Gamma Interferon Responses of CD4 and CD8 T-Cell Subsets Are Quantitatively Different and Independent of Each Other during Pulmonary<i>Mycobacterium bovis</i>BCG Infection
Bibliographic record
Abstract
Gamma interferon (IFN-gamma) is a key cytokine in host defense against intracellular mycobacterial infection. It has been believed that both CD4 and CD8 T cells are the primary sources of IFN-gamma. However, the relative contributions of CD4 and CD8 T-cell subsets to IFN-gamma production and the relationship between CD4 and CD8 T-cell activation have not been examined. By using a model of pulmonary mycobacterial infection and various immunodetection assays, we found that CD4 T cells mounted a much stronger IFN-gamma response than CD8 T cells at various times after mycobacterial infection, and this pronounced IFN-gamma production by CD4 T cells was attributed to both greater numbers of antigen-specific CD4 T cells and a greater IFN-gamma secretion capacity of these cells. By using major histocompatibility complex class II-deficient or CD4-deficient mice, we found that the lack of CD4 T cells did not negatively affect primary or secondary CD8 T-cell IFN-gamma responses. The CD8 T cells activated in the absence of CD4 T cells were capable of immune protection against secondary mycobacterial challenge. Our results suggest that, whereas both CD4 and CD8 T cells are capable of IFN-gamma production, the former represent a much greater cellular source of IFN-gamma. Moreover, during mycobacterial infection, CD8 T-cell IFN-gamma responses and activation are independent of CD4 T-cell activation.
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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".