported the development of reliable discriminative models using anthropometric and somatotypological indicators to classify Ukrainian women as typical for healthy individuals or those with urticaria, as well as distinguishing between mild or severe acute urticaria, with high accuracy in most cases.

This study in Gansu alpine fine-wool sheep identified two SNPs in the KRT71 gene that significantly affect wool length, with distinct expression patterns observed in hair follicles, suggesting KRT71 as a candidate gene for enhancing wool production traits.

This study suggests that variations in α-keratin proteins influence the structure and characteristics of wool fibers, indicating that keratin genes could serve as useful markers for identifying different wool traits.

In this study, the XGBoost algorithm successfully diagnosed polycystic ovary syndrome with an accuracy of 0.87 using a dataset from Kerala, suggesting its usefulness for classification problems in healthcare.

This study proposed a method to automatically detect and replace hair in dermoscopy images, demonstrating high sensitivity and specificity in melanoma diagnostics.

This article reviews factors influencing hair growth, with emphasis on androgenetic alopecia, and reports no clinical findings; the authors emphasize the role of androgens and mention treatments like finasteride, dutasteride, and minoxidil.

This study presents the N‑K Healing Series, a geometric approach to medicine that claims to restore tissue geometry, eliminate pain, and regenerate burns more effectively than traditional treatments, with simulations demonstrating significant improvements in healing outcomes and reduced scarring.

This study introduces the N-K Healing Series, an alternative geometric approach to medicine, claimed to restore tissue fully and eliminate pain without the traditional drawbacks, allegedly achieving results beyond mainstream medical treatments in simulated injury scenarios.

This study introduced a deep learning framework combining multiple convolutional neural networks to detect scalp and hair disorders and classify hair fall stages, reporting higher precision and robustness in detection and classification compared to individual CNN models.

In this study, researchers identified novel long non-coding RNAs related to cashmere fineness in goats, highlighting a potential regulatory network involving lncRNA XLOC_008679 and its target gene KRT35.

This study proposes a method for automatically removing hairs from skin lesion images by using edge-tangent flow for hair detection and texture synthesis for restoring occluded regions with minimal artifacts.

In this study, the integration of AI-driven SNP profiling and epigenetic insights in cosmetic dermatology was highlighted as a key development toward personalized skincare, potentially improving treatment effectiveness and reducing side effects.

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 morphometric analysis using horizontal sectioning of scalp biopsies is effective for diagnosing alopecia in Koreans, but follicular count differences from Caucasians should be considered.