Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study suggests that stress intensity is highly correlated with hairfall severity, highlighting the potential of an inexpensive and accessible machine learning approach for forecasting and prevention.

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 found that certain molecular quantum descriptors can be directly correlated with the biological activity of benzo[c]quinolizin-3-ones, potentially aiding in the identification and design of active compounds.

This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

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 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 machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.

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 found that both desmoglein-specific and non-desmoglein autoantibodies may play active roles in Pemphigus vulgaris pathogenesis, with HLA genetics influencing autoimmune specificity.

This study found that using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.

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 reported that mSKPs and DMSCs share similarities in biological characteristics but exhibit distinct transcriptome profiles, with mSKPs being more immune-related and DMSCs more associated with differentiation and disease pathways.

This study reports previously unrecognized cellular complexity in growing mouse incisors, suggesting species-specific differences in cell dynamics between mouse and human teeth related to growth and differentiation.

In this study, Gaussian process regression models combined with Grey Wolf optimization were used to predict and optimize phenolic and flavonoid content extraction from Carthamus caeruleus L. rhizomes, showing high accuracy and helping improve understanding and extraction processes through a new interactive tool.

This study, using multimodal profiling in mice, found that various tissues contain unique γδ T cell subsets adapted to their environment, revealing their functional diversity, lineage relationships, and similarities to CD8+ tissue-resident memory T cells.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

This study found that metabolism-related genes significantly impact the prognosis and metastasis in breast cancer, and the development of prediction models may guide personalized therapeutic strategies.

This study found that by using an automated system to analyze skin histology, 108 structural features were significantly affected by age, and it identified four new aging biomarkers and notable sex-based differences in skin aging.

This study observed that using the Fisher score feature selection approach with capsule network models led to a promising 94% accuracy in diabetes detection, indicating its potential as a diagnostic tool.

This study describes a follicular automaton model that simulates human hair cycles and may replicate hair pattern evolution seen in diffuse or androgenetic alopecia.

This study demonstrated that CD19-CAR T cell therapy may promote structural regeneration in the skin of systemic sclerosis patients, as evidenced by histological improvements and fibroblast population changes, suggesting its potential for tissue remodeling in fibrotic diseases.

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 evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.

This paper presents a new method using Convolutional Neural Networks for detecting hair and scalp diseases, aiming to enhance diagnostics accessibility through a web-based platform integration.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

This study found that AtCEPs in Arabidopsis thaliana play a role in controlling root hair growth by processing EXT proteins, with NAC1 acting to regulate their expression and influence elongation.

The researchers observed that in cichlid fishes with different dental structures, tooth replacement accelerated more than three times following tooth extraction, alongside distinct changes in gene expression and cellular interactions over one week, providing insights into tooth regeneration mechanisms in vertebrates.

This study used integrated single-cell chromatin and transcriptomic analyses in developing mouse skin to uncover gene networks involved in skin lineage specification and identified Mef2c+ upper fibroblasts as potential precursors to certain muscle-like structures, with cross-species findings in human skin.

This review highlights icariin’s potential to regulate macrophages in varying conditions, discussing its effects on macrophage polarization, metabolism, and disease mechanisms, and noting the development of delivery systems to enhance its therapeutic impact.

This review examines the current state of automated vitiligo detection systems, noting a lack of large, diverse datasets and consistent imaging conditions, while comparing traditional and modern machine learning approaches to improve reliability and applicability.