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

Gauri Darekar1, Taslim Murad1, Hui-Yuan Miao1

  • 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。

这项研究使用人工智能 (AI) 来从MRI扫描中预测大脑年龄,确定与正常衰老和阿尔茨海默病 (AD) 相关的关键大脑区域. 人工智能模型准确地预测了大脑年龄,并揭示了与AD严重程度相关的模式.

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:

  • 神经成像是一种神经成像.
  • 人工智能的人工智能
  • 老年学是一门学科.

背景情况:

  • 年龄是轻度认知障碍和阿尔茨海默病 (MCI/AD) 的主要危险因素.
  • 了解正常的大脑衰老和MCI/AD需要识别大脑年龄模式.
  • 以前的研究集中在个别的大脑区域上,忽视了多变量关联.

研究的目的:

  • 开发和验证人工智能模型,使用MRI数据预测大脑年龄.
  • 为了确定与大脑年龄预测相关的显著多变量大脑区域.
  • 探索大脑年龄模式与MCI/AD临床严重程度之间的关系.

主要方法:

  • 利用深度学习 (AgeNet) 和机器学习模型从区域大脑体积来估计大脑年龄.
  • 将最佳AI模型与SHAP集成,用于识别关键的多变量大脑区域.
  • 在模拟和实验MRI数据集 (n=668) 上验证了方法.

主要成果:

  • 深度学习模型 (AgeNet) 在大脑年龄预测方面显著优于传统模型.
  • 在模拟中,AgeNet-SHAP成功地确定了大脑年龄的关键预测因素.
  • 与对照组相比,在阿尔茨海默氏症患者中观察到大脑结构的显著,广泛的区域差异.

结论:

  • 可解释的人工智能 (AgeNet-SHAP) 能够有效地预测大脑年龄,并揭示多变量大脑区域的关联.
  • 该方法确定了与阿尔茨海默病严重程度相关的大脑区域.
  • 这些发现支持人工智能驱动的诊断,预后和神经退行性疾病个性化医学方法.