Qualitative and quantitative effects of 4-1BBL in boosting pre-existing influenza immunity (VAC2P.923)
Bibliographic record
Abstract
Abstract Influenza virus induces an acute respiratory infection responsible for up to 500,000 annual deaths, primarily in older persons. CD8+ T cells against conserved influenza proteins confer heterotypic protection, potentially providing a “universal vaccine.” However, live infection only transiently boosts influenza-specific T cell memory, and immunity further declines with age. To improve the longevity of T cell memory, we investigated the effects of stimulating the TNFR-family member 4-1BB. To mimic vaccination of previously flu-immune adults, mice were infected with influenza A/HK-X31 and boosted intranasally one-month later with control replication-deficient adenovirus or adenovirus encoding influenza nucleoprotein (NP) alone (Ad-NP) or NP with 4-1BBL (Ad-NP-4-1BBL). 4-1BBL dramatically enhanced NP-specific CD8+ T cell responses at a dose where Ad-NP had minimal effects. 4-1BBL induced a remarkably long-lived effector-memory population that protects mice against lethal challenge into old age (>1 year old). Increasing the Ad-NP dose did not replicate these effects, and instead caused partial functional exhaustion. Preliminary analysis indicates that the 4-1BBL-induced effector memory T cells have increased expression of IL-7R as well as the transcriptional factor TCF-1 and an increased ratio of T-bet to Eomesodermin. Thus, 4-1BBL in a vaccine vector administered in flu-immune mice induces a potentially unique protective subset of memory CD8+ T cells.
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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) | 0.002 | 0.001 |
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".