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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

749
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...
749
Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

516
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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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

Yajing Zhu1, Bing Zhang1

  • 1Nanjing Drum Tower Hospital, Affiliated Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China.

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

深度学习模型可以使用休息状态fMRI在主观认知衰退 (SCD) 个体中预测嗅觉任务期间的大脑活动. 这种方法有助于了解早期阿尔茨海默病 (AD) 嗅觉变化和神经机制.

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科学领域:

  • 神经成像是一种神经成像.
  • 人工智能的人工智能
  • 认知科学 认知科学

背景情况:

  • 嗅觉功能障碍是阿尔茨海默病 (AD) 的早期临床症状,与认知能力下降有关.
  • 主观认知衰退 (SCD) 代表了早期AD风险组.
  • 获取基于嗅觉任务的fMRI数据是一项挑战.

研究的目的:

  • 在SCD个体使用静止状态fMRI (rs-fMRI) 预测嗅觉任务期间的大脑活动.
  • 了解阿尔茨海默氏症早期嗅觉功能变化及其神经支.
  • 为了利用深度学习来分析大规模的rs-fMRI数据.

主要方法:

  • 利用一个端到端的深度学习模型.
  • 基于rs-fMRI数据的嗅觉神经回路的预测功能活动.
  • 使用皮尔森相关系数 (r) 评估预测准确性.

主要成果:

  • 取得了显著的个人预测准确性 (r = 0.879,p < 0.001).
  • 证明了显著的小组预测准确性 (r = 0.470,p = 0.02).
  • 预测和实际激活z-score之间确实存在很强的相关性.

结论:

  • 深度学习模型有效地预测SCD个体对气味刺激的反应中大脑活动.
  • 这种方法提供了一种可行的方法,用于研究使用大样本 rs-fMRI 数据的嗅觉功能机制.
  • 提供了关于与AD风险相关的早期神经变化的见解.