CT-Based radiomics nomogram of lung and mediastinal features to identify cardiovascular disease in chronic obstructive pulmonary disease: a multicenter study.

Rationale And Objectives: To investigate the performance of two diagnostic models based on CT-derived lung and mediastinum radiomics nomograms for identifying cardiovascular disease (CVD) in Chronic Obstructive Pulmonary Disease (COPD) patients.

Materials And Methods: Hospitalized participants with COPD were retrospectively recruited between September 2015 and April 2023. Clinical data and visual coronary artery calcium score (CACS) were collected.

Oral ADC189 for Adults and Adolescents with Uncomplicated Influenza.

Objectives: ADC189 is a novel anti-influenza virus inhibitor. In this study, we aimed to evaluated the safety and efficacy of ADC189 in outpatients with uncomplicated influenza infection.

Methods: In the phase 2 trial, we assigned patients in a 2:2:1 ratio to receive either single dose 15-mg or 45-mg of ADC189 or placebo.

Dietary timing enhances exercise by modulating fat-muscle crosstalk via adipocyte AMPKα2 signaling.

Feeding rhythms regulate exercise performance and muscle energy metabolism. However, the mechanisms regulating adipocyte functions remain unclear. Here, using multi-omics analyses, involving (phospho-)proteomics and lipidomics, we found that day-restricted feeding (DRF) regulates diurnal rhythms of the mitochondrial proteome, neutral lipidome, and nutrient-sensing pathways in mouse gonadal white adipose tissue (GWAT).

Comparison of three approaches for adrenal venous sampling in patients with primary aldosteronism.

Purpose: To compare the feasibility and safety of three approaches for bilateral adrenal venous sampling (AVS) and their influence on the outcomes of adrenalectomy for dominant lateral primary aldosteronism (PA).

Methods: 182 PA patients who underwent AVS at Fuwai Hospital between January 2022 and March 2024 were enrolled. According to the puncture access, patients were divided into three groups: simultaneous AVS via antecubital approach group (Group A, N = 48), simultaneous AVS via femoral approach group (Group B, N = 44) and sequential AVS via antecubital approach group (Group C, N = 90).

Reimagining cancer tissue classification: a multi-scale framework based on multi-instance learning for whole slide image classification.

In cancer pathology diagnosis, analyzing Whole Slide Images (WSI) encounters challenges like invalid data, varying tissue features at different magnifications, and numerous hard samples. Multiple Instance Learning (MIL) is a powerful tool for addressing weakly supervised classification in WSI-based pathology diagnosis. However, existing MIL frameworks cannot simultaneously tackle these issues.