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In this study, researchers identified genes IRF2BP2 and EGFR as key to understanding double-coated fleece formation in Hetian sheep, offering insights that may advance machine learning-driven multi-omics selection models in sheep breeding.

This study investigated the use of 3D cell culture technology to explore hair follicle regeneration, examining the impact of the 3D cellular environment on hair follicle morphogenesis and the effects of microwell depth on spheroid formation.

This preclinical study found that Mesenchymal Stem Cell-derived Conditioned Media formulations were safe and effective in rat and mouse models for treating chemotherapy- and radiation-induced oral mucositis, demonstrating improved tissue regeneration and healing, most notably at higher concentrations.

This study discovered that androgenetic alopecia disrupts the scalp microbiome balance across the whole scalp, not just areas with hair loss, and introduced a microbial index for early detection and severity prediction.

This study developed an ointment from palm kernel oil and shea butter, which demonstrated improved hair growth in rabbits and may inform the creation of non-harmful hair growth products for humans.

Innovative methods are reducing animal testing and improving biomedical research.

This study found that a new FAW-FS algorithm improved recognition of depression in patients with androgenic alopecia, and comprehensive psychological interventions positively impacted their rehabilitation outcomes.

This study examined the cardiovascular safety of oral minoxidil, used off-label for androgenetic alopecia, particularly as its usage expands in real-world settings, although specific results were not detailed in the abstract.

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.

In this study, researchers evaluated various machine learning models for predicting type 2 diabetes risk, finding that Random Forest and K-NN models performed best in terms of precision, recall, accuracy, and other metrics using common symptoms as features.

This study suggests that novel diagnostic, preventive, and treatment approaches for autoimmune diseases like alopecia areata may be developed by identifying hub genes, and highlights the usefulness of machine learning and bioinformatics in finding new disease biomarkers.

This study identified changes in serum albumin and calcium levels one month after laparoscopic sleeve gastrectomy as independent factors linked to hair loss in patients with obesity, highlighting the importance of monitoring these nutritional indicators to reduce postoperative alopecia risk.

This study applied a machine learning-based genomic analysis to identify genetic markers associated with wool traits in Central Anatolian Merino sheep, successfully highlighting loci relevant to fiber diameter, staple length, and greasy fleece yield, which could inform breeding programs to enhance wool quality and yield.

This abstract discusses the effects of finasteride on semen and hormone parameters in men with infertility but does not provide new research findings.

This study used machine learning to identify FDA-approved drugs afatinib, neratinib, and zanubrutinib as potential KRASG12C inhibitors for resistant non-small-cell lung cancer, highlighting the potential of AI in drug repurposing.

The LTF gene may help predict and manage nonspecific orbital inflammation.

This study reports that quantitative trichoscopy, while noninvasive and convenient, provides less accurate hair density data compared to invasive histopathologic examination, which was demonstrated in a sample of adult Chinese individuals without alopecia.

This study found that supplementing raccoon dogs' diets with 0.25 g/kg Platycladus orientalis leaf extract improved growth performance and altered intestinal microbiota, but higher doses may have adverse effects.

This study found that patients with androgenetic alopecia had thinner scalp and subcutaneous tissues, narrower and shorter hair follicles, a lower hair follicle count, and fewer color flow signals compared to healthy volunteers, as measured by 22 MHz ultrasound.

This review discusses advancements in skin microphysiological systems, such as 3D bioprinting, skin organoids, and skin-on-a-chip, and their effectiveness in emulating human skin functions for research and preclinical applications, highlighting the potential for replacing animal testing with these innovative technologies.

This study highlights emerging regenerative strategies, such as stem cell-derived therapies and machine learning tools, that may advance hair loss treatment beyond traditional methods by promoting follicle regeneration and offering personalized care.

This scoping review describes the current use of artificial intelligence in alopecia research, highlighting AI's evolution from diagnostic to prognostic applications in dermatology and identifying gaps in multimodal integration and fairness across demographics.

This review highlights new perspectives in genomics and epigenomics for skin rejuvenation, comparing innovative strategies like senolytics and DNA repair modulators with classical treatments, and emphasizing the importance of tailoring therapies using individual genomic profiles for personalized anti-ageing approaches.

This study developed a hat-shaped device with wearable sensors to estimate scalp moisture content using machine learning, demonstrating that it can provide accurate measurements comparable to professional scalp analyzers without the need for high-cost equipment.

This study found that individuals with androgenetic alopecia had higher scalp microbiome diversity and more complex scalp-gut microbiome networks than those without, suggesting a link between skin-gut microorganisms and hair loss development.

This study found that integrating machine learning enhances the predictive accuracy of forensic DNA phenotyping from low template DNA, achieving high accuracy for traits like eye color, although challenges remain for admixed populations and complex traits.

This study examined postmenopausal hyperandrogenism related to ovarian disease and found that specific biochemical markers can help differentiate between tumorous and non-tumorous cases, with a good level of sensitivity and specificity, supporting surgical intervention for resolution.

This study found that abdominal obesity was the primary predictive factor for nonalcoholic fatty liver disease in women, rather than the presence of polycystic ovary syndrome.

This study found that minoxidil had the highest solubility in shea butter, stearic acid, and rosemary oil, suggesting these natural oils may improve drug loading and encapsulation efficiency in lipid nanocarrier formulations for potential hair growth applications.