Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study found that mammary resident macrophages regulate mammary epithelium cell division and development, with implications for maintaining mammary stem cell activity and homeostasis.

In this study, the researchers found that deleting ceramide synthase 4 in the skin's epidermis alters stem cell differentiation, disrupting hair follicle structure and barrier function, potentially leading to immune responses similar to atopic dermatitis.

The study found that IPI-926 was well tolerated up to 160 mg once daily for 28-day cycles and showed activity as a single-agent treatment in Hedgehog pathway inhibitor-naïve patients with basal cell carcinoma.

This review discusses the mechanisms linking obesity and polycystic ovary syndrome and explores potential management options, but it presents no original research findings.

This review discusses the impact of weight gain and obesity on the development of polycystic ovary syndrome and explores lifestyle strategies to manage the condition, without reporting new clinical results.

This review discusses the physiology of hair growth, common abnormal patterns of hirsutism, and examines differential diagnoses and treatment options, but provides no new clinical findings.

This study concluded that finasteride is a viable first-line treatment for moderate uncomplicated benign prostatic hyperplasia, particularly in patients with larger prostates, by significantly reducing acute urinary retention and the need for surgery.

In this study, Gaussian process regression models combined with Grey Wolf optimization were used to predict and optimize phenolic and flavonoid content extraction from Carthamus caeruleus L. rhizomes, showing high accuracy and helping improve understanding and extraction processes through a new interactive tool.

In this study, abnormal glucose tolerance was found to be prevalent among young Indian women with PCOS, and the use of an oral glucose tolerance test significantly improved its detection.

This study found that the Random Forest machine learning algorithm achieved the highest accuracy, 96%, in diagnosing Polycystic Ovarian Syndrome using patients' clinical data.

This review discusses the challenges in measuring testosterone for assessing androgen excess in women and emphasizes the importance of quality control in laboratory methods, but it reports no new clinical results.

In this study, researchers used EHR data from two large PCORnet networks to identify a range of long COVID diagnoses, highlighting varying post-acute risks across populations in NYC and Florida.

This study found that using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.

This study found that Phytochrome A is crucial for regulating central metabolism and the transition to mature seedling proteomes in tomato seedlings grown under far-red light.

This study observed that using the Fisher score feature selection approach with capsule network models led to a promising 94% accuracy in diabetes detection, indicating its potential as a diagnostic tool.

This study explored the optimization of phytochemicals from Alstonia boonei to develop potential 5-alpha reductase inhibitors for Benign Prostatic Hyperplasia, finding promising analogs with enhanced binding affinity and stability compared to Finasteride, warranting further experimental validation.

This study reviews the transformative role of artificial intelligence in biomaterial design, highlighting its ability to reduce costs through virtual screening, enhance material performance, and predict biological interactions to advance personalized and precision medicine.

In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.

In this study, researchers introduced a machine learning approach to discover new nanozymes through the DiZyme platform, enabling the accurate prediction of multiple catalytic activities, and providing a comprehensive database and assistant resources for users.

This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

This study demonstrates that combining gene expression data with a random forest algorithm provides highly accurate diagnosis of childhood growth hormone deficiency, showing potential utility in distinguishing it from non-GHD short stature.

In this study, teledermatology did not significantly differ from in-person visits in complete response rates for actinic keratosis treatment, but it showed higher treatment failures and more frequent local reactions.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

This study identified a four-gene loop as a non-invasive biomarker that selectively activates in alopecia areata, providing a precise target for JAK inhibitor treatments.

This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.

This supplementary issue of the Pakistan Journal of Urology contains diverse studies spanning organ donation's significance, surgical techniques, and the comparison of treatments in urology, but it doesn't provide specific research results or detailed findings.

In this study, researchers aimed to develop a Naive Bayes algorithm to predict hair loss risk based on personal and clinical data, including age, gender, stress levels, hormones, and family history. Results were not reported.

Smart microneedles using advanced tech could improve psoriasis treatment.

In this study, the integration of obesity-related features and machine learning techniques significantly enhanced cardiovascular disease detection, with the XGBoost classifier achieving a 74% accuracy rate and improved metrics compared to other models.

This study found that bi-weekly 1550nm low-level laser therapy significantly improved hair loss conditions and scalp health in participants, suggesting its potential as an effective non-invasive treatment option.