Hemophilia

Development and Validation of a Machine Learning-Based Model Using CT Radiomics for Predicting Immune Checkpoint Inhibitor-related Pneumonitis in Patients With NSCLC Receiving Anti-PD1 Immunotherapy: A Multicenter Retrospective CaseControl Study.

RATIONALE AND OBJECTIVES: This study aimed to develop and evaluate a radiomics-based model combined ...

Voltammetric measurement of catechol-O-methyltransferase inhibitor tolcapone in the pharmaceutical form on the boron-doped diamond electrode.

This study presents an electroanalytical approach to measure the catechol-O-methyltransferase (COMT)...

Correlation Between Statin Use and Symptomatic Venous Thromboembolism Incidence in Patients With Ankle Fracture: A Machine Learning Approach.

BACKGROUND: Identifying factors that correlate with the incidence of venous thromboembolism (VTE) ha...

Chewing gum prophylaxis for postoperative nausea and vomiting in the intensive care unit: a pilot randomised controlled trial.

To test the effectiveness of chewing gum in the prophylaxis of postoperative nausea and vomiting (P...

Deep learning-assisted high-content screening identifies isoliquiritigenin as an inhibitor of DNA double-strand breaks for preventing doxorubicin-induced cardiotoxicity.

BACKGROUND: Anthracyclines including doxorubicin are essential components of many cancer chemotherap...

TFEB/LAMP2 contributes to PM-induced autophagy-lysosome dysfunction and alpha-synuclein dysregulation in astrocytes.

Atmospheric particulate matter (PM) exacerbates the risk factor for Alzheimer's and Parkinson's dise...

α-Amylase inhibitory, antioxidant and emulsification potential of glycoproteinaceous bioactive molecule from .

The bioactive components of microbial origin have been extensively applied to restrict the enormous ...

A large-scale evaluation of NLP-derived chemical-gene/protein relationships from the scientific literature: Implications for knowledge graph construction.

One area of active research is the use of natural language processing (NLP) to mine biomedical texts...

Use of Deep-Learning Assisted Assessment of Cardiac Parameters in Zebrafish to Discover Cyanidin Chloride as a Novel Keap1 Inhibitor Against Doxorubicin-Induced Cardiotoxicity.

Doxorubicin-induced cardiomyopathy (DIC) brings tough clinical challenges as well as continued deman...

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibro...

Artificial intelligence-assisted repurposing of lubiprostone alleviates tubulointerstitial fibrosis.

Tubulointerstitial fibrosis (TIF) is the most prominent cause which leads to chronic kidney disease ...

Machine learning and biological evaluation-based identification of a potential MMP-9 inhibitor, effective against ovarian cancer cells SKOV3.

MMP-9, also known as gelatinase B, is a zinc-metalloproteinase family protein that plays a key role ...

How can artificial intelligence help rehabilitation programs for people with hemophilia in the coming decades?

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.

BACKGROUND: The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-...

A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database.

Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery effor...

Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M) using molecular docking and deep learning methods.

Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused...

Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors.

Identifying hit compounds is an important step in drug development. Unfortunately, this process cont...

Strategy toward Kinase-Selective Drug Discovery.

Kinase drug selectivity is the ground challenge in cancer research. Due to the structurally similar ...

Development, validation, and evaluation of a deep learning model to screen cyclin-dependent kinase 12 inhibitors in cancers.

Deep learning-based in silico alternatives have been demonstrated to be of significant importance in...

A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.

BACKGROUND: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a firs...

Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer.

AIM: To develop and validate a nomogram model that combines computed tomography (CT)-based radiologi...