Preexisting Inflammation Due to<i>Mycobacterium bovis</i>BCG Infection Differentially Modulates T-Cell Priming against a Replicating or Nonreplicating Immunogen
Bibliographic record
Abstract
Induction of T-cell memory by vaccination ensures long-term protection against pathogens. We determined whether on-going inflammatory responses during vaccination influenced T-cell priming. A preexposure of mice to Mycobacterium bovis BCG impaired their subsequent ability to prime T cells against Listeria monocytogenes. This was characterized by a decrease in L. monocytogenes-specific gamma interferon (IFN-gamma)-secreting CD4(+) and CD8(+) T cells. The intensity of T-cell priming towards L. monocytogenes depended on the extent of L. monocytogenes expansion, and a cessation of this expansion caused by M. bovis BCG-induced inflammation resulted in impairment in T-cell priming. A challenge of M. bovis BCG-infected mice with a higher L. monocytogenes dose increased L. monocytogenes survival and restored T-cell priming towards L. monocytogenes. Impairment in T-cell priming towards L. monocytogenes due to M. bovis BCG-induced inflammation resulted in a compromised protective efficacy in the long term after mice were rechallenged with L. monocytogenes. Preexisting inflammation selectively impaired T-cell priming for replicating immunogens as CD8(+) T-cell response to ovalbumin administered as an inert antigen (ovalbumin-archaeosomes) was enhanced by M. bovis BCG preimmunization, whereas priming towards ovalbumin administered as a live immunogen (L. monocytogenes-ovalbumin) was impaired. Thus, depending on the nature of the immunogen, the presence of prior inflammatory responses may either impede or boost vaccine efficacy.
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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) | 0.001 | 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".