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Updated: Sep 10, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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基于血液生物标志物的2019年冠状病毒病严重性的预测模型:一项回顾性研究

Liu Xiaoyan1, Bao Zhongying1, Duan Shuhong1

  • 1Department of Infectious Diseases, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.

Frontiers in medicine
|August 27, 2025
PubMed
概括

这项研究使用C反应性蛋白和中性粒细胞计数等血液生物标志物开发了COVID-19严重性预测模型. 该模型准确地识别了2019年严重冠状病毒疾病的高风险患者,有助于临床决策.

关键词:
美国血液中的生物标志物预测模型后期的队列分析严重程度

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
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Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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