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This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.

This study introduced an innovative metric for assessing college students' mental health, incorporating temporal perception and a hybrid clustering algorithm, and found it achieved over 90% accuracy, outperforming existing methods in evaluating mental health during public health challenges.

This review presents a comprehensive evaluation of medical reasoning using large language models, highlighting a significant gap between exam-level performance and true clinical decision-making accuracy.

This study developed a new neural network model (AA-GAN-AB-MTEDeep) to enhance Alopecia Areata classification using synthetic scalp images, achieving an accuracy of 96.94%.

This study found that the DNN-DTIs prediction model achieved high accuracy in predicting drug-target interactions, suggesting its potential application in drug repositioning and the discovery of new uses for existing drugs.

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 utilized U-Net's image-processing capabilities to achieve 92% accuracy in segmenting individual hair strands, enhancing early detection and reliable identification of hair fall areas, which assists in addressing challenges of subtle hair thinning that are difficult to see otherwise.

Results are not reported in this abstract, but it outlines the objective to evaluate the efficacy of microneedling, alone and in combination with other treatments, for pattern hair loss through a systematic review and meta-analysis.

This study found that nicotine may have a therapeutic role in mitigating the exacerbation of infertility conditions connected with alpha-synuclein-related Parkinson’s disease through molecular interactions identified via pathway analysis.

This paper discusses the limitations of clinical trials in evaluating combination treatment regimens for Alzheimer's disease and ischemic stroke, highlighting the extensive resources required for such trials.

This study identified shared genes and pathways in idiopathic pulmonary fibrosis patients with COVID-19, suggesting these may increase mortality and pointing to potential drug targets for treatment.

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.

This study analyzed word dynamics and keyword trends in androgenetic alopecia literature over a decade using natural language processing, revealing hidden patterns and linkages that could inform future research directions and policy decisions.

This study explores the changes in biomedical knowledge over time using temporal and distributional concept representations from scientific literature, highlighting the importance of diachronic analysis in understanding evolving information.

This study reports on a new semantic annotation approach used to identify substances in MEDLINE abstracts responsible for adverse drug reactions, with promising performance shown by a prototype system.

This study found that progression ads are more persuasive for individuals with a weak fresh start mindset, whereas before/after ads are more effective for those with a strong fresh start mindset.

This meta-analysis found that male pattern baldness is associated with an increased risk of aggressive prostate cancer and benign prostatic hyperplasia.

This study found that combining data from different databases provided a more comprehensive estimate of serious adverse drug reactions in a French university hospital, highlighting the limitations of current ADR reporting methods.

This study observed that using the LMNN algorithm improved diagnostic accuracy in identifying biomarker correlations associated with hair loss, suggesting potential for advanced automated diagnostics.

This article examines the emergence of predictive analytics with big data and concludes that Foucault's concept of biopower is now a hybrid involving various technologies to monitor and model behavior and risk.

This study found that using curated biomedical databases as training examples for information extraction tasks in Genome-Wide Association Studies can outperform cost-insensitive methods, demonstrating their potential use without expert annotation.

This research developed a pig graph pangenome assembly of 27 genomes, revealing the importance of structural variations in adaptation and breed-specific traits, with BTF3 identified as a key gene influencing intramuscular fat and meat quality.

This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.

In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.

This bibliography compiles recent publications on comparative and functional genomics across 16 sections and reports no new research findings.

This study found a significant association between montelukast and neuropsychiatric adverse events such as suicidal ideation and depression, suggesting that the drug's interaction with specific genes may contribute to these effects.