Article ; Online: A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort.
International immunopharmacology
2024 Volume 131, Page(s) 111829
Abstract: Background: Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets ...
| Abstract | Background: Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection. Methods: Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections. Results: Following SARS-CoV-2 breakthrough infection, naïve T cells and T Conclusion: SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making. |
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| MeSH term(s) | Humans ; COVID-19 Vaccines ; COVID-19 ; SARS-CoV-2 ; Breakthrough Infections ; Pandemics ; Prognosis ; Prospective Studies ; Tumor Necrosis Factor-alpha ; Immunity, Cellular ; Antibodies, Viral | |||||
| Chemical Substances | COVID-19 Vaccines ; Tumor Necrosis Factor-alpha ; Antibodies, Viral | |||||
| Language | English | |||||
| Publishing date | 2024-03-14 | |||||
| Publishing country | Netherlands | |||||
| Document type | Journal Article | |||||
| ZDB-ID | 2043785-7 | |||||
| ISSN | 1878-1705 ; 1567-5769 | |||||
| ISSN (online) | 1878-1705 | |||||
| ISSN | 1567-5769 | |||||
| DOI | 10.1016/j.intimp.2024.111829 | |||||
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| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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