Background: The upper reference limit of normal (ULN) of cardiac troponin (cTn) for older adults can be higher than for young adults, while the same ULN is used for both older and young adults in the current clinical practice.
Methods: In this multicentre longitudinal cohort study, non-acute myocardial infarction (non-AMI) inpatients with at least two cTn concentrations hospitalised between 2013 and 2022 in the Tianjin Health and Medical Data Platform were included. Multivariable Cox proportional hazards and landmark regression models were used to estimate the risk of in-hospital, 30-day and 1-year mortality in different cTn groups (normal, stable minor elevation (1-2×ULN with variation ≤20%), acute minor elevation (1-2×ULN with variation >20%) and apparent elevation (>2×ULN)).
Focal adhesions (FAs) are force-bearing multiprotein complexes, whose nanoscale organization and signaling are essential for cell growth and differentiation. However, the specific organization of FA components to exert spatiotemporal activation of FA proteins for force sensing and transduction remains unclear. In this study, we unveil the intricacies of FA protein nanoarchitecture and that its dynamics are coordinated by a molecular scaffold protein, BNIP-2, to initiate downstream signal transduction for cardiomyoblast differentiation.
View Article and Find Full Text PDFThere is a new awareness of the widespread nature of metabolic dysfunction-associated steatotic liver disease (MASLD) and its connection to cardiovascular disease (CVD). This has catalyzed collaboration between cardiologists, hepatologists, endocrinologists, and the wider multidisciplinary team to address the need for earlier identification of those with MASLD who are at increased risk for CVD. The overlap in the pathophysiologic processes and parallel prevalence of CVD, metabolic syndrome, and MASLD highlight the multisystem consequences of poor cardiovascular-liver-metabolic health.
View Article and Find Full Text PDFTo date, over 40 epigenetic and 300 epitranscriptomic modifications have been identified. However, current short-read sequencing-based experimental methods can detect <10% of these modifications. Integrating long-read sequencing technologies with advanced computational approaches, including statistical analysis and machine learning, offers a promising new frontier to address this challenge.
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