Extensive T cell cross-reactivity between diverse seasonal influenza strains in the ferret model
Bibliographic record
Abstract
Influenza virus causes widespread, yearly epidemics by accumulating surface protein mutations to escape neutralizing antibodies established from prior exposure. In contrast to antibody epitopes, T cell mediated immunity targets influenza epitopes that are more highly conserved and have potential for cross-protection. The extent of T cell cross-reactivity between a diverse array of contemporary and historical influenza strains was investigated in ferrets challenged with 2009 pandemic H1N1 influenza or the seasonal H3N2 strain, A/Perth/16/2009. Post-challenge cell-mediated immune responses demonstrated extensive cross-reactivity with a wide variety of contemporary and historical influenza A strains as well as influenza B. Responses in peripheral blood were undetectable by 36d post-challenge, but cross-reactivity persisted in spleen. The strongest responses targeted peptides from the NP protein and demonstrated cross-reactivity in both the CD4+ and CD8+ T cell populations. Cross-reactive CD4+ T cells also targeted HA and NA epitopes, while cross-reactive CD8+ T cells targeted internal M1, NS2, and PA. T cell epitopes demonstrated extensive cross-reactivity between diverse influenza strains in outbred animals, with NP implicated as a significant antigenic target demonstrating extensive cross-reactivity for both CD4+ and CD8+ T cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".