Original Article: Highly conserved cross‐reactive CD4+ T‐cell HA‐epitopes of seasonal and the 2009 pandemic influenza viruses
Bibliographic record
Abstract
BACKGROUND: The relatively mild nature of the 2009 influenza pandemic (nH1N1) highlights the overriding importance of pre-existing immune memory. The absence of cross-reactive antibodies to nH1N1 in most individuals suggests that such attenuation may be attributed to pre-existing cellular immune responses to epitopes shared between nH1N1 virus and previously circulating strains of inter-pandemic influenza A viruses. RESULTS: We sought to identify potential CD4+ T cell epitopes and predict the level of cross-reactivity of responding T cells. By performing large-scale major histocompatibility complex II analyses on Hemagglutinin (HA) proteins, we investigated the degree of T-cell cross-reactivity between seasonal influenza A (sH1N1, H3N2) from 1968 to 2009 and nH1N1 strains. Each epitope was examined against all the protein sequences that correspond to sH1N1, H3N2, and nH1N1. T-cell cross-reactivity was estimated to be 52%, and maximum conservancy was found between sH1N1 and nH1N1 with a significant correlation (P < 0.05). CONCLUSIONS: Given the importance of cellular responses in kinetics of influenza infection in humans, our findings underscore the role of T-cell assays for understanding the inter-pandemic variability in severity and for planning treatment methods for emerging influenza viruses.
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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) | 0.011 | 0.002 |
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".