A universal influenza mRNA vaccine candidate boosts T cell responses and reduces zoonotic influenza virus disease in ferrets
Bibliographic record
Abstract
Universal influenza vaccines should protect against continuously evolving and newly emerging influenza viruses. T cells may be an essential target of such vaccines, as they can clear infected cells through recognition of conserved influenza virus epitopes. We evaluated a novel T cell-inducing nucleoside-modified messenger RNA (mRNA) vaccine that encodes the conserved nucleoprotein, matrix protein 1, and polymerase basic protein 1 of an H1N1 influenza virus. To mimic the human situation, we applied the mRNA vaccine as a prime-boost regimen in naïve ferrets (mimicking young children) and as a booster in influenza-experienced ferrets (mimicking adults). The vaccine induced and boosted broadly reactive T cells in the circulation, bone marrow, and respiratory tract. Booster vaccination enhanced protection against heterosubtypic infection with a potential pandemic H7N9 influenza virus in influenza-experienced ferrets. Our findings show that mRNA vaccines encoding internal influenza virus proteins represent a promising strategy to induce broadly protective T cell immunity against influenza viruses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".