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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.

This study reports that the ScalpEye system accurately diagnosed dandruff, folliculitis, hair loss, and oily hair with a precision range of 97.41% to 99.09%.

In this study, researchers evaluated deep learning methods for diagnosing Alopecia Areata and found that a modified Inception-Resnet-v2 model achieved a high validation accuracy of 97.94% and loss of 10.4%, suggesting it as an effective tool for classifying alopecia-affected hair.

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 found that a specific fragment of AIMP1, secreted by hair follicle stem cells, activated dermal papilla cells, leading to enhanced hair regrowth and maintenance in mice.

This study measured the amounts of thiamine and riboflavin in Bogura's Geographical Indication branded yogurt and whey, finding them rich in these vitamins, which are beneficial for health.

In this study, researchers developed a method to create a synthetic dataset of facial acne images using generative techniques, achieving 97.6% classification accuracy with InceptionResNetv2, which helps overcome privacy concerns in biomedical applications by using anonymized data.

Martin-Perez et al. show that rapamycin's beneficial effects in a mouse model of Leigh syndrome are linked to the downregulation of protein kinase C.

This study found that finasteride affected ethanol withdrawal severity differently in WSP and WSR lines, with more modulation seen in the WSP line.

This review highlights current and future AI applications in hair restoration and diagnosis of hair disorders, including automated systems for hair detection and self-diagnosis, emphasizing the need for experts to understand their benefits and limitations.

This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

This review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

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 introduced a single-stage network using large kernel attention that effectively restores high-resolution images by capturing both global and local details, reducing parameters and improving processing speed.

This study observed that insurance coverage for oral tofacitinib in alopecia areata patients was difficult to obtain, with 71% of patients ultimately not receiving approval despite the drug's efficacy as an off-label treatment.

This study found that trilostane was effective in resolving symptoms such as polyuria and polydipsia in many dogs with pituitary-dependent hyperadrenocorticism, with significant hormone level reductions observed.

This review examines the evolving field of nanozymes, highlighting their unique properties and biocatalytic capabilities that challenge traditional concepts of biocatalysis, with implications for sustainable applications and potential roles in physiological processes and disease pathogenesis.

This study investigated the effects of the herbal formulation DA-5512 on hair growth, finding that it significantly promotes dermal papilla cell proliferation and stimulates hair growth in mice while showing efficacy and safety in human trials compared to placebo and minoxidil (3%).

In this study, the ChatGPT-5 model accurately answered 76.47% of questions on the Polish endocrinology specialty exam, surpassing the 60% passing threshold, yet its role in clinical decision-making remains uncertain, highlighting the need for further research.

This review discusses recent advancements in nanotechnology for treating hypertrophic scarring and atopic dermatitis, highlighting the potential of nanotherapeutic platforms in enhancing therapeutic effects by modulating the skin's microenvironment.

This study reported the expression patterns and potential functions of long non-coding RNAs in the hair follicle cycle of yak, with insights into their sequence conservation between yak and cashmere goat.

This study introduced a semi-automatic hair follicle implanter that can reduce surgery time and fatigue for both patients and surgeons, with effectiveness demonstrated in animal and clinical trials.

This review examines current understanding of how skin microbiota interact with and influence wound healing, highlighting the roles of commensal microorganisms in inflammation resolution, tissue regeneration, and barrier restoration, and discusses potential microbiota-based therapies for improving wound management.

In this study, researchers developed a machine learning model that accurately diagnosed scalp conditions like fine dandruff and perifollicular erythema with 75% and 82% accuracy, respectively, and created a user-friendly web platform for scalp health self-assessment, which achieved high user satisfaction.

This study evaluated a novel, user-friendly approach for detecting hairfall trends over time using machine learning models. The Temporal Fusion Transformer model demonstrated high accuracy in identifying anomalies in hair shedding patterns, potentially aiding in the early detection of health risks related to hormonal fluctuations.

This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.

This study found that COL17 deficiency in neonatal mice causes abnormal skin cell proliferation due to disrupted Wnt signaling, while replenishing or overexpressing COL17 can reverse this effect in both neonatal and aged skin.

This study identified thousands of variant-metabolite associations in the human plasma metabolome, offering insights into the genetic bases of metabolism and potential adverse drug effects.

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.