Distinct NKT Cell Subsets Are Induced by Different <i>Chlamydia</i> Species Leading to Differential Adaptive Immunity and Host Resistance to the Infections
Bibliographic record
Abstract
We investigated the role of NKT cells in immunity to Chlamydia pneumoniae and Chlamydia muridarum infections using a combination of knockout mice and specific cellular activation approaches. The NKT-deficient mice showed exacerbated susceptibility to C. pneumoniae infection, but more resistance to C. muridarum infection. Activation of NKT reduced C. pneumoniae in vivo growth, but enhanced C. muridarum infection. Cellular analysis of invariant NKT cells revealed distinct cytokine patterns following C. pneumoniae and C. muridarum infections, i.e., predominant IFN-gamma in the former, while predominant IL-4 in the latter. The cytokine patterns of CD4(+) and CD8(+) T cells matched those of NKT cells. Our data provide in vivo evidence for a functionally diverse role of NKT cells in immune response to two intracellular bacterial pathogens. These results suggest that distinct NKT subsets are induced by even biologically closely related pathogens, thus leading to differential adaptive immune response and infection outcomes.
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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".