Blocking ITGA5 potentiates the efficacy of anti-PD-1 therapy on glioblastoma by remodeling tumor-associated macrophages.

Background: Glioblastoma (GBM) is largely refractory to antibodies against programmed cell death 1 (anti-PD-1) therapy. Fully understanding the cellular heterogeneity and immune adaptations in response to anti-PD-1 therapy is necessary to design more effective immunotherapies for GBM. This study aimed to dissect the molecular mechanisms of specific immunosuppressive subpopulations to drive anti-PD-1 resistance in GBM.

Postoperative radiotherapy improves disease-free survival of EGFR wild-type pN2 non-squamous-cell non-small-cell lung cancer (Nsq-NSCLC) patients after complete resection: a propensity score matching analysis.

Background: The ADAURA study indicated that adjuvant TKI therapy improves survival in postoperative patients with EGFR-mutated (EGFRm) non-small-cell lung cancer (NSCLC), especially in stage III disease. However, the effect of PORT for stage III (N2) NSCLC with different EGFR statuses remains unclear, which we aimed to investigate in the present study.

Methods: Between 2006 and 2019, consecutive patients with pN2 non-squamous cell NSCLC (Nsq-NSCLC) after complete resection and adjuvant chemotherapy or EGFR tyrosine kinase inhibitor (TKI) who had detection of EGFR status were retrospectively analyzed.

Biological age prediction and NAFLD risk assessment: a machine learning model based on a multicenter population in Nanchang, Jiangxi, China.

Background: The objective was to develop a biological age prediction model (NC-BA) for the Chinese population to enrich the relevant studies in this population. And to investigate the association between accelerated age and NAFLD.

Methods: On the basis of the physical examination data of people without noninfectious chronic diseases (PWNCDs) in Nanchang, Jiangxi, China, the biological age measurement method was developed via three feature selection methods (all-subset regression, LASSO regression (LR), and recursive feature elimination) and three machine learning algorithms (generalized linear model (GLM), support vector machine, and deep generalized linear model (deep GLM)).

Discovery of ASGR1 and HMGCR dual-target inhibitors based on supervised learning, molecular docking, molecular dynamics simulations, and biological evaluation.

3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR) and Asialoglycoprotein Receptor 1 (ASGR1) are potential therapeutic targets for atherosclerotic cardiovascular disease (ASCVD). In this study, we employed an innovative approach that combined ligand-based supervised learning, molecular docking, molecular dynamics simulations, and various in-silico techniques. The objective was to effectively screen the Chemdiv and SPECS molecule databases to discover potential inhibitors targeting both HMGCR and ASGR1, resulting in a dual inhibition effect.

System-Level Transformations to Increase Patient Participation in Clinical Trials.

Purpose: Over the course of the COVID-19 pandemic, the Food and Drug Administration allowed cancer clinical trials to make modifications. As policymakers consider sustaining these modifications, understanding patient perspectives on impact is critical.

Methods: This cross-sectional study used survey data collected between August 2021 and September 2021 by the Translational Breast Cancer Research Consortium and December 2022 by Patient Advocate Foundation among female breast cancer survivors.