肺部和脏长期COVID在两年后的再访时
Jing Wang1,2,3, Xiao Liang4,5,6, Yufen Zheng1,2,3
1Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.
iScience
|July 26, 2024
概括
长期COVID会影响肺部和脏,在两年内患病率增加. 机器学习模型使用早期的临床和多omics数据准确地预测这些情况.
科学领域:
- 免疫学 免疫学 免疫学
- 肺部病理学 肺部病理学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 计算生物学 计算生物学
背景情况:
- 长期COVID是一种COVID-19感染后的持续性疾病,呈现出影响多个器官系统的多种症状.
- 了解长期COVID的纵向进展和分子基础对于有效管理至关重要.
研究的目的:
- 纵向研究长期COVID患者的宿主反应.
- 确定肺和长期COVID的临床和分子预测因素.
- 为早期识别长期COVID并发症开发一个预测模型.
主要方法:
- 在1年和2年间隔对COVID-19队列进行长度随访.
- 在疾病的第一个月内进行临床评估和多组数据收集 (蛋白质组).
- 机器学习模型开发用于预测肺和长期COVID.
主要成果:
- 肺长期COVID (肺纤维条纹) 从1年后的8.7%增加到2年后的17.8%.
- 长期COVID患病率从1年后的15.2%上升到2年后的23.9%.
- 一个机器学习模型在使用早期数据预测1年的肺和长期COVID时达到87.5%的准确性.
- 蛋白质组分析将肺纤维化与表面活性蛋白B的降低以及脏问题与尿蛋白表达的改变联系起来.
结论:
- 长期COVID,特别是影响肺部和脏,在两年内患病率呈上升趋势.
- 早期的临床和多病理学数据可以准确地预测肺和长期COVID的发展.
- 分子特征,包括表面活性蛋白B和尿蛋白,与特定的长期COVID表现有关.
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