Airway-resident T cells from unexposed individuals cross-recognize SARS-CoV-2
Bibliographic record
Abstract
Abstract T cells can contribute to clearance of respiratory viruses that cause acute-resolving infections such as SARS-CoV-2, helping to provide long-lived protection against disease. Recent studies have suggested an additional role for T cells in resisting overt infection: pre-existing cross-reactive responses were preferentially enriched in healthcare workers who had abortive infections 1 , and in household contacts protected from infection 2 . We hypothesize that such early viral control would require pre-existing cross-reactive memory T cells already resident at the site of infection; such airway-resident responses have been shown to be critical for mediating protection after intranasal vaccination in a murine model of SARS-CoV 3 . Bronchoalveolar lavage samples from the lower respiratory tract of healthy donors obtained before the COVID-19 pandemic revealed airway-resident, SARS-CoV-2-cross-reactive T cells, which correlated with the strength of human seasonal coronavirus immunity. We therefore demonstrate the potential to harness functional airway-resident SARS-CoV-2-reactive T cells in next-generation mucosal vaccines.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".