Persistence of T Cell and Antibody Responses to SARS-CoV-2 Up to 9 Months after Symptom Onset
Bibliographic record
Abstract
Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces T cell, B cell, and Ab responses that are detected for several months in recovered individuals. Whether this response resembles a typical respiratory viral infection is a matter of debate. In this study, we followed T cell and Ab responses in 24 mainly nonhospitalized human subjects who had recovered from PCR-confirmed SARS-CoV-2 infection at two time points (median of 45 and 145 d after symptom onset). Ab responses were detected in 95% of subjects, with a strong correlation between plasma and salivary anti-spike (anti-S) and anti—receptor binding domain IgG, as well as a correlation between circulating T follicular helper cells and the SARS-CoV-2–specific IgG response. T cell responses to SARS-CoV-2 peptides were determined using intracellular cytokine staining, activation markers, proliferation, and cytokine secretion. All study subjects had a T cell response to at least one SARS-CoV-2 Ag based on at least one T cell assay. CD4+ responses were largely of the Th1 phenotype, but with a lower ratio of IFN-γ– to IL-2–producing cells and a lower frequency of CD8+:CD4+ T cells than in influenza A virus (IAV)-specific memory responses within the same subjects. Analysis of secreted molecules also revealed a lower ratio of IFN-γ to IL-2 and an altered cytotoxic profile for SARS-CoV-2 S- and nucleocapsid-specific responses compared with IAV-specific responses. These data suggest that the memory T cell phenotype after a single infection with SARS-CoV-2 persists over time, with an altered cytokine and cytotoxicity profile compared with long-term memory to whole IAV within the same subjects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".