Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

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...
516

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

DANCE: Deep Learning-Assisted Analysis of ProteiN Sequences Using Chaos Enhanced Kaleidoscopic Images.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

An explainable AI approach for mapping multivariate regional brain age and clinical severity patterns in Alzheimer's disease.

Biology methods & protocols·2025
Same author

Multivariate whole brain neurodegenerative-cognitive-clinical severity mapping in the Alzheimer's disease continuum using explainable AI.

medRxiv : the preprint server for health sciences·2025
Same author

Characterizing multivariate regional hubs for schizophrenia classification, sex differences, and brain age estimation using explainable AI.

medRxiv : the preprint server for health sciences·2025
Same author

AgeNet-SHAP: An explainable AI approach for optimally mapping multivariate regional brain age and clinical severity patterns in Alzheimer's disease.

medRxiv : the preprint server for health sciences·2025
Same journal

Multimorbidity burden and patterns associated with DeepBrainNet-derived brain-age gap in dementia-free older adults: A community-based study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Reply to "Shifting the emphasis of brain health literacy from individuals to systems to reduce inequalities".

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Shifting the emphasis of brain health literacy from individuals to systems to reduce inequalities.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Correlates and predictors of self-efficacy among dementia caregivers: D-CARE findings.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

What should convince a clinician of disease modification in Alzheimer's disease clinical trials?

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Primary cilia-extracellular vesicle crosstalk in Alzheimer's disease: Emerging mechanisms and biomarker potential.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
查看所有相关文章

相关实验视频

Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

37.1K

生物标志物 生物标志物

Taslim Murad1, Hui-Yuan Miao1, Deepa S Thakuri2

  • 1Washington University in St. Louis, St. Louis, MO, USA.

Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的可解释的人工智能 (AI) 方法DL-SHAP,使用脑MRI扫描来预测阿尔茨海默病 (AD) 的认知衰退. 人工智能模型准确地识别了与认知功能和疾病严重程度相关的关键大脑区域.

更多相关视频

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans
08:14

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Published on: April 28, 2023

702

相关实验视频

Last Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

37.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans
08:14

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Published on: April 28, 2023

702

科学领域:

  • 神经成像和人工智能的人工智能
  • 阿尔茨海默氏症疾病研究研究
  • 大脑体积测量分析

背景情况:

  • 通过MRI检测到的大脑体积变化与阿尔茨海默病 (AD) 的认知衰退相关.
  • 之前使用单变量方法的研究探索了个体大脑区域与认知的关联.
  • 整个大脑 (WB) 体积变化与阿尔茨海默氏症的认知之间的多变量关系仍未得到充分探索.

研究的目的:

  • 通过使用可解释的人工智能,研究AD连续体中的复杂多变量WB认知关系.
  • 从MRI数据中预测全球认知 (迷你心理状态检查 - MMSE) 使用WB区域特征.
  • 为了确定对认知预测和AD严重程度 (临床痴呆症评分-盒子总和-CDR-SB) 有贡献的关键大脑区域.

主要方法:

  • 使用机器学习 (ML) 和深度学习 (DL) 模型进行认知预测.
  • 集成了一个最佳的DL模型与特征重要性 (DL-SHAP) 的Shapley添加式扩展 (SHAP).
  • 在半模拟 (n=1108) 和实验 (n=668) MRI 数据集上验证了DL-SHAP,评估了MMSE和CDR-SB.

主要成果:

  • 在MMSE预测方面,DL模型显著优于传统ML模型.
  • 在半模拟数据 (斯皮尔曼相关性=0.94) 和实验数据 (斯皮尔曼相关性=0.96) 上,DL-SHAP表现出强大的性能.
  • 确定与MMSE预测和AD严重程度相关的等级主导的大脑区域.

结论:

  • 可解释的人工智能方法DL-SHAP有效地从大型MRI数据集预测全球认知.
  • 在AD连续中,DL-SHAP成功地确定了多变量全脑认知关系.
  • 该方法为预测临床严重性提供了令人信服的证据,并突出了关键的大脑区域.