Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study highlights the need for standardized preclinical murine models to improve the translation of wound healing research to human clinical applications, specifically noting variations in diabetes duration, animal sex, and assessment methods on healing outcomes in rat models.

This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

This study found that integrating machine learning enhances the predictive accuracy of forensic DNA phenotyping from low template DNA, achieving high accuracy for traits like eye color, although challenges remain for admixed populations and complex traits.

Certain patient characteristics can help predict hair regrowth success with ritlecitinib in alopecia areata.

Ch55 may help reduce skin scarring and fibrosis.

This study found that ATR-FTIR spectroscopy combined with machine learning effectively differentiated alopecia areata patients from healthy controls with an AUC of 0.85, and also showed promise in predicting treatment response, particularly through alterations in the Amide I band.

Researchers created a simple tool to predict bladder blockage from prostate enlargement using urine flow rate and prostate volume.

This study found that communication about aging across seven domains may predict successful aging in Indonesia through both positive and negative effects and efficacy toward aging.

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 while genetic variants contribute minimally to predicting hair greying in a Polish population, age remains the primary predictor, underscoring the complexity of hair greying as a genetic trait.

In this study, a machine learning framework incorporating the CatBoost algorithm accurately predicted Systemic Lupus Erythematosus in Omani patients, suggesting potential for early clinical intervention.

This study introduced the KRDQN predictive framework, which outperformed existing methods in predicting adverse drug reactions and provided interpretable insights into drug mechanisms, aiding pharmacovigilance and clinical decision-making.

This study developed a vascularized full-thickness skin equivalent with a complex vascular network, enhancing its predictiveness for topical and systemic drug applications in dermatology research.

This study found that higher Dietary Inflammatory Index scores were significantly associated with an increase in both the occurrence and severity of alopecia areata.

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 review discusses recent advances in understanding Merkel cell carcinoma biology, including the development of genetically-engineered mouse models and potential therapeutic targets, but reports no new clinical results.

In this study, a novel GAN-based image translation method focusing on regions of interest showed improved predictive performance for post-hair transplant images compared to existing methods, using an ensemble approach to enhance robustness and detection accuracy.

This case study reports that ingestion of topical minoxidil, used for hair loss, can cause severe circulatory shock and acute pulmonary edema, necessitating immediate medical intervention with fluids and vasopressors.

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

This study found that modeling and simulation effectively informed decision-making and dose selection for ritlecitinib in treating alopecia areata, supporting an accelerated drug development strategy.

This study found that integrating the Theory of Planned Behaviour with the Stimulus-Organism-Response framework improved the prediction of intentions to buy embarrassing products, with subjective norms being significant across drugstore, internet, and multi-channel environments.

This study found that PC 3DP models are reliable preclinical tools that could potentially customize treatment strategies and predict patient prognoses by correlating drug sensitivity profiles with clinical outcomes, though larger patient cohort validation is needed to confirm clinical utility.

This study found that anagen stage protein homogenates and specific epitopes from melanogenesis proteins activated CD8 T cells, suggesting alopecia areata is an anagen-specific disease.

This study used homology modeling to create a detailed in silico structure of 5α-reductase type II, suggesting it can aid in designing steroid reductase drugs.

In this study, desmopressin-loaded elastic liposomes (ODEL1) showed improved permeation and drug deposition across rat skin by altering the skin's cohesive energies and causing reversible changes on its surface.

This study found that using machine learning models, particularly Random Forest with 93% accuracy and 86% sensitivity, can effectively predict PCOS by analyzing features like antral follicle count, hair growth, and skin pigmentation, offering a promising alternative to traditional diagnostic methods.

This study reported an increase in allergic contact dermatitis cases linked to natural products, particularly propolis and marigold, with a higher incidence in women than men.

This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

This review suggests a shift toward genetically informed treatments for male pattern hair loss by integrating genetic insights and pharmacogenetic markers into therapeutic decision-making.

In this study, researchers introduced a machine learning approach to discover new nanozymes through the DiZyme platform, enabling the accurate prediction of multiple catalytic activities, and providing a comprehensive database and assistant resources for users.