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This study describes a follicular automaton model that simulates human hair cycles and may replicate hair pattern evolution seen in diffuse or androgenetic alopecia.

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 established an androgen-responsive human organ culture model using hair follicles, demonstrating that women's intermediate facial follicles grow more hair in response to men's higher androgen levels.

This research introduces WomenCare, a web-based system using a machine learning model to predict PCOD risk by evaluating factors like age, BMI, and lifestyle habits; it aims to help women monitor their health but is not a substitute for a professional diagnosis.

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.

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 epidermal-specific deletion of integrin α3β1 significantly reduces papilloma formation in a skin carcinogenesis model by modulating HB stem cell behavior and CCN2 expression.

This study found that about 80% of known fullerene-binding proteins rank in the top 10% of scorers for C60 docking sites, confirming the accuracy of the predictive model used.

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.

This study found that a combination of five medicinal plants traditionally used in Chinese medicine significantly improved skin repair and delayed aging markers in laboratory models, suggesting synergistic anti-aging effects over individual ingredients.

This study explores how Astragaloside A from traditional Chinese medicine may combat lung adenocarcinoma by regulating key signaling molecules like STAT3 and AKT, using techniques such as network pharmacology and molecular docking to understand its mechanisms in cellular models.

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.

In a murine model of androgenetic alopecia, this study found that the newly designed IGF-1 biomimetic peptide Ac-GFFY-IGF led to faster and superior hair regrowth compared to minoxidil, suggesting potential for improved human therapies.

This study presents the first detailed 3D models of the complete K1/K10 keratin dimer and identifies structural features and interactions that may inform understanding of keratin filament assembly.

This study proposes a new mathematical model, the Harmonic Mean equation, for precisely quantifying nuclear pleomorphism in breast cancer grading, showing high performance with accuracy, recall, specificity, precision, and F1-score metrics.

In this study using a mouse model, researchers utilized single-cell RNA sequencing to create a comprehensive cellular atlas of infected versus uninfected wounds, revealing that Enterococcus faecalis infections lead to immunosuppressive changes in skin cells and disrupt normal wound healing processes.

This study aims to develop an automatic machine learning-based method using the VGG-19 model to accurately classify various hair and scalp diseases.

In this study, PA-011, a traditional Chinese medicine extract, was shown to improve hair growth in alopecia areata model mice by reducing skin inflammation and altering skin microbiota, indicating its potential therapeutic role for this condition.

This review examined the role of organoid technology in modeling genetic diseases, highlighting its promise for understanding disease pathology and developing tailored treatments by integrating genetic insights with advancements in regenerative medicine and biological engineering.

This study found that integrating breed-specific BOA and SNP-based models helps reveal the genetic factors involved in thermotolerance traits in beef cattle, enhancing insights into thermoregulation and potentially improving cattle's heat resilience.

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.

In this study, researchers used transcriptomics and modeling to uncover previously unknown cell populations and marker genes in developing hair follicles, providing insights into early cell fate establishment and offering tools for further research on skin appendages.

This study found that blocking the chemokine CXCL12 in a testosterone-induced mouse model of androgenetic alopecia restored hair regeneration and reduced fibrosis and immune alterations.

In this study, XZYFD, a Traditional Chinese Medicine formulation, was found to improve androgenetic alopecia in a testosterone-induced mouse model by promoting hair regrowth, restoring follicular morphology, and modulating androgen metabolism, MAPK signaling, and lipid metabolism pathways, suggesting potential benefits for patients with metabolic dysfunction.

This study found that the antiviral drug daclatasvir significantly improved fibrosis and quality of life in a mouse model of recessive dystrophic epidermolysis bullosa, suggesting potential for treating this and other fibrotic diseases.

In this study, cell membrane remodeling with a thermoresponsive boronic acid copolymer was shown to rapidly form spheroids from cancer or cardiac cell lines under standard conditions, promising advances in tissue engineering.

This study found that Dex-loaded hyaluronic acid hydrogel microneedles significantly reduced pain in an acute inflammatory visceral pain model without sedation, suggesting a promising and patient-friendly alternative to intravenous delivery.

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 explores the diverse physiological roles of the vitamin D endocrine system, discussing current research on receptor structure, transcription details, murine models, and alternative receptors, but reports no new findings.

In this study, a deficiency in the zinc finger protein Zfp462 in mice led to anxiety-like behaviors and excessive self-grooming, providing a new model for studying anxiety disorders.