research Frontal Fibrosing Alopecia: A Review of Disease Pathogenesis
The cause of Frontal fibrosing alopecia, a type of hair loss, is complex, likely involving immune responses and genetics, but is not fully understood.
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research Cannabinoid Compounds as a Pharmacotherapeutic Option for the Treatment of Non-Cancer Skin Diseases
Cannabinoids like CBD and THC may help treat non-cancer skin diseases, but more research is needed.
research Analysis of Animal Models of Alopecia Areata Based on Clinical Features of Traditional Chinese and Western Medicine
Better models and evaluation methods for alopecia areata are needed.
research Hair Follicle-Related MicroRNA-34a Serum Expression and rs2666433A/G Variant in Patients with Alopecia: A Cross-Sectional Analysis
Higher miR-34a levels and the A variant of the MIR-34A gene are linked to increased risk and severity of alopecia areata.
research Trichoscopy of Androgenetic Alopecia: A Systematic Review
Trichoscopy shows hair diameter variability, vellus hairs, and the peripilar sign are key indicators for diagnosing Androgenetic Alopecia.
research Evolution of Ritlecitinib Population Pharmacokinetic Models During Clinical Drug Development
Ritlecitinib shows promise as a versatile treatment for various autoimmune and inflammatory diseases.
research Cutaneous Manifestations of Lupus Erythematosus from a Tertiary Care Center in Coastal Kerala
Most lupus patients in coastal Kerala had skin-specific lesions that could help diagnose the disease, and non-specific skin issues were linked to more severe, systemic lupus.
research Follicular Density and Ratios in Scarring and Nonscarring Alopecia
Nonscarring alopecia has higher hair density than scarring alopecia, and hair density can help diagnose the type of alopecia.
research Morphological Changes of the Hair Roots in Alopecia Areata: A Scanning Electron Microscopic Study
Scanning electron microscopy revealed four distinct hair root shapes in alopecia areata, suggesting a less invasive diagnostic method.
research Hair Disease Classification Using Convolutional Neural Network Algorithm with VGG-16 Architecture
The system can accurately classify hair diseases with 94.5% accuracy using a CNN.