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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 study utilized the Random Forest Algorithm to create a machine learning model aimed at accurately predicting hair loss by considering complex datasets involving genetic, hormonal, lifestyle, and environmental factors, but specific outcomes were not reported.

This study developed a mathematical model and online tool using serum AMH, androstenedione levels, UML, and BMI to screen for undiagnosed PCOS, particularly useful for Asian populations.

This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.

This study established a predictive model for water intake in hair sheep, finding significant associations with dry matter and its intake but not with sex classes, suggesting a more accurate and efficient way to predict and manage water use in these animals.

This study developed a prediction model using data from 185 hair sheep, finding that daily water intake is best predicted by dry matter intake and establishing a widely applicable equation for these animals, validated to enhance the efficient use of water.

This review introduces pharmacophore technology and discusses its applications in toxicity prediction and limits, but reports no new clinical results.

This study used machine learning to identify molecular predictors of drug response in alopecia areata, suggesting a tool for predicting treatment efficacy based on gene signatures.

In this study, distinct molecular mechanisms of action were identified for three drugs used in alopecia areata clinical trials, providing insights for precision medicine and treatment selection.

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.

In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.

In this study, researchers developed a regression model that reasonably predicts consumer-perceived hair breakage using various parameters such as hair smoothness, detangling forces, extensional strength, and hair density among Indian women.

In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.

This study reported that a subcutaneous injection formulation using PLGA microspheres to release finasteride stably achieved sustained monthly drug delivery without burst release in beagle dogs, with a dose of 16.8 mg identified as optimal for potential first-in-human trials.

This study used machine learning to develop classifiers for identifying effective inhibitors of 5α-reductase isozyme 2, achieving high performance in distinguishing potent from weak inhibitors.

This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.

Machine learning can use blood tests to help predict moderate-to-severe alopecia areata.

This study found that using trait prevalence-informed priors may improve the prediction accuracy of appearance traits in Bayesian models, but their application is limited by sparse knowledge on trait prevalence.

This review systematically evaluates 13 animal and 2 mathematical models for alopecia areata, assessing their effectiveness in understanding the condition's pathogenesis and aiding in the development of new therapeutic strategies.

This study presented a new two-dimensional model that successfully predicts transdermal permeation of caffeine, highlighting the significant role of the follicular pathway in increasing systemic bioavailability.

In this study, researchers used computational modeling and virtual screening to identify two FDA-approved drugs, Tadalafil and Finasteride, that may effectively inhibit key proteins involved in melanoma progression, suggesting potential for new therapeutic strategies against aggressive melanoma.

In this study, the researchers identified that perturbing both AKT1 and MDM2 significantly reduces epithelial-mesenchymal transition in melanoma, proposing Cialis and Finasteride as potential therapeutic candidates with favorable properties for managing aggressive melanoma.

This study found that fibronectin nanofiber dressings, made using rotary jet spinning, accelerated wound healing and improved tissue restoration in a full-thickness wound mouse model.

In this study, researchers developed a prognostic model using 16 genes to predict lung adenocarcinoma prognosis, showing good predictive accuracy and potential clinical utility in guiding treatment decisions based on hypoxia and mitochondrial-associated gene expression.

The study introduced an advanced skin model, ImmuSkin-MT, which effectively distinguished between different strengths of skin sensitizers by mimicking the interaction of immune cells in skin tissue.

This study found that a data-driven model using XGBoost effectively predicts individualized responses to minoxidil for androgenetic alopecia, outperforming traditional methods in accuracy and reliability.

This study found that in female dogs, exposure to free fatty acids increased androgen production, suggesting a potential link to factors characteristic of polycystic ovary syndrome.

This study found that people with systemic lupus erythematosus consulted primary care more frequently and with specific clinical features before diagnosis, and it developed a risk prediction model that may help in identifying at-risk individuals.

This review found that current forensic DNA phenotyping panels for biogeographical ancestry and visible traits face significant limitations due to inconsistencies in terminology, genetic understanding, and genotyping technologies, highlighting the need for harmonization and further research.

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.