Protease-Activated Receptor 2 Agonist as Adjuvant: Augmenting Development of Protective Memory CD8 T Cell Responses Induced by Influenza Virosomes
Bibliographic record
Abstract
Abstract Protease-activated receptor 2 (PAR-2) is expressed in various tissues, including lung, and when activated, promotes inflammation, differentiation, and migration of dendritic cells. We found that combining influenza virosomes containing hemagglutinin and neuraminidase with a PAR-2 agonist peptide (PAR-2AP) in an intranasal prime boost approach increased survival of mice challenged weeks later with lethal influenza virus over that by virosome or PAR-2AP prime boost alone. No weight loss occurred from influenza challenge after virosome-plus–PAR-2AP prime boost compared with either virosomes or PAR-2AP alone. Thus, virosomes plus PAR-2AP prevented morbidity as well as mortality. Through adoptive transfer, CD8+ lung T cells but not CD4+ T cells from virosomes plus PAR-2AP–primed mice protected from lethal influenza virus challenge and enhanced survival with less weight loss and faster recovery. Virosome-plus–PAR-2AP prime boost resulted in greater percentages of T effector memory phenotype cells (Tem) in lung, and higher frequencies of CD8 Tem and T central memory cells displayed effector functions in response to virus challenge in vivo. Virosome-plus–PAR-2AP prime boost also resulted in greater percentages of Ag-specific CD8+ T cells, both Tem and T central memory cells, in lungs of animals subsequently challenged with live influenza virus. Our findings indicate that PAR-2AP, a short peptide, may be a new and useful mucosal adjuvant.
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) | 0.001 | 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".