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Pioglitazone, Trimipramine, and Dimetindene may be repurposed to treat psoriasis.

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

The enzyme system in hair follicles is similar to the liver's and is inhibited by certain substances.

The system can automatically classify scalp conditions with 85% accuracy.

The method accurately detects and classifies scalp diseases, including alopecia areata, with 89.3% accuracy.

Artificial neural networks can accurately diagnose Alopecia Areata.

Deep learning effectively diagnoses scalp disorders, but improvements are needed.

AI can improve alopecia areata diagnosis with high accuracy.

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

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

Machine learning can predict hair health accurately using personal data.

The model accurately predicts hair loss by analyzing various factors.

Machine learning can accurately tell apart False Daisy and Smooth Joy Weed.

AI can improve early diagnosis and classification of PCOS, aiding in prevention of related health issues.

Machine learning can accurately predict mental disorders.

A new method accurately measures cell changes in breast cancer.

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

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.

Topical minoxidil can cause excessive hair growth in children, so use it carefully.

Machine learning can accurately diagnose PCOS non-invasively using clinical and ultrasound features.

Deep learning can effectively detect hair and scalp diseases early.

A new method helps diagnose alopecia areata using specific gene markers and could guide targeted treatments.

AI can effectively predict COVID-19 mortality risk using patient data.

The random forest model effectively helps diagnose COVID-19 using key factors like age and symptoms.

The models can help find better inhibitors for conditions like baldness and prostate disorders.

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

Lichen planus is a skin condition that can resolve on its own, is linked to hepatitis C, and increases the risk of skin cancer.

Combining systemic and topical treatments, guided by weekly fungal cultures, effectively treats cat ringworm.

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