Prescriptions

Fabric defect detection using fine-tuned Yolo-12.

Defect identification is critical for ensuring the reliability and price of fabrics. Defective fabrics result in significant waste and losses. Automatic defect identification using the use of deep learning is a faster and more efficient way to analyze fabric quality, replacing human inspection. Furthermore, both plain and printed textiles are produced concurrently in enterprises; hence, one design...

Natural Language Processing to Identify Substance Use in Electronic Health Records: A Scoping Review.

BACKGROUND: This scoping review aimed to characterize natural language processing (NLP) techniques deployed for identifying substance use in electronic health records (EHRs) and to compare the performance of these techniques by substance type. METHODS: We conducted a systematic search of PubMed, the Cochrane Library, Embase, Web of Science, ACM Digital Library, IEEE Xplore, and Scopus for peer-rev...

Mandibular Angle Bone Appositions in Bruxism: A Deep Learning-Based Detection and Staging Study.

This study aims to automatically detect, classify, and stage bone apposition changes in the mandibular angle region associated with bruxism using pano...

Digital chameleons of the food environment: an exploratory analysis of the shapeshifting nature of dark kitchens in Australia and their regulatory challenges.

BACKGROUND/AIM: Dark kitchens are food service models that do not have a physical space for patrons and mostly sell through online food delivery servi...

DR.DEGMON: self-explainable deep neural network for drug-induced cell viability prediction incorporating differentially expressed genes and gene ontology.

BACKGROUND: Accurate prediction of cancer drug responses is essential for advancing cancer treatment strategies and drug development. With the increas...

AI-driven computational drug design: tools, workflow and challenges.

Drug discovery is a time-consuming and resource-intensive process with a development period of more than ten years and a clinical attrition rate of mo...

Tailored injectable nanocomposite hydrogels for high-performance biomedical applications.

Conventional hydrogel systems for biomedical applications face critical limitations in mechanical robustness, therapeutic functionality, and responsiv...

A Structure-Aware Multimodal Framework for Drug-Target Interaction Prediction via Heterogeneous Graph Learning.

Predicting drug-target interactions is critical for drug discovery, yet many deep learning methods overlook atom-residue-level relationships. We propo...

Artificial intelligence for pediatric neuroimaging.

Artificial intelligence (AI) is revolutionizing health care, particularly in radiology for which large retrospective electronic datasets are naturally...

Predicting the absence of World Health Organization fungal priority pathogens in agricultural soils using machine learning and ITS metabarcoding.

Environmental fungal pathogens relevant to human and animal health pose significant risks, particularly in regions with intensive farming and climate ...

Post COVID-19 condition diagnosis among older people: findings from Swedish national register data.

INTRODUCTION: Post COVID-19 condition (PCC) denotes the persistence of symptoms following Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)...

Multimodal prediction of metachronous liver metastasis in stage I-III colorectal cancer patients: multicenter cohort study employing machine learning.

OBJECTIVE: Postoperative metachronous liver metastasis (MLM) in colorectal cancer (CRC) patients is often difficult to predict using conventional clin...

Clinical Evaluation of an AI-Assisted Decision Support System for General Anesthesia Management Based on Data From 6 Centers: Comparative Study.

BACKGROUND: AI is rapidly transforming medical practice, with emerging applications in perioperative care and anesthesiology. However, the clinical im...

Deep cross-modal representation fusion learning for enhanced drug target affinity prediction.

Prediction of Drug Target Affinity (DTA) is essential for accelerating computational drug discovery and reducing experimental costs. However, traditio...

Robust Prediction of Drug Interactions using Chemical Descriptors.

Polypharmacy requires accurate prediction of drug-drug interactions to prevent adverse events, yet existing models often lack reliability and explaina...

Pre-operative concerns and post-operative satisfaction: comparing attitudes toward bariatric surgery and medication intervention for weight loss on Reddit.

BACKGROUND: Patient perceptions influence the success of bariatric surgery and pharmacologic weight loss therapies, yet many concerns never reach prov...

Bioinformatics reveals the prognostic potential of manganese metabolism-related genes in lung adenocarcinoma.

Manganese metabolism may be involved in the malignant progression of lung adenocarcinoma (LUAD). Clarifying the roles of manganese metabolism-related ...

Deep Learning-Based Prediction of Drug Bitterness Using Molecular Structures.

Bitterness is a major cause of poor medication adherence, particularly in pediatric patients. Although several machine learning models have been devel...

Exploration peptide-GPCR specificity beyond homology and tertiary structure: computational insights into deorphanization and molecular mechanisms.

Peptide-responsive G protein-coupled receptors (GPCRs) play pivotal roles in a wide variety of physiological regulatory systems in animals. Despite th...

Symbolic and domain-generalized machine learning for interpretable solubility modeling in supercritical CO₂.

Accurate prediction of drug solubility in supercritical CO₂ remains challenging due to the limited generalizability of compound-specific correlations ...