Search

everythingexperts & clinicslearnresearchcommunity

for

Search:↓

Using trait prevalence priors in genetic prediction models for appearance traits is currently impractical due to limited knowledge and potential accuracy issues.

Genetic factors influence hair traits like shape, color, and greying in Latin Americans.

Genomic prediction can improve breeding strategies for Korean Sapsaree dogs.

Artificial intelligence can accurately predict hair growth and treatment results in female pattern hair loss patients, with age of onset and duration being key factors.

New models predict male pattern baldness better than old ones but still need improvement.

Alopecia areata may be linked to scalp microbiome differences, suggesting potential treatments with prebiotics, probiotics, and postbiotics.

Human hair follicle cells can help regenerate hair and reduce inflammation, aiding in hair loss treatment.

Serum AMH levels are a strong marker for diagnosing PCOS in Indian women.

Hair greying is mainly influenced by age, with genetics playing a smaller role.

Ch55 may help reduce skin scarring and fibrosis.

IL-1 blockade is effective for treating SURF, and personalized treatment is needed.

KRDQN effectively predicts adverse drug reactions with high accuracy and clear explanations.

Certain immune molecules and stress affect hair loss, and while genes play a role, more research is needed to fully understand and treat it.

The study identified 12 potential biomarkers for hair loss and how they affect hair growth.

A new tool accurately identifies human cornea cell states and key factors.

Shaoyao Gancao decoction may treat alopecia areata by affecting the PI3K/AKT signaling pathway.

Model improves accuracy in predicting hair loss effects.

CDK4/6 inhibitors affect safety and quality of life differently, requiring careful use.

Cellularity can estimate mesenchymal stem cell concentration in bone marrow, aiding regenerative medicine.

Polygenic risk scores can predict the risk and outcomes of benign prostatic hyperplasia.

Ischemic modified albumin could be a new indicator of oxidative stress in people with alopecia areata.

The new method predicts post-hair transplant images more accurately than other methods.

Machine learning can effectively predict type 2 diabetes risk.

Long-lived proteins may predict age-related diseases.

Lupeol shows promise for hair growth but needs better absorption and safety testing.

Tadalafil and Finasteride may help treat aggressive melanoma.

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

N6-methyladenosine affects hair follicle development differently in Rex and Hycole rabbits.

The model predicts minoxidil's effectiveness and side effects better than traditional methods.

Female donor to male recipient sex mismatch and positive ACA-IgG are key risk factors for vitiligo and alopecia areata in chronic GvHD patients.