CD8+ DC, but Not CD8−DC, Isolated from BCG-Infected Mice Reduces Pathological Reactions Induced by Mycobacterial Challenge Infection
Bibliographic record
Abstract
BACKGROUND: Tuberculosis is a mycobacterial infection causing worldwide public health problems but the available vaccine is far from ideal. Type-1 T cell immunity has been shown to be critical for host defence against tuberculosis infection, but the role of dendritic cell (DC) subsets in pathogenesis of mycobacterial infection remains unclear. METHODOLOGY/PRINCIPAL FINDINGS: We examined the effectiveness of dendritic cell (DC) subsets in BCG-infected mice in generating immune responses beneficial for pathogen clearance and reduction of pathological reactions in the tissues following challenge infection. Our data showed that only the adoptive transfer of the subset of CD8alpha+ DC isolated from infected mice (iCD8+ DC) generated significant protection, demonstrated by less mycobacterial growth and pathological changes in the lung and liver tissues in iCD8+ DC recipients than sham-treated control mice. The adoptive transfer of the CD8alpha(-)DC from the infected mice (iCD8(-) DC) not only failed to reduce bacterial growth, but enhanced inflammation characterized by diffuse heavy cellular infiltration. Notably, iCD8(-) DC produced significantly higher levels of IL-10 than iCD8+ DC and promoted more Th2 cytokine responses in in vitro DC-T cell co-culture and in vivo adoptive transfer experiments. CONCLUSIONS/SIGNIFICANCE: The data indicate that in vivo BCG-primed CD8+ DC is the dominant DC subset in inducing protective immunity especially for reducing pathological reactions in infected tissues. The finding has implications for the rational improvement of the prophylactic and therapeutic approaches for controlling tuberculosis infection and related diseases.
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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 | 0.001 |
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".