Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

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 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 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 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 develop classifiers for identifying effective inhibitors of 5α-reductase isozyme 2, achieving high performance in distinguishing potent from weak inhibitors.

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, 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.

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

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.

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.

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.

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.

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 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.

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 discusses forensic DNA phenotyping and its potential applications, particularly for human identification in Latin American populations, but notes challenges due to genetic diversity and reports no new results.

This study concluded that QSPR models effectively predict drug skin penetration, with the Potts-Guy model showing the highest predictive accuracy for the drugs tested.

This study found that a genetic model using SNPs can predict early-onset male-pattern baldness with moderate accuracy, which may assist in decisions about interventions.

The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.

In this study, single-cell sequencing revealed clonal expansions of CD4+ and CD8+ T cells in murine and human alopecia areata, supporting the development of predictive models for human disease.

This study found that a genetic test using 5 to 20 SNPs can predict male pattern baldness with variable accuracy in European men, especially those aged 50 and older.

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 review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

In this study, machine learning models showed high accuracy in predicting therapeutic outcomes for female pattern hair loss, highlighting the significant impact of age of onset and condition duration on treatment response.

Results are not reported for this study, which used a directed acyclic graph to analyze the association between chest hair amount and prostate cancer, adjusting for age and ancestry.