Comparison of the Protective Efficacy of Neutralizing Epitopes of 2009 Pandemic H1N1 Influenza Hemagglutinin
Bibliographic record
Abstract
The 2009 H1N1 influenza (Pdm09) pandemic has been referred to as the first influenza pandemic of the twenty-first century. There is a marked difference in antigenicity between the pandemic H1N1 virus and past seasonal H1N1 viruses, which allowed the pandemic virus to spread rapidly in humans. Antibodies (Abs) against hemagglutinin (HA), especially neutralizing Abs against epitopes in the head of HA, play critical roles in defending the host against the virus. Some preexisting neutralizing Abs that recognize neutralizing epitopes of Pdm09 HA, thereby affording cross-protection, have been reported. To better understand the protective effects of epitopes in Pdm09 HA, we constructed a series of plasmid DNAs (DNA vaccines) by cloning various combinations of Pdm09 neutralizing epitopes into the HA backbone derived from A/PR/8/1934 (H1N1). We subsequently compared the protective immune responses induced by these various forms of HA in a mouse model. We found that the plasmid DNAs with epitope substitutions provided better protection against lethal virus challenge and induced higher strain-specific antibody titers, with epitope Sa being the most effective. Moreover, the combination of epitopes Sa and Sb provided almost complete protection in mice. These findings provide new insights into the protective efficacy of neutralizing epitopes of influenza HA.
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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) | 0.001 | 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".