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Machine learning is effective in predicting gene functions and their relationships with diseases.

This study found that a new FAW-FS algorithm improved recognition of depression in patients with androgenic alopecia, and comprehensive psychological interventions positively impacted their rehabilitation outcomes.

A machine learning model can predict scarring in lichen planopilaris using factors like vitamin D levels and diagnostic delay.

This article proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.

This study aims to develop an automatic machine learning-based method using the VGG-19 model to accurately classify various hair and scalp diseases.

This study reports that a robotic and AI-based system successfully analyzes FUE hair transplant procedures, aiding surgeons in planning and assessing operation success through detailed pre-op and post-op evaluations.

This bibliometric analysis reviewed 4,515 articles on strabismus research from 2004 to 2023, highlighting a shift towards innovative and interdisciplinary treatment methods, with significant contributions noted from the USA, China, and the UK.

In this study, the researchers found that rare genetic variants make a minor contribution to male-pattern hair loss risk, identifying five significant gene associations, including novel genes, and noting a shared basis with monogenic hair loss disorders.

This historical analysis highlights the pioneering contributions of several late 19th-century Jagiellonian University professors to Polish and European medicine, including advancements in craniotomy, urology, and gastroscopic techniques, as well as clinical use of nitroglycerin and early identification of coronary artery embolism.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

This systematic review examines the landscape of cosmetic microneedling using SWOT analysis, identifying strengths like precise delivery and self-administration, while noting challenges such as potential skin damage and regulatory issues, with opportunities in AI-enhanced targeted treatments noted by these researchers.

This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.

This study found that higher Dietary Inflammatory Index scores were significantly associated with an increase in both the occurrence and severity of alopecia areata.

This review consolidates various rational design strategies for nanozymes, emphasizing the mechanisms needed for precise design and exploring their applications in treating inflammatory diseases, diagnosing diseases, and environmental uses, while also discussing the challenges and future prospects in this emerging field.

In this study, researchers evaluated various machine learning models for predicting type 2 diabetes risk, finding that Random Forest and K-NN models performed best in terms of precision, recall, accuracy, and other metrics using common symptoms as features.

This systematic review identified robust biomarkers associated with hidradenitis suppurativa and confirmed potential drugs for repurposing, highlighting key pathogenetic pathways and their links to comorbid disorders.

This study introduces Cell Journey, a new platform for visualizing RNA velocity in 3D, which aims to better capture complex cellular transitions in single-cell datasets compared to current 2D methods.

This study found that platelet-rich plasma therapy led to significantly increased hair growth and density compared to placebo in patients with androgenic alopecia over six months.

This study used machine learning to identify FDA-approved drugs afatinib, neratinib, and zanubrutinib as potential KRASG12C inhibitors for resistant non-small-cell lung cancer, highlighting the potential of AI in drug repurposing.

This review discusses therapeutic deep eutectic solvents as promising "green" solutions for enhancing drug solubility and delivery through the skin, reporting no new clinical results and highlighting future research directions.

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

This analysis of England's National End of Life Care Intelligence Network reports that the initiative improved palliative care using national data, addressing disparities in care especially among the poorest patients and those from non-white ethnicities over 14 years.

In this study, researchers performed a genome-wide characterization of the Wnt gene family in domestic donkeys, identifying 19 genes and highlighting their evolutionary conservation among mammals, along with tissue-specific expression patterns potentially linked to reproductive regulation and tissue homeostasis.

In this study, a computational analysis of promoter regions in human fertility-related genes identified several new candidate regulatory motifs, but these require further experimental validation due to the limitations of being an in silico examination.

This scoping review reports that while empirical evidence suggests an overlap between Long/Post Covid-19 syndrome, chronic fatigue syndrome, and fibromyalgia, the underlying molecular mechanisms remain unclear, highlighting the need for standardized definitions and rigorous methodologies in Long Covid research.

In this in silico study, researchers analyzed nonsynonymous SNPs in the LIPH gene linked to hypotrichosis and identified three potentially harmful variants (W108R, C246S, and H248N) out of 215 total, using sequence- and architecture-based bioinformatics techniques to differentiate between harmful and benign SNPs.

Combining different treatments often works best for acne scars, but see a dermatologist first.

In this retrospective study, researchers developed machine learning models to predict mortality risk among 7115 COVID-19 patients in Iran, finding that the random forests model performed best with 96% accuracy and identified factors like intubation and SpO2 as significant predictors.

This article reviews key questions in skin biology, particularly the mechanisms of hair follicle patterning and the role of stem cells in the epidermis, reporting no new results.

This review discusses techniques for analyzing the cell cycle, highlighting emerging tools for measuring cell cycle speed at single-cell resolution in live animals, and reports no new results.