Multiple Mechanisms Compensate to Enhance Tumor-Protective CD8+ T Cell Response in the Long-Term Despite Poor CD8+ T Cell Priming Initially: Comparison Between an Acute Versus a Chronic Intracellular Bacterium Expressing a Model Antigen
Bibliographic record
Abstract
We evaluated CD8(+) T cell responses against the dominant CTL epitope, OVA(257-264), expressed by an acute (Listeria monocytogenes (LM) OVA) vs a chronic pathogen (Mycobacterium bovis bacillus Calmette-Guérin (BCG) OVA) to reveal the influence on CD8(+) T cell memory and consequent protection against a challenge with OVA-expressing tumor cells. Infection with lower doses of both pathogens resulted in stronger bacterial growth but weaker T cell memory indicating that memory correlates with pathogen dose but not with bacterial expansion. The CD8(+) T cell response induced by LM-OVA was helper T cell-independent and was characterized by a rapid effector response followed by a rapid, but massive, attrition. In contrast, BCG-OVA induced a delayed and weak response that was compensated for by a longer effector phase and reduced attrition. This response was partly dependent on CD4(+) T cells. CD8(+) T cell response induced by BCG-OVA, but not LM-OVA, was highly dependent on pathogen persistence to compensate for the weak initial CD8(+) T cell priming. Despite a stronger initial T cell response with LM-OVA, BCG-OVA provided more effective tumor (B16OVA) control at both local and distal sites due to the induction of a persistently activated acquired, and a more potent innate, immunity.
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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) | 0.001 | 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".