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相关概念视频

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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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:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
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Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

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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.
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...
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生物标志物 生物标志物

Wenbo Zhang1, Ming Chen1, Fuxin Zhong1

  • 1The First Affiliated Hospital of Chongqing Medical University, Chongqing, Chongqing, China.

Alzheimer's & dementia : the journal of the Alzheimer's Association
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概括

功能近红外光谱 (fNIRS) 与机器学习相结合,可以准确检测轻度认知障碍 (MCI) 和阿尔茨海默病 (AD). 这种方法为早期诊断提供了对认知任务期间大脑活动变化的洞察力.

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科学领域:

  • 神经科学是一个神经科学.
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • 早期检测认知障碍,包括正常认知 (NC),轻度认知障碍 (MCI) 和阿尔茨海默病 (AD),仍然是一个挑战.
  • 功能近红外光谱学 (fNIRS) 提供了一种非侵入性方法来评估大脑活动.

研究的目的:

  • 在时钟绘图测试 (CDT) 中使用fNIRS区分认知障碍 (NC,MCI,AD) 的阶段.
  • 根据fNIRS数据开发和验证一种用于预测认知进展的机器学习模型.

主要方法:

  • 在CDT期间使用多通道fNIRS系统记录神经激活.
  • 从Oxy-Hb信号中提取特征 (整数,中位数,斜率,差异,平均值),并使用通用线性模型 (GLM) 进行分析.
  • 使用机器学习开发了三类分类模型,并用Shapley增量解释 (SHAP) 解释了特征的重要性.

主要成果:

  • 在NC,MCI和AD组中观察到心脏中心值的显著差异 (P <0.05).
  • 平均中心点值从NC逐渐增加到AD,MCI在特定通道中显示最小值.
  • 结合SMOTE和Extra Trees的机器学习模型实现了0.776的宏观平均AUC,SHAP从背侧前额叶皮层,上旋和布罗卡区域识别了关键特征.

结论:

  • fNIRS与机器学习相结合,可以改善早期发现认知障碍,特别是MCI.
  • 这项研究为与认知衰退相关的神经机制提供了临床见解.