tetano
Editor, Senior Moderator
Clin Exp Med
. 2025 Jul 8;25(1):236.
doi: 10.1007/s10238-025-01772-2. Predicting SARS-CoV-2-specific CD4[SUP]+[/SUP] and CD8[SUP]+[/SUP] T-cell responses elicited by inactivated vaccines in healthy adults using machine learning models
Jie Ning[SUP] 1 [/SUP], Yayi Ren[SUP] 1 [/SUP], Zelin Zhang[SUP] 1 [/SUP], Xianhuang Zeng[SUP] 1 [/SUP], Qinjin Wang[SUP] 1 [/SUP], Jia Xie[SUP] 1 [/SUP], Yue Xu[SUP] 1 [/SUP], Yali Fan[SUP] 1 [/SUP], Huilan Li[SUP] 1 [/SUP], Aixia Zhai[SUP] 1 [/SUP], Bin Li[SUP] 1 [/SUP], Chao Wu[SUP] 2 [/SUP], Ying Chen[SUP] 3 [/SUP]
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[SUP]+[/SUP] and CD8[SUP]+[/SUP] 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[SUP]+[/SUP] and CD8[SUP]+[/SUP] T-cell responses elicited by inactivated vaccines in healthy adults using machine learning models
Jie Ning[SUP] 1 [/SUP], Yayi Ren[SUP] 1 [/SUP], Zelin Zhang[SUP] 1 [/SUP], Xianhuang Zeng[SUP] 1 [/SUP], Qinjin Wang[SUP] 1 [/SUP], Jia Xie[SUP] 1 [/SUP], Yue Xu[SUP] 1 [/SUP], Yali Fan[SUP] 1 [/SUP], Huilan Li[SUP] 1 [/SUP], Aixia Zhai[SUP] 1 [/SUP], Bin Li[SUP] 1 [/SUP], Chao Wu[SUP] 2 [/SUP], Ying Chen[SUP] 3 [/SUP]
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[SUP]+[/SUP] and CD8[SUP]+[/SUP] 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.