Publications by authors named "Shaqi He"
Article Synopsis
- - Epicardial adipose tissue (EAT) is important in cardiovascular disease progression, but measuring its volume manually is tough and prone to errors.
- - This study introduces a new deep learning method for EAT quantification using coronary computed tomography angiography (CCTA) that combines data-driven techniques with specific anatomical information.
- - The automated method showed strong agreement with traditional manual measurements, achieving high accuracy for both 2D slices and 3D volumes, suggesting its potential value in clinical settings.
Introduction: Arterial calcification, an independent predictor of cardiovascular events, increases morbidity and mortality in patients with diabetes mellitus (DM), but its mechanisms remain unclear. Extracellular vesicles (EVs) play an important role in intercellular communication. The study investigates the role and potential mechanisms of EVs derived from endothelial cells (ECs) in regulating vascular smooth muscle cell (VSMC) calcification under high glucose (HG) condition, with a goal of developing effective prevention and treatment strategies for diabetic arterial calcification.
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- Cognitive impairment is a major issue for older adults, prompting this study to investigate how diet and nutrients affect brain structure using Mendelian randomization analysis.
- The study found no significant overall causal links between diet and cortical structure but did identify specific dietary factors, such as fat and protein, that correlated with certain brain regions.
- Interestingly, it also suggested that brain thickness could influence dietary choices, indicating a complex interaction between dietary habits and cognitive health, and supporting the idea of a gut-brain connection.
Diagnostic efficiency among Eu-/C-/ACR-TIRADS and S-Detect for thyroid nodules: a systematic review and network meta-analysis.
Front Endocrinol (Lausanne)
September 2023
Background: The performance in evaluating thyroid nodules on ultrasound varies across different risk stratification systems, leading to inconsistency and uncertainty regarding diagnostic sensitivity, specificity, and accuracy.
Objective: Comparing diagnostic performance of detecting thyroid cancer among distinct ultrasound risk stratification systems proposed in the last five years.
Evidence Acquisition: Systematic search was conducted on PubMed, EMBASE, and Web of Science databases to find relevant research up to December 8, 2022, whose study contents contained elucidation of diagnostic performance of any one of the above ultrasound risk stratification systems (European Thyroid Imaging Reporting and Data System[Eu-TIRADS]; American College of Radiology TIRADS [ACR TIRADS]; Chinese version of TIRADS [C-TIRADS]; Computer-aided diagnosis system based on deep learning [S-Detect]).