Search

everythinglearnresearchcommunity

for

Search:↓

Machine learning can predict symptoms and quality of life in chronic skin disease patients using smartphone app data, and shows that app use varies with patient characteristics.

The machine learning model accurately predicts Systemic Lupus Erythematosus in Omani patients.

Tandem repeats significantly influence hair color, especially darker shades, across different ancestries.

Mothers have more hair proteins than their children, with age-related differences in protein patterns, and some proteins in hair could indicate early childhood development.

Ovol2 is crucial for hair growth and skin healing by controlling cell movement and growth.

Hair proteins in preschool children and their mothers could indicate developmental changes and health status.

The system can accurately classify hair diseases with 94.5% accuracy using a CNN.

A new model can predict drug-disease links well, helping drug research.

Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.

The Random Forest algorithm was the most accurate at diagnosing Polycystic Ovarian Syndrome.

Case-only trees and random forests improve predictions of treatment effects in clinical trials.

A model called PM-DBiGRU was developed for analyzing sentiments in drug reviews, and it performed better than other models, but struggled with complex sentences and situations requiring background knowledge.

Adult esophageal cells can start to become like skin cells, with a key pathway influencing this change.

Deep learning models can analyze scalp diseases effectively.

Gene expression in hair follicles can help diagnose methamphetamine use disorder.

A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.

Pantethine may boost the immune system's ability to fight sarcoma.

Young HS patients often have other physical and mental health issues, and research on HS covers a wide range of topics including genetics, triggers, treatments, and the need for more data.

Combining multiple algorithms predicts hair fall more accurately than using single algorithms.

Researchers found that certain lipids, especially vitamin D3, are lower in prematurely grey hair than in pigmented hair.

Researchers found four genes that could help diagnose severe alopecia areata early.

Consumers prefer general hair care products over those specifically for hair loss.

Afatinib, neratinib, and zanubrutinib could be effective against KRASG12C-mutant tumors.

Wire brush snares are best for collecting Eurasian Lynx hair for DNA analysis.

BMP2 and BMP7 have opposite roles in feather formation.

Rat dermal papilla cells have unique genes crucial for hair growth.

Taking 5-alpha reductase inhibitors does not increase the risk of breast cancer in men.

People with alopecia areata often have lower vitamin D levels than healthy people.

The document concludes that proper diagnosis and a multidisciplinary approach are crucial for managing Congenital Adrenal Hyperplasia effectively.

Lower SHBG levels may increase the risk of PCOS.