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Clin Exp Med
. 2025 Jul 8;25(1):236.
doi: 10.1007/s10238-025-01772-2. Predicting SARS-CoV-2-specific CD4+ and CD8+ T-cell responses elicited by inactivated vaccines in healthy adults using machine learning models
Jie Ning 1 , Yayi Ren 1 , Zelin Zhang 1 , Xianhuang Zeng 1 , Qinjin Wang 1 , Jia Xie 1 , Yue Xu 1 , Yali Fan 1 , Huilan Li 1 , Aixia Zhai 1 , Bin Li 1 , Chao Wu 2 , Ying Chen 3
Affiliations
The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants highlights the importance of monitoring immune responses to guide vaccination strategies. Although neutralizing antibodies (NAbs) have garnered increasing attention, T-cells are crucial for conferring long-lasting immunity, especially their resilience against viral mutations. However, assessing T-cell responses clinically has been hindered by cost and complexity. In this study, we recruited a cohort of 134 healthy adults, who had been immunized with three doses of the SARS-CoV-2 inactivated vaccine. Cellular immunity elicited by a comprehensive array of overlapping peptides covering the entire sequence of the virus's structural proteins was assessed by intracellular cytokine staining (ICS). Additionally, a dataset including demographic information, routine blood indices, and immune cell indicators comprising 32 variables was collected. Multivariate analysis revealed age and days post-vaccination as key factors influencing the strength of the T-cell response. Importantly, random forest (RF) and classification and regression tree (CART) algorithms were employed, along with 8 easily accessible indicators to formulate predictive models for the SARS-CoV-2-specific CD4+ and CD8+ T-cell responses. Besides, these models demonstrated substantial accuracy (r > 0.9) in both the training and testing sets. Our findings offer an efficient and economical methodology for evaluating the T-cell reactions in healthy adults following inactivated SARS-CoV-2 vaccination, which is visualizable and easy to use, providing a novel strategy for assessing cellular immunity after vaccination.
Keywords: Cellular immunity; Classification and regression tree; Machine learning; Random forest; SARS-CoV-2.
. 2025 Jul 8;25(1):236.
doi: 10.1007/s10238-025-01772-2. Predicting SARS-CoV-2-specific CD4+ and CD8+ T-cell responses elicited by inactivated vaccines in healthy adults using machine learning models
Jie Ning 1 , Yayi Ren 1 , Zelin Zhang 1 , Xianhuang Zeng 1 , Qinjin Wang 1 , Jia Xie 1 , Yue Xu 1 , Yali Fan 1 , Huilan Li 1 , Aixia Zhai 1 , Bin Li 1 , Chao Wu 2 , Ying Chen 3
Affiliations
- PMID: 40627199
- PMCID: PMC12238115
- DOI: 10.1007/s10238-025-01772-2
The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants highlights the importance of monitoring immune responses to guide vaccination strategies. Although neutralizing antibodies (NAbs) have garnered increasing attention, T-cells are crucial for conferring long-lasting immunity, especially their resilience against viral mutations. However, assessing T-cell responses clinically has been hindered by cost and complexity. In this study, we recruited a cohort of 134 healthy adults, who had been immunized with three doses of the SARS-CoV-2 inactivated vaccine. Cellular immunity elicited by a comprehensive array of overlapping peptides covering the entire sequence of the virus's structural proteins was assessed by intracellular cytokine staining (ICS). Additionally, a dataset including demographic information, routine blood indices, and immune cell indicators comprising 32 variables was collected. Multivariate analysis revealed age and days post-vaccination as key factors influencing the strength of the T-cell response. Importantly, random forest (RF) and classification and regression tree (CART) algorithms were employed, along with 8 easily accessible indicators to formulate predictive models for the SARS-CoV-2-specific CD4+ and CD8+ T-cell responses. Besides, these models demonstrated substantial accuracy (r > 0.9) in both the training and testing sets. Our findings offer an efficient and economical methodology for evaluating the T-cell reactions in healthy adults following inactivated SARS-CoV-2 vaccination, which is visualizable and easy to use, providing a novel strategy for assessing cellular immunity after vaccination.
Keywords: Cellular immunity; Classification and regression tree; Machine learning; Random forest; SARS-CoV-2.