Expansion of cytotoxic tissue-resident CD8+ T cells and CCR6+CD161+ CD4+ T cells in the nasal mucosa following mRNA COVID-19 vaccination
Bibliographic record
Abstract
Abstract Vaccines against SARS-CoV-2 have shown high efficacy in clinical trials, yet a full immunologic characterization of these vaccines, particularly within the human upper respiratory tract, is less well known. Here, we enumerate and phenotype T cells in nasal mucosa and blood using flow cytometry before and after vaccination with the Pfizer-BioNTech COVID-19 vaccine ( n = 21). Tissue-resident memory (Trm) CD8 + T cells expressing CD69 + CD103 + increase in number ~12 days following the first and second doses, by 0.31 and 0.43 log 10 cells per swab respectively ( p = 0.058 and p = 0.009 in adjusted linear mixed models). CD69 + CD103 + CD8 + T cells in the blood decrease post-vaccination. Similar increases in nasal CD8 + CD69 + CD103 − T cells are observed, particularly following the second dose. CD4 + cells co-expressing CCR6 and CD161 are also increased in abundance following both doses. Stimulation of nasal CD8 + T cells with SARS-CoV-2 spike peptides elevates expression of CD107a at 2- and 6-months ( p = 0.0096) post second vaccine dose, with a subset of donors also expressing increased cytokines. These data suggest that nasal T cells may be induced and contribute to the protective immunity afforded by this vaccine.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".