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A new computer tool quickly measures hair thickness differences in people with common types of hair loss.

Blood type affects COVID-19 infection rates differently in Europe and Africa.

Diagnosing sex development disorders requires combining medical history, physical exams, imaging, lab tests, and genetic data.

TBX3 gene affects pigmentation and marking formation in Dun Mongolian horses.

Machine learning can help find new ways to treat alopecia areata.

Forensic DNA Phenotyping can now better predict appearance, ancestry, and age from DNA, but more research is needed for precise police use.

The AI model somewhat predicts lymph node status in melanoma patients using skin sample images.

RCM and dermoscopy help identify different types of hair loss in children.

The document suggests a new model for evaluating public research that better captures the full value of knowledge creation and use, using PCOS research as an example.

The model can effectively help diagnose meibomian gland dysfunction automatically.

Dermatology datasets need more diversity in skin tones and ethnic representation.

The study created a model to help design new inhibitors for steroidal 5α-reductase enzymes.

XGBoost can effectively diagnose PCOS with 87% accuracy.

Common baldness is likely inherited through multiple genes, not just one.

Zebrafish help understand genetic causes of skin pigment disorders like albinism.

Machine learning identified three unique subtypes of androgen excess in women with PCOS, each with different metabolic risks.

No link between finger length ratios and color blindness was found.

3D skin models better mimic human skin and melanoma progression than older methods.

The model accurately detects early-stage hair loss using images.

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

A new CNN model can detect Alopecia Areata with 98% accuracy.

A model using hormone levels, cycle length, and BMI can help identify PCOS in Chinese women but isn't for screening teens.

Hair diameter diversity could improve androgenetic alopecia assessment and treatment planning.

No current system perfectly classifies male-pattern hair loss, indicating a need for a new system for better diagnosis and treatment.

The digital system for measuring melasma shows promise but needs more development for better accuracy and automation.

Large language models can accurately identify monkeypox from medical records.

Discoid lupus erythematosus involves immune activation and fibrosis around hair follicles, with shared pathways across humans, dogs, and mice, suggesting potential treatments for both humans and animals.

The technique accurately identifies and evaluates hair follicle structures in skin.

The ShorT method can detect and help reduce bias in medical AI by identifying shortcut learning.

DNA profiling in forensics has improved, but predicting physical traits and ancestry from DNA has limitations and requires ethical consideration.