Scholarlink

<p>At present, there are currently no molecular biomarkers for the early diagnosis of sepsis cardiomyopathy (SCM) in clinical practice. This study focuses on an in-depth examination of the DNA hydroxymethylation profiles within plasma extracellular vesicles and explores potential molecular biomarkers during the process of SCM. The 5hmC-Seal sequencing technology was utilized to examine the hydroxymethylation modifications of extracellular vesicles DNAs in 13 patients with septic cardiomyopathy, 18 patients with sepsis without cardiomyopathy, and 8 patients without sepsis. Additionally, a diagnostic model was constructed using machine learning methods based on the differential hydroxymethylation modifications to screen for candidate biomarkers. The accuracy of the diagnostic model was 0.962, with a sensitivity and specificity of 92.3% and 88.89%, respectively.&#xa0;Furthermore, the diagnostic accuracy was validated using the GEO dataset, with an accuracy rate reaching 1 (GSE79962 and GSE66890), and the differential diagnostic accuracy rates also reached 0.959 and 0.944 (GSE79962). Together, the results suggest that extracellular vesicles DNAs hydroxymethylation markers can be used for diagnosis of septic cardiomyopathy.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

5-Hydroxymethylcytosine signatures as diagnostic biomarkers for septic cardiomyopathy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月4日

查看全文
  • Baixin Zhen,
  • Zhiling Zhao,
  • Hangyu Chen,
  • Wen Li,
  • Lei Zhang,
  • Xi Zhu,
  • Qinggang Ge,
  • Jian Lin

摘要

At present, there are currently no molecular biomarkers for the early diagnosis of sepsis cardiomyopathy (SCM) in clinical practice. This study focuses on an in-depth examination of the DNA hydroxymethylation profiles within plasma extracellular vesicles and explores potential molecular biomarkers during the process of SCM. The 5hmC-Seal sequencing technology was utilized to examine the hydroxymethylation modifications of extracellular vesicles DNAs in 13 patients with septic cardiomyopathy, 18 patients with sepsis without cardiomyopathy, and 8 patients without sepsis. Additionally, a diagnostic model was constructed using machine learning methods based on the differential hydroxymethylation modifications to screen for candidate biomarkers. The accuracy of the diagnostic model was 0.962, with a sensitivity and specificity of 92.3% and 88.89%, respectively. Furthermore, the diagnostic accuracy was validated using the GEO dataset, with an accuracy rate reaching 1 (GSE79962 and GSE66890), and the differential diagnostic accuracy rates also reached 0.959 and 0.944 (GSE79962). Together, the results suggest that extracellular vesicles DNAs hydroxymethylation markers can be used for diagnosis of septic cardiomyopathy.