Nanoparticle Adjuvant Sensing by TLR7 Enhances CD8+ T Cell–Mediated Protection from <i>Listeria Monocytogenes</i> Infection
Bibliographic record
Abstract
Developing new adjuvants and vaccination strategies is of paramount importance to successfully fight against many life-threatening infectious diseases and cancer. Very few adjuvants are currently authorized for human use, and these mainly stimulate a humoral response. However, specific Abs are not sufficient to confer protection against persisting infections or cancer. Therefore, development of adjuvants and immunomodulators able to enhance cell-mediated immune responses represents a major medical need. We recently showed that papaya mosaic virus nanoparticles (PapMV), self-assembled from the coat protein of a plant virus and a noncoding ssRNA molecule, are highly immunogenic in mice. PapMV can be used either as a vaccine delivery platform, through fusion of various epitopes to the coat protein or as adjuvant to enhance humoral immune responses against coadministered Ags or vaccines. However, the mechanisms that confer these immunomodulatory properties to PapMV and its ability to enhance T cell vaccines remain unknown. Using immunization studies in mice, we demonstrate in this paper that PapMV represents a novel TLR7 agonist with strong immunostimulatory properties. More importantly, pretreatment with PapMV significantly improves effector and memory CD8(+) T cell responses generated through dendritic cell vaccination increasing protection against a Listeria monocytogenes challenge.
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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) | 0.001 | 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".