Associations between inflammation-related dietary patterns and obesity: A cohort study among Tibetan adults.

Substantial changes resulting from the interaction of environmental and dietary factors contribute to an increased risk of obesity, while their specific associations with obesity remain unclear. Identify inflammation-related dietary patterns (DPs) and explore their associations with obesity among urbanized Tibetan adults under significant environmental and dietary changes.Totally, 1826 subjects from the suburbs of Golmud City were enrolled in an open cohort study, of which 514 were followed up.

Measuring HSD17β13 protein turnover in mouse liver with DO metabolic labeling and hybrid LC-MS.

Background: Metabolic labeling with heavy water (DO) followed by LC-MS has become a powerful tool for studying protein turnover . Developing a quantitative method to measure partially labeled low-abundance proteins poses many challenges because heavy isotopomers of peptides, especially their changes through deuterium labeling, are difficult to detect.

Methods: A workflow that coupled immunocapture and LC-high-resolution MS to determine the synthesis rate of HSD17β13 protein in mouse liver was presented.

Urban-rural disparities in the association between social trust patterns and changes in depressive symptoms: longitudinal evidence from an elderly Chinese population.

Objective: To examine the relationship between social trust and depressive symptoms among China's elderly, placing special emphasis on the disparities between urban and rural settings.

Design: We employed latent profile analysis to categorise individual patterns of social trust. Subsequently, multiple linear regression analysis was employed to determine if there was an association between these identified social trust patterns and depressive symptoms.

: Empowering artificial intelligence for respiratory diseases diagnosis based on electronic health records, a multicenter study.

Respiratory diseases pose a significant global health burden, with challenges in early and accurate diagnosis due to overlapping clinical symptoms, which often leads to misdiagnosis or delayed treatment. To address this issue, we developed , an artificial intelligence (AI)-based diagnostic system that utilizes natural language processing (NLP) to extract key clinical features from electronic health records (EHRs) for the accurate classification of respiratory diseases. This study employed a large cohort of 31,267 EHRs from multiple centers for model training and internal testing.

Seasonal Variation in Chemical Composition and Antioxidant and Antibacterial Activity of Essential Oil from Leaves.

has been extensively utilized in traditional medicine systems worldwide. The essential oil (EO) content and composition are influenced by various external and internal factors, such as climate and harvest season, making it vital to determine the optimal harvest period for high-quality EO production. This study is the first to evaluate the chemical profiles, as well as the antioxidant and antibacterial activities, of leaf oil across the four seasons.