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3D microenvironments in microwells improve hair follicle stem cell behavior and hair regeneration.

The gel is safe and effective for treating oral mucositis from chemotherapy and radiation.

Machine learning can effectively identify genes to improve wool quality in sheep.

Microbial imbalances on the scalp can help diagnose and manage hair loss early.

The organic hair ointment is safe, promotes hair growth in male rabbits, and has good stability and pH for human use.

Innovative methods are reducing animal testing and improving biomedical research.

The FAW-FS algorithm improves depression recognition, and psychological interventions help AGA patients' mental health.

Minoxidil is strongly linked to heart problems, and machine learning can improve drug safety checks.

Machine learning can accurately predict hair loss early, improving treatment options.

Lower levels of certain nutrients after surgery may increase hair loss risk.

The LTF gene may help predict and manage nonspecific orbital inflammation.

Trichoscopy and pathological examination both have unique benefits and cannot completely replace each other in evaluating hair.

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

A small dose of Platycladus orientalis leaf extract improves raccoon dogs' growth and health, but higher doses can be harmful.

Machine learning can predict how well patients with alopecia areata will respond to certain treatments.

High-frequency ultrasound can effectively visualize and assess hair loss.

New technologies replicate human skin for testing without animals.

New regenerative treatments show promise in improving hair growth for androgenetic alopecia.

Personalized skin rejuvenation using genomics shows promise but needs more research.

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

The scalp microbiome is more diverse and may be more important in hair loss than the gut microbiome.

Machine learning improves DNA predictions for eye and hair color, but challenges remain for skin tone and facial features.

Biochemical markers help diagnose ovarian tumors in postmenopausal women.

Belly fat is the main predictor of nonalcoholic fatty liver disease in both women with Polycystic ovary syndrome and healthy women.

Reliable machine learning in medical imaging needs bias checks and data drift detection for consistent performance.

Minoxidil dissolves best in shea butter, stearic acid, and rosemary oil, which may improve hair growth treatments.

Adding obesity data to machine learning models improves heart disease prediction accuracy.

There is no significant link between metabolic syndrome and alopecia areata.