Prescriptions

Evolution of Aptamer Materials Maps Exosomal Surface Proteins at the Nano-Bio Interface.

Exosomes are nanoscale extracellular vesicles whose membrane-embedded proteins encode phenotypic information but remain difficult to interrogate due to hydrophobicity, low copy number, and nanoscale curvature. Here, we present AptEx-ID, a chemically guided aptamer evolution strategy that enables direct discovery and quantification of exosomal membrane proteins under native conditions. By introduci...

Comparative Mortality Risk of Aripiprazole, Olanzapine, Quetiapine, and Risperidone in Alzheimer's Disease: A Real‑World Retrospective Cohort Study with Treatment Effect Heterogeneity Analysis.

BACKGROUND: Second-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimer's disease (AD), despite ongoing concerns about their safety. Comparative evidence on mortality risk across specific SGAs remains limited. In this study, we aim to compare all-cause mortality among patients with AD treated with one of the commonly prescribed SGAs and to explo...

Low-data cross-modal adaptation for remote sensing with proxy-enhanced multi-granularity feature caching.

Despite the potential of vision-language models for open-vocabulary recognition, their deployment in remote sensing is limited by the limited effectiv...

Toward Opioid-Free Ambulatory Surgery: A Prospective Study Using Machine Learning to Predict Postoperative Opioid Use.

BACKGROUND: Postoperative opioid use has the risk of dependence and diversion. We developed an opioid-sparing regimen and identified factors associate...

Etiologic diagnosis of seasonal allergic rhinitis supported by artificial intelligence: the @IT-2020 project.

BACKGROUND: A precise etiological diagnosis of seasonal allergic rhinitis (SAR) is essential for a tailored prescription of its only curative treatmen...

LogoXpertNet: a novel lightweight logo classification using deep learning.

Logo classification is crucial in various applications, including brand monitoring, copyright protection, and digital forensics. Traditional computer ...

A Comprehensive Review of the Revolutionary Potential of Blockchain in Safe, Secure, and Sustainable Pharmaceutical Operations.

BACKGROUND: Pharmaceutical research and industrial operations generate vast volumes of sensitive data across drug discovery, formulation development, ...

Enhancing drug synergy in malignant diseases with deep architecture optimization algorithms.

Malignant diseases remain one of the leading causes of death globally. Drug synergy has emerged as an effective approach for treating malignancy, offe...

DeepLMI: Deep Feature Mining with a Globally Enhanced Graph Convolutional Network for Robust lncRNA-miRNA Interaction Prediction.

MOTIVATION: Interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play pivotal roles in gene regulation and disease progression, ...

Multi-modal contrastive learning based on molecular and textual data for drug response prediction.

Accurate prediction of drug response is crucial for advancing precision medicine and optimizing therapeutic strategies. However, current deep learning...

SPARTAN: automated table detection and extraction from documents using advanced OpenCV heuristics and OCR techniques.

The rapid growth of born-digital PDF documents has amplified the demand for fast, precise tabular data extraction on an industrial scale. State-of-the...

Explainable drug side effect prediction in central neural system via biologically informed graph neural network.

The early detection of potential side effects (SEs) is a critical yet formidable challenge within the realms of drug development and patient healthcar...

Bone Metastasis Detection at CT with Deep Learning Models Trained Using Multicenter, Multimodal Reference Standards: Development and Evaluation.

Purpose To develop and validate deep learning models for detecting bone metastases on abdominal and thoracic CT scans, considering lesion visibility, ...

Transformer-based deep learning model for real-time prediction of intraoperative hypotension using dynamic time-series vital signs: A retrospective study.

BACKGROUND: The clinical importance of transient intraoperative hypotension (IOH) remains debated, and existing models often rely on high-resolution w...

AI-generated Feedback Following Social Robotic Virtual Patient Interactions and Medical Student Performance: Nonrandomized Quasi-Experimental Study.

BACKGROUND: Virtual patients (VPs) demonstrate effectiveness in improving clinical reasoning skills; however, traditional VP platforms often lack indi...

DeepSTFSynergy: A multi-scale structural information fusion method for personalized drug combination prediction.

Efficiently predicting drug synergy is crucial for developing personalized cancer combination therapy regimens. However, existing methods primarily fo...

OptiSyn: an interpretable, multi-omics-driven graph convolutional network framework for synergy-oriented drug combination design in disease treatment.

BACKGROUND: Bioinformatics and large-scale computational modelling have emerged as essential research fields in modern biomedical science, enabling dr...

Multi-output LSTM-based prediction of postoperative delirium: integrating baseline and perioperative data for enhanced risk stratification in older spine surgery patients.

INTRODUCTION: Postoperative delirium (POD) adversely affects clinical outcomes among older adults undergoing spine surgery. However, existing predicti...

Predicting CT-based coronal plane knee phenotype parameters using imageless navigation and machine learning.

BACKGROUND: Coronal plane alignment of the knee (CPAK) categorizes knee phenotypes according to joint line obliquity (JLO) and the arithmetic hip‒knee...

PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.

MOTIVATION: The precise prediction of peptide-protein interaction (PepPI) is a core support for promoting breakthroughs in peptide drug research, as w...