Publications by authors named "X L Jiang"

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.

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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.

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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).

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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).

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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.

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