No evidence of immune exhaustion after repeated SARS-CoV-2 vaccination in vulnerable and healthy populations
Bibliographic record
Abstract
Frequent SARS-CoV-2 vaccination in vulnerable populations has raised concerns that this may contribute to T cell exhaustion, which could negatively affect the quality of immune protection. Herein, we examined the impact of repeated SARS-CoV-2 vaccination on T cell phenotypic and functional exhaustion in frail older adults in long-term care (n = 23), individuals on immunosuppressive drugs (n = 10), and healthy adults (n = 43), in Canada. Spike-specific CD4+ and CD8+ T cell levels did not decline in any cohort following repeated SARS-CoV-2 vaccination, nor did the expression of exhaustion markers on spike-specific or total T cells increase. T cell production of multiple cytokines (i.e. polyfunctionality) in response to the spike protein of SARS-CoV-2 did not decline in any cohort following repeated vaccination. None of the cohorts displayed elevated levels of terminally differentiated T cells following multiple SARS-CoV-2 vaccinations. Thus, repeated SARS-CoV-2 vaccination was not associated with increased T cell exhaustion in older frail adults, immunosuppressed individuals, or healthy adults. Repeated vaccination is needed to maintain high levels of SARS-CoV-2 immunity in vulnerable populations, but there is concern that it could lead to immune exhaustion. Here, the authors assess the evidence for immune exhaustion following multiple SARS-CoV-2 vaccination three vulnerable population cohorts in Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".