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This study used machine learning to identify molecular predictors of drug response in alopecia areata, suggesting a tool for predicting treatment efficacy based on gene signatures.

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.

In this study, no significant relationship between metabolic syndrome parameters and alopecia areata was observed among the patients.

This study found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

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 observed significant abnormalities in clinical and laboratory parameters related to metabolic syndrome among patients with skin tags.

This review explores traditional Ayurvedic methods in Bhela Samhita for assessing health through physical parameters, and calls for further research to validate these practices.

In this study, scalp hypothermia with the Theracool™ cooling machine was largely ineffective for preventing alopecia in breast cancer patients undergoing a chemotherapeutic regimen including cyclophosphamide and anthracyclines.

This study found that isotretinoin therapy at 0.5 mg/kg per day does not significantly alter hair growth parameters in patients with moderate-severe acne vulgaris over a three-month period.

This research introduces WomenCare, a web-based system using a machine learning model to predict PCOD risk by evaluating factors like age, BMI, and lifestyle habits; it aims to help women monitor their health but is not a substitute for a professional diagnosis.

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 research explores the impact of advanced technologies, such as machine learning and robotics, on cardiothoracic surgery, noting that innovations like artificial hearts and enhanced circulatory support systems may improve patient outcomes by aiding diagnostics, surgery planning, and postoperative care.

In this study, daughters of women with PCOS and control daughters showed no significant differences in metabolic and hormonal parameters, but more PCOS daughters exhibited hirsutism at advanced pubertal stages.

This study introduced a single-stage network using large kernel attention that effectively restores high-resolution images by capturing both global and local details, reducing parameters and improving processing speed.

This study found that Guasha therapy did not significantly alter nerve fiber morphology or the expression of CGRP and SP in rat skin, with parameters generally returning to normal within five days.

This study conducted a comprehensive analysis of methods for assessing surface roughness, highlighting that contact methods using profilometers provide accurate results for machine parts, while non-contact optical and laser methods offer high precision and efficiency without damaging surfaces.

In this study, the authors found that syntax-based neural networks performed comparably to pre-trained Transformers on tasks involving definitely unseen sentences, suggesting they are a more transparent and parameter-efficient alternative for certain Natural Language Processing applications.

In this study, researchers developed a machine learning model that accurately diagnosed scalp conditions like fine dandruff and perifollicular erythema with 75% and 82% accuracy, respectively, and created a user-friendly web platform for scalp health self-assessment, which achieved high user satisfaction.

This study introduces a novel method for digitizing hair color that accurately captures and renders the color appearance of physical hair samples in synthetic images.

This study found that cell-free RNA, particularly DNAJB9, shows potential as a biomarker for diagnosing and prognosing female androgenetic alopecia using a machine learning model.

This study evaluated a novel, user-friendly approach for detecting hairfall trends over time using machine learning models. The Temporal Fusion Transformer model demonstrated high accuracy in identifying anomalies in hair shedding patterns, potentially aiding in the early detection of health risks related to hormonal fluctuations.

In this split-scalp study, researchers observed that combining fractional CO2 laser and PRP treatment was significantly more effective in improving hair parameters in women with female pattern hair loss than using each intervention alone or placebo.

This study found that the EpiLight Hair Removal System achieved approximately 60% hair removal after 12 weeks following a single treatment, indicating effectiveness and safety for long-term hair reduction.

This study found that GTx-024 and Danazol had similar effects to dihydrotestosterone on uterine tissue in mice, while GTx-007 showed minimal impact on the parameters tested.

This review examines the potential therapeutic effects and mechanisms of quercetin for treating polycystic ovary syndrome, noting improved clinical parameters in some studies but presenting no new results.

This study found that patients with tropical chronic pancreatitis exhibited significantly reduced plasma amino acid levels and antioxidant capacity, with folate deficiency identified as a key factor in hyperhomocysteinemia.

This study investigated the sub-acute toxicity of Eclipta alba extract in Wistar albino rats, finding generally comparable hematological and biochemical parameters to controls, but noted mild morphological liver and kidney changes at the highest dose, highlighting a key area for further safety research.

This study highlights the growing role of artificial intelligence in vascular surgery, where AI improves diagnostic accuracy, surgical planning, and patient monitoring, ultimately enhancing clinical outcomes, shortening recovery times, and reducing healthcare costs.

This study found that a daily dose of 1 mg finasteride is effective for treating male pattern baldness, but benefits typically require at least three months to manifest and ongoing treatment is necessary to maintain results.