Vaccinia‐based influenza vaccine overcomes previously induced immunodominance hierarchy for heterosubtypic protection
Bibliographic record
Abstract
Growing concerns about unpredictable influenza pandemics require a broadly protective vaccine against diverse influenza strains. One of the promising approaches was a T cell-based vaccine, but the narrow breadth of T-cell immunity due to the immunodominance hierarchy established by previous influenza infection and efficacy against only mild challenge condition are important hurdles to overcome. To model T-cell immunodominance hierarchy in humans in an experimental setting, influenza-primed C57BL/6 mice were chosen and boosted with a mixture of vaccinia recombinants, individually expressing consensus sequences from avian, swine, and human isolates of influenza internal proteins. As determined by IFN-γ ELISPOT and polyfunctional cytokine secretion, the vaccinia recombinants of influenza expanded the breadth of T-cell responses to include subdominant and even minor epitopes. Vaccine groups were successfully protected against 100 LD50 challenges with PR/8/34 and highly pathogenic avian influenza H5N1, which contained the identical dominant NP366 epitope. Interestingly, in challenge with pandemic A/Cal/04/2009 containing mutations in the dominant epitope, only the group vaccinated with rVV-NP + PA showed improved protection. Taken together, a vaccinia-based influenza vaccine expressing conserved internal proteins improved the breadth of influenza-specific T-cell immunity and provided heterosubtypic protection against immunologically close as well as distant influenza strains.
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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) | 0.000 | 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".