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相关概念视频

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

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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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相关实验视频

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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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生物标志物 生物标志物

Christopher J Patterson1, Nikhil J Dhinagar2, Emma J Gleave2

  • 1Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina Del Rey, CA, USA.

Alzheimer's & dementia : the journal of the Alzheimer's Association
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概括
此摘要是机器生成的。

预测阿尔茨海默氏病在轻度认知障碍患者的进展是至关重要的. 使用大脑MRI的3D卷积神经网络 (CNN) 在预测两年内认知衰退方面表现有前途.

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科学领域:

  • 神经成像和人工智能的人工智能
  • 神经病学和老年病学

背景情况:

  • 轻度认知障碍 (MCI) 影响数以百万计的老年人,每年有很大比例的人发展为阿尔茨海默病 (AD).
  • 准确预测MCI到AD进展对于及时干预,临床试验设计和了解疾病机制至关重要.

研究的目的:

  • 开发和验证一个3D卷积神经网络 (CNN),用于预测MCI患者的认知衰退.
  • 为了预测临床痴呆症评分表 (sobCDR) 框总和得分在两年内发生的变化.

主要方法:

  • 对三个独立队列 (ADNI,OASIS-3,NACC) 的分析,包括3D T1加权脑MRI和纵向sobCDR得分的参与者.
  • 使用3D DenseNet-121 CNN,使用MRI数据预测未来的sobCDR分数.
  • 来自CNN的预测被整合到线性混合效应模型中,以评估其在预测认知衰退方面的附加值.

主要成果:

  • 结合图像衍生预测的预测模型,准确地预测了两年后的sobCDR得分,平均绝对误差约为1点.
  • 纳入神经成像数据显著提高了认知衰退预测的准确性.

结论:

  • 深度学习方法,特别是使用神经成像的CNN,显示出在神经退行性疾病中增强预后模型的巨大潜力.
  • 未来的研究应该探索先进的深度学习技术,多模式神经成像数据 (例如PET,扩散MRI) 和新型数据融合策略.