Chemotherapy

Interferon gamma-induced hub genes and key pathways: A study based on biological network analysis and experimental validation.

By performing a biological network analysis, we identified some hub genes, which were up- or down-re...

Leafing a mark on immunity: Quercetin-3-O-D-glucopyranoside and quercetin-3-O-(2″,6″-digalloyl)-β-D-galactopyranoside-rich Melia azedarach L. extract's immunoinflammatory effects on an sRBC-immunized BALB/c model.

Melia azedarach L. (Meliaceae) (MA) leaves are traditionally used for inflammatory disorders, yet th...

Correction to "The Melamine-Driven Solvation Effect Promotes Oxygen Reduction on Platinum Catalyst: Machine Learning-Aided Free Energy Calculations".

Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy.

BACKGROUND: The early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC)...

Identification of Npas4 as a biomarker for CICI by transcriptomics combined with bioinformatics and machine learning approaches.

Chemotherapy is one of the most successful strategies for treating cancer. Unfortunately, up to 70 %...

An efficient patient's response predicting system using multi-scale dilated ensemble network framework with optimization strategy.

The forecasting of a patient's response to radiotherapy and the likelihood of experiencing harmful l...

Evaluation of Artificial Intelligence Models for Nutritional Symptom Management in Breast Cancer Patients Undergoing Chemotherapy.

The purpose of the study is to determine if artificial intelligence (AI) models could provide dietar...

Artificial intelligence-based muscle analysis risk assessment of treatment-related toxicity in metastatic colorectal cancer.

IntroductionUp to 60% of adults with metastatic colorectal cancer (mCRC) receiving combination cytot...

Training, Validating, and Testing Machine Learning Prediction Models for Endometrial Cancer Recurrence.

PURPOSE: Endometrial cancer (EC) is the most common gynecologic cancer in the United States with ris...

Forecasting optimal treatments in relapsed/refractory mature T- and NK-cell lymphomas: A global PETAL Consortium study.

There is no standard of care in relapsed/refractory T-cell/natural killer-cell lymphomas. Patients o...

Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.

This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using r...

Detection of pathologic complete response using deep neural network-based endoscopic evaluation in patients with esophageal cancer receiving neoadjuvant chemotherapy: a nationwide multicenter retrospective study from 46 Japanese esophageal centers.

BACKGROUND: Detecting pathological complete response (pCR) preoperatively facilitated a non-surgical...

Development and validation of an interpretable machine learning model for diagnosing pathologic complete response in breast cancer.

BACKGROUND: Pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT) is a critic...

Machine learning modeling and response surface methodology driven antioxidant and anticancer activities of chitosan nanoparticle-mediated extracts of Bacopa monnieri.

This study investigates the potential of chitosan nanoparticles (CNPs) in enhancing the bioavailabil...

The association of trimethylamine N-oxide with diabetic retinopathy Pathology: Insights from network toxicology and molecular docking analysis.

Trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite, has emerged as a potential contr...

Inhibiting Dissolution of Platinum with Atomic Rare Earth Bridged by Nitrogen to Boost Alkaline Hydrogen Evolution.

The unfavorable water dissociation and continuous dissolution of Pt single-atom catalysts significan...

Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks.

OBJECTIVE: Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicit...

Histological tumor necrosis predicts decreased survival after neoadjuvant chemotherapy in head and neck squamous cell carcinoma.

OBJECTIVE: Despite growing interest in neoadjuvant therapies, there are no methods to predict radio-...

An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).

The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, r...

Fullerene Network-Buffered Platinum Nanoparticles Toward Efficient and Stable Electrochemical Ammonia Oxidation Reaction for Hydrogen Production.

Green ammonia is a promising hydrogen carrier due to its well-established production, storage, and t...

Self-supervised network predicting neoadjuvant chemoradiotherapy response to locally advanced rectal cancer patients.

Radiographic imaging is a non-invasive technique of considerable importance for evaluating tumor tre...