Prescriptions

Advances in In silico predictive models for DDI prediction: Implications and practical applications in drug discovery.

Advances in machine learning and artificial intelligence have recently extended to the quantitative prediction of drug-drug interaction (DDI). Because DDIs arise from diverse mechanisms and the required level of predictive accuracy varies with both the endpoint and the stage of drug development, evaluating their significance and deciding what is needed demand unusually broad expertise-ranging from...

AI-driven identification of a selective dual function inhibitor blocking HK2 activity and HK2-VDAC1 interaction displaying enhanced anticancer efficacy under hypoxia.

Selective inhibition of hexokinase 2 (HK2) represents a promising therapeutic strategy due to the pivotal role of HK2 in the Warburg effect, enhancement of glycolysis and anti-apoptosis via HK2-Voltage-Dependent Anion Channel 1 (VDAC1) protein-protein interaction. Moreover, HK2 initiates glycolysis to generate lactate, hence this central enzyme can be pharmacologically targeted to enhance therapy ...

IDH enzyme inhibition in cancer therapy: mechanisms, mutational insights, and effects of IDH inhibitors in glioma, acute myeloid leukemia and chondrosarcoma.

Isocitrate dehydrogenase (IDH) enzymes have recently emerged as a highly promising target for therapeutic intervention in cancer treatment. Mutations ...

Prospective evaluation of artificial intelligence integration into breast cancer screening in multiple workflow settings: the GEMINI study.

Artificial intelligence (AI) tools can improve breast screening performance but different screening sites have varying needs. Here the GEMINI prospect...

Distinct Prescription Patterns Emerge Years Before ALS Diagnosis: A Nationwide Registry-Based Study.

OBJECTIVE: The prodromal phase of amyotrophic lateral sclerosis (ALS) is poorly defined. We aimed to characterize prescription drug use patterns in th...

Assessment of quantum chemical predictors for anti-colorectal cancer agents using QSAR modeling.

Optimizing the prediction of anti-colorectal cancer agent activity is essential aspect in the identification and creation of medications. Machine lear...

Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.

Artificial intelligence (AI) is increasingly used in mental health, yet its rehabilitation-oriented applications in schizophrenia have not been system...

Identifying Predictors of Early Treatment Intensification in Individuals With Type 2 Diabetes Treated With GLP-1 Receptor Agonists: A Machine Learning-Based Analysis of a Large Real World Cohort.

AIMS: Despite the proven efficacy of GLP-1 receptor agonists (GLP-1 RAs), many patients with type 2 diabetes (T2DM) are not able to achieve glycaemic ...

Prescription‑dose stratification improves deep learning‑based VMAT dose prediction in locally advanced NSCLC.

Volumetric-modulated arc therapy (VMAT) planning for locally advanced non-small cell lung cancer (NSCLC) is an iterative and planner-dependent process...

MML-DTI: Multimanifold Learning with Hyperbolic Graph Neural Networks for Enhanced Drug-Target Interaction Prediction.

Accurately predicting drug-target interactions (DTIs) is crucial for drug discovery, repositioning. However, most deep learning-based DTI models are d...

Improving Maternal Health Equity and Outcomes Through the Development of a Clinician-Informed Algorithm: A Feasibility Study.

OBJECTIVE: Increasing proportions of adverse maternal health outcomes occur in the 12-month postpartum period and could be addressed in outpatient set...

Brand identification of roller-type date stamps based on impression features.

Roller-type date stamps have become increasingly used in recent years within Chinese low-cost manufacturing and packaging sectors to mark production a...

Leveraging machine learning for accurate forecasting of pulmonary tuberculosis epidemics in a coastal city in China.

BACKGROUND: While pulmonary tuberculosis (PTB) remains a leading notifiable cause of death in China, city-level monthly forecasts with sufficient reso...

Adaptive machine learning models for predictive maintenance in industrial internet of things (IIoT) systems.

The research examines how RL and DRL models can be used to enhance the prediction of maintenance needs in the IIoT setting. The purpose is to assess t...

Role of the gut microbiome in shaping drug response in immunocompromised hosts.

BACKGROUND: There is an increasing amount of evidence on microbiome-drug interactions in several clinical settings, including in immunocompromised pat...

Enhancing adverse drug event extraction and summarization for cancer drugs through large language models.

PURPOSE: Cancer treatments such as chemotherapy, targeted therapy, and immunotherapy can effectively combat malignant cells but frequently cause serio...

Trustworthy Compound-Protein Interaction Prediction with Interpretable and Conformalized Cross-Attention Transformers.

Deep learning has accelerated drug discovery by enabling large-scale virtual screening, but current models often act as "black boxes" and provide no f...

A Benchmark Dataset for Machine Learning Surrogates of Pore-Scale CO2-Water Interaction.

Accurately capturing the complex interaction between CO2 and water in porous media at the pore scale is essential for various geoscience applications,...

MoGraphDRP: Multi-omics and graph fusion with bilinear attention for predicting drug sensitivity.

Accurate prediction of drug response in cancer cells is a fundamental step toward achieving precision medicine and designing personalized therapies. I...

Cross-Cultural Differences in Public Discourse on COVID-19 Vaccination in the United States and South Korea: Cross-Sectional Analysis Using Natural Language Processing.

BACKGROUND: The COVID-19 vaccine was introduced as a crucial tool to combat the pandemic. However, concerns about its effectiveness, side effects, and...