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This study identified specific genetic markers related to wool quality in Rambouillet sheep, which may aid breeders in making informed selection and breeding decisions for improved fine wool production.

This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.

This review summarizes how genetic studies using conditional β-catenin loss- and gain-of-function mice have advanced understanding of canonical Wnt signaling's role in embryonic development, adult stem cell maintenance, and cancer modeling.

This review discusses the integration of drug repurposing with personalized medicine through off-label prescribing and reports no new results, highlighting the potential for systematic exploration using omics technologies.

This study identified a new subtype of lichen planopilaris that mimics androgenetic alopecia, selectively affecting vellus hair follicles and presenting unique histopathological features.

This abstract describes a program with various presentations and events focused on Canadian excellence in rheumatology but reports no new research findings.

This study explored how an educational intervention on reducing phthalate exposure through hair care product choices influenced the hair care practices of pregnant women of color, identifying key life stages and themes related to these practices, including cultural influences, product impacts, and individual autonomy.

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 article examines advancements in understanding and managing androgenic alopecia (male pattern baldness), discussing genetic and psychological factors, diagnostic methods, classifications, and various treatment options, including traditional and emerging therapies, to aid healthcare professionals in making informed treatment decisions.

This comprehensive review analyzes androgenetic alopecia treatments, discussing the mechanisms, costs, efficacy, and safety of FDA-approved drugs like minoxidil and finasteride, as well as newer non-FDA-approved options that have shown effectiveness across various studies.

This paper reviews the role of pharmacokinetic/pharmacodynamic modeling in drug development, emphasizing its application for drug dosing guidance, safety resolution, and therapeutic monitoring improvement, without presenting new clinical results.

This article argues that while evidence-based medicine has provided many benefits, it has also led to negative unintended consequences, and proposes a renewed focus on integrating context and professional expertise to improve patient treatment.

This study highlights that Demodex mites, commonly found on human skin, can cause significant eye and skin issues such as blepharitis, rosacea, and dermatitis when present in abnormally high densities, underscoring the need for targeted management strategies.

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 review highlights the significant negative impact of alopecia areata on quality of life, mental health, and work, calling for individualized approaches and effective treatments to mitigate these effects.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

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 ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.

This study reviewed the current status and future scope of artificial intelligence in healthcare, highlighting its potential to revolutionize medical practices through improved affordability, efficiency, and speed, as well as its applicability in various fields such as imaging, diagnosis, and individualized care.

This review found that oral methotrexate plus oral prednisone may improve disease activity or damage in juvenile active morphea compared to placebo plus prednisone, but adverse events may slightly increase.

This study identified a regulatory element necessary for Foxn1 expression specifically in mouse thymic epithelial cells, crucial for thymus development but not affecting hair follicles.

This study successfully developed a method to measure four flavonoids from Platycladus orientalis leaves in rat plasma, which was used in a pharmacokinetic study on healthy rats.

This study established a rapid and accurate ultra-performance liquid chromatographic method for chemical fingerprinting and analyzing Platycladi cacumen, effectively distinguishing batches based on geographical and climatic conditions.

This review discusses the interplay between signaling/transcription factor-mediated and epigenetic mechanisms in skin development and regeneration, highlighting the need for further exploration of epigenome reorganization in these processes.

This study observed that activin A and its antagonist follistatin influence the differentiation of inner ear hair cells by regulating the expression of the transcription factor ATOH1.

Detailed history and physical examination are crucial for diagnosing hair loss.

In this study, finasteride was found to reduce costs associated with benign prostatic hyperplasia compared to watchful waiting and terazosin, especially in men with higher prostate-specific antigen levels.

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.

This paper presents a new method using Convolutional Neural Networks for detecting hair and scalp diseases, aiming to enhance diagnostics accessibility through a web-based platform integration.

This study found that dutasteride is more cost-effective compared to finasteride for treating benign prostatic hyperplasia in Montenegro, justifying its funding by the national health insurance fund.