Immunity to SARS-CoV-2 persists 9 months post-symptoms with an altered T cell phenotype compared to influenza A virus-specific memory
Bibliographic record
Abstract
Abstract SARS-CoV-2 induces T cell, B cell and antibody responses that are detected for several months in recovered individuals. Whether this response resembles a typical respiratory viral infection is a matter of debate. Here we followed T cell and antibody responses in 24 mainly non-hospitalized SARS-CoV-2 recovered subjects at two time points (median of 45- and 145-days post-symptom onset). Antibody responses were detected in 95% of subjects, with a strong correlation between plasma and salivary anti-S and anti-RBD IgG, as well as a correlation between circulating T follicular helper cells and the SARS-CoV-2-specific IgG response. Based on intracellular cytokine production or proliferation, CD4 + T cell responses to SARS-CoV-2 were detected in all subjects, decaying with a half-life of 5-6 months for S-specific IL-2-producing cells. CD4 + responses were largely of the T helper 1 phenotype, but with a lower ratio of IFN-γ: IL-2 producing cells and a lower frequency of CD8 +: CD4 + T cells compared to influenza A virus-(IAV)-specific memory responses within the same subjects. Analysis of secreted molecules also revealed a lower ratio of IFN-γ: IL-2 and IFN-γ: IL-6 and an altered cytotoxic profile for S- and N-specific compared to 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 cytotoxic profile compared to long term memory to IAV within the same subjects. One Sentence Summary Immunity to SARS-CoV-2 in a cohort of patients, mainly with mild COVID-19 disease, persists to 9 months with an altered T cell cytokine and cytotoxicity profile compared to influenza A virus-specific memory T cells from 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) | 0.002 | 0.001 |
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".