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相关概念视频

Cytomegalovirus Disease01:27

Cytomegalovirus Disease

Cytomegalovirus (CMV) disease is caused by human cytomegalovirus, a double-stranded DNA virus of the Herpesviridae family. While primary CMV infection is often asymptomatic in immunocompetent individuals, the virus can cause severe disease in neonates and immunocompromised patients. CMV is the most common cause of congenital viral infection in the United States, and a major pathogen in solid organ and hematopoietic stem cell transplant recipients.CMV is transmitted via bodily fluids, sexual...

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Generation of Multivirus-specific T Cells to Prevent/treat Viral Infections after Allogeneic Hematopoietic Stem Cell Transplant
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通过机器学习方法揭示全源干细胞移植后的CMV风险标志:来自GETH-TC的多中心分析.

Estela Giménez1, Pablo Rodriguez-Belenguer2, Carlos Solano3,4

  • 1Microbiology Service, Hospital Clínico Universitario, Biomedical Research Institute INCLIVA, Valencia, Spain.

Journal of medical virology
|March 13, 2026
PubMed
概括
此摘要是机器生成的。

机器学习模型可以预测在异性造血干细胞移植 (allo-HCT) 后的细胞巨乳病毒 (CMV) 感染. 这些模型为CMVDNAemia,临床上显著的CMV感染和耐火性CMV感染在Allo-HCT患者中的风险分层提供了改进.

关键词:
全基性 hameatopietic 干细胞移植.细胞巨型病毒 (cytomegalovirus) 是一种病毒.机器学习是机器学习.耐火性CMV感染

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Automated Cell Enrichment of Cytomegalovirus-specific T cells for Clinical Applications using the Cytokine-capture System
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科学领域:

  • 血液学 血液学 血液学
  • 传染性疾病 传染性疾病
  • 计算生物学 计算生物学

背景情况:

  • 细胞巨乳病毒 (CMV) 感染是异构造血型干细胞移植 (allo-HCT) 后的重大并发症,导致发病率和死亡率增加.
  • 耐火性CMV感染在allo-HCT接受者中构成一个特殊的挑战.
  • 机器学习 (ML) 提供了在管理CMV感染时增强风险分层的潜力.

研究的目的:

  • 开发和评估ML模型,用于预测CMVDNAemia,临床显著的CMV感染 (csCMV-I) 和耐火的CMV感染 (CMV-R).
  • 确定不同CMV相关结果的关键预测特征.
  • 建立一个准确的风险分层的基础,在CMV管理后合金-HCT.

主要方法:

  • 对来自21个西班牙中心的933名allo-HCT接受者的回顾性分析 (2014-2018).
  • 开发和评估六个监督的ML算法 (随机森林,SVM,决策树,XGBoost,弹性网,PLS-Logistic回归),使用重复的五重分层交叉验证.
  • 预测CMVDNAemia,csCMV-I和CMV-R. 这三种病毒的发生.

主要成果:

  • 在53%的患者中发生了CMVDNAemia,其中72%的患者发展为csCMV-I,26%的患者发展为CMV-R.
  • XGBoost模型显示高灵敏度 (0.97) 预测CMV DNAemia和csCMV-I.
  • 在SVM模型中,预测CMV-R的灵敏度为0.58.
  • 关键预测因素包括接受者的CMV血清状态,调节方案,GvHD和免疫抑制方案.

结论:

  • ML模型可以识别与alo-HCT中CMV结局相关的独特临床概况.
  • 这些模型在预测CMV事件方面表现出高灵敏度但适度的区分能力.
  • 这项研究为精确的风险分层提供了基础,补充了当前的预防策略,需要进行前性验证.