Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study introduced the KRDQN predictive framework, which outperformed existing methods in predicting adverse drug reactions and provided interpretable insights into drug mechanisms, aiding pharmacovigilance and clinical decision-making.

This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

In this bibliometric analysis, researchers observed that precision medicine approaches, such as individualized interventions and biomarker-guided subtyping, are increasingly integrated into prediabetes research, highlighting shifts toward using multi-omics data and artificial intelligence for patient stratification and prevention strategies.

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 clustering model to improve detection of drug-induced cognitive disorder risk signals, finding that it identified drugs with moderate risk signals, like Carbidopa/Levodopa, missed by traditional methods, enhancing clinical assessment comprehensiveness.

This study introduces a novel computational model using fuzzy logic and multi-criteria decision-making techniques to create a triage system for androgenetic alopecia management, stratifying patients into seven severity levels and aiding in resource allocation and treatment planning.

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 found that Cochrane systematic reviews on drug safety had higher methodological quality than non-Cochrane reviews, with the type of review and disease area affecting quality.

This study outlines the Human Gene Correlation Analysis tool, which classifies human genes by coexpression levels and identifies overrepresented annotation terms in correlated gene groups, with no new clinical results reported.

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 article has no abstract available, so it presents no findings or conclusions.

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

This study used artificial neural networks to predict hair loss by analyzing factors like gender and zinc deficiency, suggesting neural networks may effectively model hair loss prediction.

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.

This abstract describes Upubscience Publisher as a prominent publisher of over 40 open access, peer-reviewed journals across numerous academic fields, supported by an editorial team of leading researchers. Results are not reported in this description.

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.

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 found a causal link between eight blood metabolites and allergic conjunctivitis, highlighting their potential role in predicting and preventing the condition.

This study developed a deep-learning model that accurately diagnosed alopecia areata with an accuracy of 88.92% and distinguished its activity levels with an accuracy of 83.33%, highlighting the potential for artificial intelligence in improving the diagnosis and treatment of this autoimmune hair loss condition.

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

The BAYONET trial is a phase II study designed to assess the efficacy and safety of combining encorafenib, binimetinib, and cetuximab for patients with BRAF V600E-mutant metastatic colorectal cancer that is resistant to encorafenib plus cetuximab; results are not yet reported.

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 review discussed recent research on using machine learning to predict mental disorders, reporting that Adaboost could predict depression with 92.5% accuracy and 93.6% specificity, while other models like XGBoost and RNN were applied for post-stroke depression and EEG-based depression detection, respectively.

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 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 examined the genetic basis of alopecia areata by analyzing gene expression differences between patients and healthy controls, identifying critical pathways, hub genes, transcription factors, and miRNAs implicated in the disease, and suggesting potential targets for treatment.