research Development of a Predictive Model for Iron Levels in Bovine Muscle Tissue Using Hair as a Predictor
Hair can accurately predict iron levels in cattle muscle, helping diagnose mineral imbalances.
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research Prediction of water intake in hair sheep: development of a new model
A new model accurately predicts water intake in hair sheep using dry matter intake.
research Prediction of water intake in hair sheep: development of a new model
A new model accurately predicts water intake in hair sheep using dry matter intake.
research Predicting Polycystic Ovary Syndrome among Reproductive-Aged Women in Bangladesh Using Machine Learning Algorithms: Development of a Hospital-Based Predictive Model
Machine learning can accurately predict Polycystic Ovary Syndrome in women using clinical features.
research In Search of Predictive Models for Inhibitors of 5-alpha Reductase 2 Based on the Integration of Bioactivity and Molecular Descriptors Data.
The models can help find better inhibitors for conditions like baldness and prostate disorders.
research A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort Study
A model using hormone levels, cycle length, and BMI can help identify PCOS in Chinese women but isn't for screening teens.
research Development of predictive regression model for perceived hair breakage in Indian consumers
The model predicts hair breakage based on key hair properties and helps product developers.
research Pharmacophore Models for Toxicology Prediction
Pharmacophore models can predict liver toxicity and central nervous system toxicity, but they have limitations and specific requirements.
research A novel predictive model for the recurrence of pediatric alopecia areata by bioinformatics analysis and a single-center prospective study
A new model uses specific blood markers to predict if children's hair loss will return.
research LB1040 Machine learning-based predictive model with routine blood work identifies moderate-severe alopecia areata
Machine learning can use blood tests to help predict moderate-to-severe alopecia areata.
research Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
A deep learning model accurately predicts male hair loss types using scalp images.
research BaldPredict: AI-Driven Prediction Model for Alopecia Areata
AI can improve alopecia areata diagnosis with high accuracy.
research Development of a Two-Dimensional Model for Predicting Transdermal Permeation with the Follicular Pathway: Demonstration with a Caffeine Study
Researchers developed a model that shows hair follicles increase skin absorption of caffeine by 20%.
research Application of big data analytics in minoxidil drug development: a data-driven model for predicting efficacy and side effect risks
The model predicts minoxidil's effectiveness and side effects better than traditional methods.
research The pathogenesis model research of alopecia areata: from animal experiments to mathematical prediction models
Animal and mathematical models help understand and develop treatments for alopecia areata.
research A hair-follicle reconstructed in vitro immunocompetent skin model for prediction of the sensitizing potential of chemicals
The new skin model can predict how chemicals might cause skin allergies.
research Identification of Hypoxia and Mitochondrial-related Gene Signature and Prediction of Prognostic Model in Lung Adenocarcinoma
A gene-based model predicts lung adenocarcinoma outcomes and helps guide treatment decisions.
research Forensic DNA phenotyping: a review on SNP panels, genotyping techniques, and prediction models
Forensic DNA phenotyping faces challenges like inconsistent terms and limited genetic knowledge.
research Forensic DNA Phenotyping: a review on SNP panels, genotyping techniques, and prediction models
Forensic DNA phenotyping faces challenges due to inconsistent terminology, limited genetic understanding, and debates over technology and models.
research Genetic Prediction of Eye, Hair, and Skin Color: Forensic Applications and Challenges in Latin American Populations
Accurate prediction of eye, hair, and skin color in Latin American populations requires region-specific models and ethical guidelines.
research Predictive Skin Permeation of Ethinylestradiol,α-Asarone,Finasteride and Risperidone
The Potts-Guy model best predicts skin permeability for the tested drugs.
research Prediction of male-pattern baldness from genotypes
Genetic data can predict male-pattern baldness with moderate accuracy, especially for early-onset cases in some European men.
research Prediction of Alopecia Areata using CNN
A new CNN model can detect Alopecia Areata with 98% accuracy.
research A transcriptomic map of murine and human alopecia areata
Alopecia areata involves specific immune cells, offering potential treatment targets.
research Evaluation of DNA Variants Associated with Androgenetic Alopecia and Their Potential to Predict Male Pattern Baldness
DNA variants can predict male pattern baldness, with higher risk scores increasing baldness likelihood.
research Predictive Modeling of Hair Fall using Random Forest Algorithms
The model accurately predicts hair loss by analyzing various factors.
research Genetic prediction of male pattern baldness based on large independent datasets
New models predict male pattern baldness better than old ones but still need improvement.
research Severity Prediction of Hair Loss in Alopecia Areata with Hybrid Deep Learning Architecture
The model accurately predicts hair loss severity in alopecia areata.
research Bayesian measurement-error-driven hidden Markov regression model for calibrating the effect of covariates on multistate outcomes: Application to androgenetic alopecia
Model improves accuracy in predicting hair loss effects.
research Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment
Deep learning and explainable AI are improving scalp disorder diagnosis, but challenges in transparency and data quality remain.