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This study synthesizes evidence on the complex interplay between stress, sleep, nutrition, lifestyle, and systemic health and their impact on hair health, encompassing premature greying, hair loss, and follicle cycling.

In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.

This study developed a model predicting uranium excretion in human hair from drinking water exposure, suggesting it is a viable indicator for assessing internal uranium burden.

This study in young Ukrainian men found that specific body measurements, like shoulder width and tibia epiphysis width, are highly predictive of alopecia areata occurrence, though not for its progression, using discriminant models (p<0.001).

This study found that a laboratory model using nude mice can produce human hair follicles with amino acid compositions resembling both normal and trichothiodystrophy-affected human scalp hair over extended periods.

This article reviews the clinical use of platelet rich plasma for skin regeneration, highlighting the challenges of inconsistent outcomes due to varying preparation protocols, and calls for standardized models to better assess its effectiveness.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

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.

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 found that the DNN-DTIs prediction model achieved high accuracy in predicting drug-target interactions, suggesting its potential application in drug repositioning and the discovery of new uses for existing drugs.

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 documented that a CNN-KNN hybrid model achieved 98% accuracy in predicting hair breakage levels due to Telogen Effluvium, highlighting its potential for enhancing diagnosis and treatment in clinical dermatology through early detection of hair-related conditions.

This study proposed a model that accurately predicts skin condition using genotype information and machine learning, suggesting potential for creating customized cosmetics.

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 database provides detailed physiological parameters of porcine skin to enhance the MPML MechDermA Model, improving dermal absorption predictions for human studies in the Simcyp Simulator.

This study introduced a novel nonsteroidal androgen receptor antagonist that effectively controls sebum production in the golden Syrian hamster ear model through targeted follicular delivery.

In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.

This study found that the 4Kscore model, when adjusted for kallikrein marker changes due to finasteride use, improved the prediction of high-grade prostate cancer compared to adjusted total PSA alone in men taking 5-α-reductase inhibitors.

This review discusses recent advances in microfabrication and microfluidic technologies for improving the production of organoids and spheroids, but it reports no new experimental results.

This study found that HB-EGF and TGF-α work together to stimulate wound healing in a murine model of thermal injury, with peak keratinocyte proliferation observed by postburn day five.

This study found that using 3D dermal papilla spheroid models enhances extracellular matrix production and hair follicle marker expression, providing insights into hair follicle biology and potential for drug screening.

This study developed a new method for analyzing multivariate time-series data that successfully predicts website competition dynamics and outperforms conventional methods in identifying and predicting competitive structures.

This study reports that a clinical prognostic model based on immune-related genes improved survival prediction for patients with clear cell renal cell carcinoma, also identifying potential drugs targeting the gene DOCK8.

This study found that a specific amino-pyridine compound showed potent androgen receptor antagonist activity, stimulating hair growth in mice and reducing sebum production in a hamster model.

This study reports that 4-(alkylthio)- and 4-(arylthio)-benzonitriles showed moderate sebum reduction as androgen receptor antagonists when applied topically in a validated animal model.

This study found that among the tested analogs, compound 4e was the most effective in inhibiting wax esters in vivo in the Golden Syrian hamster ear model.

In this study, topical applications of Agrimonia pilosa extract and agrimonolide demonstrated notable anti-inflammatory and wound healing benefits in animal models, significantly reducing inflammation and enhancing wound closure compared to controls. However, further research in humans is needed to confirm these preclinical findings.

This study suggests that histological features of primary melanoma can partially predict lymph node metastasis using AI, achieving a best prediction AUROC of 0.65.

This study found that localized skin calcification could be induced in adult rats through dihydrotachysterol treatment combined with topical trauma, offering an experimental model for calcifying scleroderma.

This study found that Ashwagandha-derived exosome-like nanovesicles promoted hair growth in several preclinical models and may offer a novel non-drug strategy to reduce hair shedding.