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Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients
Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. O...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9846646/ https://www.ncbi.nlm.nih.gov/pubmed/36684609 http://dx.doi.org/10.3389/fcvm.2022.1059543 |
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author | Li, Hongyu Sun, Xinti Li, Zesheng Zhao, Ruiping Li, Meng Hu, Taohong |
author_facet | Li, Hongyu Sun, Xinti Li, Zesheng Zhao, Ruiping Li, Meng Hu, Taohong |
author_sort | Li, Hongyu |
collection | PubMed |
description | Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients. |
format | Online Article Text |
id | pubmed-9846646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98466462023-01-19 Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients Li, Hongyu Sun, Xinti Li, Zesheng Zhao, Ruiping Li, Meng Hu, Taohong Front Cardiovasc Med Cardiovascular Medicine Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients. Frontiers Media S.A. 2023-01-04 /pmc/articles/PMC9846646/ /pubmed/36684609 http://dx.doi.org/10.3389/fcvm.2022.1059543 Text en Copyright © 2023 Li, Sun, Li, Zhao, Li and Hu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cardiovascular Medicine Li, Hongyu Sun, Xinti Li, Zesheng Zhao, Ruiping Li, Meng Hu, Taohong Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients |
title | Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients |
title_full | Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients |
title_fullStr | Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients |
title_full_unstemmed | Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients |
title_short | Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients |
title_sort | machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9846646/ https://www.ncbi.nlm.nih.gov/pubmed/36684609 http://dx.doi.org/10.3389/fcvm.2022.1059543 |
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