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机器学习方法用于剖析混合免疫和疫苗诱导免疫

Giorgio Montesi¹, Simone Costagli¹, Simone Lucchesi¹

¹Department of Medical Biotechnologies, Laboratory of Molecular Microbiology and Biotechnology, University of Siena, Siena, Italy.

Communications medicine

|July 8, 2025

概括

机器学习识别了14名先前感染的个人,他们不知道自己的SARS-CoV-2状态. 这种方法有助于估计真正的感染患病率,并了解疫苗接种和感染的混合免疫力.

科学领域:

免疫学免疫学免疫学
传染性疾病传染性疾病
计算生物学计算生物学

背景情况:

SARS-CoV-2 Omicron 变种的传播导致未报告的感染增加.
识别以前未知感染的个体对于评估真正的流行率和混合免疫力至关重要.
机器学习 (ML) 为应对这一挑战提供了一种新的方法.

研究的目的:

应用ML技术来识别那些不知道自己曾感染过SARS-CoV-2的人.
分析这些个体的免疫特征,以了解混合免疫.
评估ML在估计感染患病率方面的有用性.

主要方法:

在IMMUNO_COV研究中的116名接种疫苗的参与者提供了疫苗第三剂前和后的血液样本.
机器学习分析包括缩小维度,无监督聚类和分类模型 (k-NN,RF,SVM).
使用了血清学数据 (对SARS-CoV-2变体的抗体反应) 和B细胞评估.

主要成果:

参与者被分为高响应者和低响应者;感染者主要是高响应者.
ML模型确定了14名参与者,他们不知道以前的感染.
这些人表现出独特的免疫特征,具有强烈的幽默和B细胞反应.

结论:

ML有效地识别了未被识别的SARS-CoV-2感染的个体.
这种具有成本效益的方法有助于估计真正的感染流行率.
这些发现提高了对混合免疫的理解,并为公共卫生战略提供了信息.

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