心脏骤停后的多组学生物标志物

IF 2.8 Q2 CRITICAL CARE MEDICINE
Victoria Stopa, Gabriele Lileikyte, Anahita Bakochi, Prasoon Agarwal, Rasmus Beske, Pascal Stammet, Christian Hassager, Filip Årman, Niklas Nielsen, Yvan Devaux
{"title":"心脏骤停后的多组学生物标志物","authors":"Victoria Stopa, Gabriele Lileikyte, Anahita Bakochi, Prasoon Agarwal, Rasmus Beske, Pascal Stammet, Christian Hassager, Filip Årman, Niklas Nielsen, Yvan Devaux","doi":"10.1186/s40635-024-00675-y","DOIUrl":null,"url":null,"abstract":"<p><p>Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after cardiac arrest. This review article delves into the current knowledge of cardiac arrest biomarkers across various omic fields and suggests directions for future research aiming to integrate multiple omics data layers to improve outcome prediction and cardiac arrest patient's care.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"12 1","pages":"83"},"PeriodicalIF":2.8000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11436561/pdf/","citationCount":"0","resultStr":"{\"title\":\"Multiomic biomarkers after cardiac arrest.\",\"authors\":\"Victoria Stopa, Gabriele Lileikyte, Anahita Bakochi, Prasoon Agarwal, Rasmus Beske, Pascal Stammet, Christian Hassager, Filip Årman, Niklas Nielsen, Yvan Devaux\",\"doi\":\"10.1186/s40635-024-00675-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after cardiac arrest. This review article delves into the current knowledge of cardiac arrest biomarkers across various omic fields and suggests directions for future research aiming to integrate multiple omics data layers to improve outcome prediction and cardiac arrest patient's care.</p>\",\"PeriodicalId\":13750,\"journal\":{\"name\":\"Intensive Care Medicine Experimental\",\"volume\":\"12 1\",\"pages\":\"83\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11436561/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Intensive Care Medicine Experimental\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s40635-024-00675-y\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CRITICAL CARE MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intensive Care Medicine Experimental","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40635-024-00675-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
引用次数: 0

摘要

心脏骤停是指心脏功能突然停止,导致重要器官突然失去血流和氧气。这种危及生命的紧急情况需要立即进行医疗干预,并可能导致严重的神经损伤或死亡。目前已有预测神经系统结果的方法和生物标志物,但缺乏准确性。这种方法可以实现个性化医疗保健,并有助于临床决策。为确定心脏骤停的预后生物标志物,已经开展了大量研究。随着可将不同层次的 omics 数据结合起来的技术的出现,在人工智能和机器学习的帮助下,有可能将多组学特征用作心脏骤停后的预后生物标志物。这篇综述文章深入探讨了目前不同物组学领域中有关心脏骤停生物标志物的知识,并提出了未来的研究方向,旨在整合多个物组学数据层以改善预后预测和心脏骤停患者的护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiomic biomarkers after cardiac arrest.

Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after cardiac arrest. This review article delves into the current knowledge of cardiac arrest biomarkers across various omic fields and suggests directions for future research aiming to integrate multiple omics data layers to improve outcome prediction and cardiac arrest patient's care.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Intensive Care Medicine Experimental
Intensive Care Medicine Experimental CRITICAL CARE MEDICINE-
CiteScore
5.10
自引率
2.90%
发文量
48
审稿时长
13 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信