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

Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.

The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.

Reviewers suggested the study on finding new drug uses through social media side-effects needs better methods and clearer limitations.

The study improved and was accepted despite initial concerns about data clarity, methodology, and potential overfitting.

A hat with sensors can measure scalp moisture well, helping with hair care.

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

A machine learning model called CATNIP can predict new uses for existing drugs, like using antidepressants for Parkinson's disease and a thyroid cancer drug for diabetes.

Four genes are potential markers for hair loss condition alopecia areata, linked to a specific type of cell death.

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

The machine learning model accurately detected hair loss and scalp diseases using processed images.

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

C-scores can help predict gain-of-function and loss-of-function mutations.

AI is revolutionizing healthcare by improving diagnosis, treatment, and monitoring, but still needs close supervision.

The method can effectively extract biomedical information without needing expert annotation, performing better than previous models.

The search scheme SMRI is faster and more secure for retrieving encrypted data from the cloud.

Certain molecules in hair change with age and could be used for cosmetic treatments.

AnnoPharma effectively identifies substances causing adverse drug reactions in medical abstracts.

Zebrafish larvae are used to study and find treatments for ear cell damage because they are easier to observe and test than mammals.

Hair elemental analysis could be useful for health and exposure assessment but requires more standardization and research.

Hair mineral analysis might help diagnose diseases early, but standard methods are needed.

The document concludes that computational methods using networks and various data can improve the process of finding new uses for existing drugs.

The study developed a tool to predict how gut microbes process foods and drugs, showing that similar compounds often share metabolic pathways and effects.

lncRNA XLOC_008679 and gene KRT35 affect cashmere fineness in goats.

Some growth factors, while important for normal body functions, can cause diseases when not regulated properly.

lncRNAs may regulate hair follicle development in Hu sheep.

Non-coding RNAs help control hair growth in cashmere goats.

Aging skin shows thinner layers, fewer hair follicles, and new biomarkers like increased space between cells and smaller sebaceous glands.

Certain long non-coding RNAs are important for the growth of hair follicles in Inner Mongolian cashmere goats.

Taking Saw palmetto and Pygeum africana can change the levels of certain steroids in urine, which could cause confusion in doping tests.