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通过综合转录组分析和机器学习来识别和验证异常性肺高血压中细胞衰老生物标志物的细胞衰老生物标志物

Wenzhang Lu¹, Jiayi Xu¹, Yanrong Chen²

¹Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China; Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China.

Experimental gerontology

|September 30, 2023

概括

异形性肺高血压 (IPAH) 很难在早期诊断出来. 这项研究使用机器学习识别了五种血液生物标志物,以帮助早期发现和管理IPAH.

科学领域:

肺高血压研究肺高血压研究
生物标志物发现发现
机器学习在医学中的应用

背景情况:

异常性肺高血压 (IPAH) 是一种严重的血管疾病.
由于依赖侵袭性手术,早期IPAH诊断具有挑战性.
识别周围血液生物标志物对于及时检测IPAH至关重要.

研究的目的:

开发基于细胞衰老的IPAH诊断模型.
为了确定新的血液生物标志物用于早期IPAH检测.

主要方法:

综合转录组分析和机器学习 (LASSO,随机森林).
开发一种用于IPAH细胞衰老的诊断名录.
使用外部数据集和qRT-PCR进行验证.

主要成果:

确定了五个关键的生物标志物:TNFRSF1B,CCL16,GCLM,IL15和SOD1.
这些生物标志物与免疫反应,细胞衰老和亡有关.
诊断名图显示出诊断的实用性.

结论:

机器学习算法有效地帮助IPAH诊断.
已识别的生物标志物为早期IPAH检测提供了潜在的非侵入性方法.
这项研究为改进IPAH管理策略提供了基础.

关键词:

生物标志物生物标志物细胞衰老细胞衰老异常性肺高血压是一种异常性肺高血压.机器学习机器学习对转录组进行分析.

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