Mast cells and interleukin‐6 are required for the optimal accumulation of dendritic cell subpopulations in the lymph node in response to bacterial peptidoglycan
Bibliographic record
Abstract
Acquired immune responses are shaped by the nature and activation status of dendritic cell (DC) subpopulations in the lymph node. To better understand the process of DC accumulation in the lymph node during infection we examined the importance of several proinflammatory cytokines and mast cells, which can be a potent source of factors that promote accumulation of DC in the lymph node. The number of plasmacytoid DC (pDC), CD8+ DC and CD11b+ DC in the lymph nodes that drained the site of intradermal injection of S. aureus peptidoglycan (PGN) or saline was determined at 18h post injection. To examine the importance of tumor necrosis factor (TNF), interleukin (IL)1α/β and IL‐6 in PGN induced DC recruitment, we assayed responses in mice deficient in these mediators. TNF and IL‐1 receptor were dispensable for the accumulation of pDC, CD8+ DC and CD11b+ DC in response to PGN. IL‐6 deficient mice had significantly reduced accumulation of CD11b+ DC. Examination of responses to PGN in mast cell deficient mice and mice locally reconstituted with mast cells revealed that mast cells are essential for the optimal recruitment of CD8+ DC and pDC, but not the IL‐6 dependent CD11b+ DC subset. These data suggest that PGN activated mast cells can drive the accumulation of DC subpopulations in the lymph node and thus could play a role in modulating the ensuing acquired immune responses. This work is funded by the Canadian Institutes of Health Research.
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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 | 0.001 |
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".