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This study reported that in rat retinas under high pressure, allopregnanolone synthesis increased and helped reduce pressure-induced damage through GABAA receptors, suggesting potential therapeutic use in glaucoma.

This study found that metabolism-related genes significantly impact the prognosis and metastasis in breast cancer, and the development of prediction models may guide personalized therapeutic strategies.

In this animal study on albino Wistar rats, Lavandula stoechas extract was found to significantly enhance diabetic wound healing, with improved healing percentages and tissue development observed, suggesting its potential in diabetic foot management and pointing to the need for further research into its mechanisms.

This review highlights how single-cell transcriptomics has advanced understanding of tissue regeneration by revealing cellular diversity and key molecular interactions in animal models, despite methodological challenges, suggesting future applications in developing targeted regenerative therapies.

This study aims to use a comprehensive health data set to develop a statistical model that can improve personalized and preventive health care by understanding relationships between various health parameters in individuals.

This study introduces PROMETHEUS, a framework that organizes causal claims from scientific texts into navigable and persistent "causal atlases," enhancing research by highlighting localized evidence, agreement, and contradictions within complex data sets.

This preclinical study found that exercise-induced extracellular vesicles delayed breast cancer tumor growth in mice, potentially by enhancing the immune response within the tumor microenvironment.

This review discusses existing forensic DNA phenotyping panels for biogeographical ancestry and externally visible characteristics and highlights major technical limitations, including terminology issues, genetic knowledge gaps, and technological debates; it reports no new results.

This study introduced a novel deep clustering approach for melanoma detection from dermoscopic images, demonstrating improved performance over existing methods by mitigating class imbalance issues using a center-oriented margin-free triplet loss.

This narrative review found that while AI-based tools in dermatology, particularly for hair disorder assessment, have potential to enhance clinical practice by improving objectivity and personalization, they currently serve mainly a complementary role and face challenges like methodological limitations and data bias.

This study explored deep learning's potential in diagnosing scalp conditions like alopecia, psoriasis, and folliculitis, using a two-dimensional Convolutional Neural Network, achieving high accuracy and precision despite challenges of a small and uneven dataset.

This article presents a comprehensive guide for classifying human hair follicle cycle stages in vivo using scalp xenografts on immunocompromised mice, offering valuable resources for researchers in the field.

This study reported that the PM-DBiGRU model enhances aspect-level sentiment classification in drug reviews, outperforming existing methods on the newly proposed SentiDrugs dataset.

Spirulina extract may effectively promote hair growth and reduce inflammation, offering a natural alternative for hair loss treatment.

This study constructed a model using serum levels of BMP2, CD8A, PRF1, and XCL1 as a non-invasive biomarker to accurately predict recurrence in patients with alopecia areata.

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 research observed that hairless guinea pig dermal fibroblasts were more sensitive to a toxic exposure than human dermal fibroblasts, suggesting that guinea pigs might serve as an intermediate model for translating in vitro findings to whole organisms.

This study evaluated various NLP models for detecting bias in medical curricula, finding that fine-tuned BERT models perform well, whereas LLMs, despite being state-of-the-art in many tasks, are unsuitable for this application.

This study developed a mathematical model using hair biomarkers (levels of Mg, K, Fe, Al, Cr) to noninvasively predict iron content in Hereford cattle muscle tissue, potentially improving livestock management and meat quality.

This paper reviews advancements in 3D human skin models using bioprinting, organoid, and organ-on-a-chip technologies, noting improvements in physiological realism through vascularization and multi-omics data, but also highlighting challenges such as cost and lack of standardization that hinder clinical adoption.

This study details the presence of two types of cysts in rhino mouse skin, with utricles derived from hair canals and deeper cysts from hair root sheath epithelium.

This study developed a model using cardiovascular radiomics to estimate biological heart age, finding obesity and other health factors strongly correlated with accelerated heart aging.

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 review discusses the expression of circadian genes in hair follicles and their potential for monitoring circadian-rhythm-related conditions, reporting no clinical results; the authors suggest combining hair sampling with other assessments for better insight.

This study found that a nomogram prediction model based on clinical characteristics, bone metabolism, and ovarian function can effectively predict the treatment response to long-acting GnRHa in girls with idiopathic central precocious puberty.

This study found that among various predictive models for baldness risk, Random Forest Regression performed best with the lowest mean squared error and highest R², indicating strong predictive accuracy, especially with complex datasets, while Linear Regression was better suited to simpler datasets.

This study utilized a mouse model of traumatic brain injury to reveal that acute neurotrauma triggers widespread lipid metabolism reprogramming and storage lipid accumulation in microglial and monocyte populations, leading to lysosomal dysfunction, inhibited autophagy, and exacerbated inflammation through a pathological feedback loop.

This study observed that using the Fisher score feature selection approach with capsule network models led to a promising 94% accuracy in diabetes detection, indicating its potential as a diagnostic tool.

This review discusses the data resources and computational models used in drug repositioning, highlighting their role in discovering unknown drug mechanisms and reports no new empirical results.

In this study, mouse models of 22q11.2 deletion syndrome showed that growth issues in small embryonic thymuses were linked to mesenchymal cells, which could be corrected by substituting with normal mesenchyme.