Recruitment of myeloid cells into mycobacterial granulomas.
Bibliographic record
Abstract
Abstract Granuloma formation is a hallmark of mycobacterial infection. The dominant cell types in granuloma are the blood monocyte derived macrophages and inflammatory dendritic cells that represent up to 80% of cells in these lesions. Using granuloma transplantation we demonstrated that in a week more than the third of these cells are replaced. Innate and cognate immunity against the bacteria induces a battery of chemokines that are important in this recruitment and MCP1-CCR2 is one of the main representatives of these pathways. We have also shown that cell death and death-induced extracellular ATP through P2X7R promote VEGF production in a subpopulation of granuloma macrophages and that is also important for myeloid cell recruitment through VEGFR1 displayed by blood monocytes. These factors affect granuloma size, numbers and the number of extravasated monocytes in the tissue around granulomas. We further compare BCG-induced liver and Mtb-induced lung granulomas in wild type, hypoVEGF, macrophage VEGF production ablated, selective macrophage P2X7R deficient, and CCR2 deficient mice. Additionally to the size and frequency of granulomas we compare cell composition, the cytokines present and bacterial load in the lesions. These data clarify the role of immune response and cell death driven cell recruitment pathways in the granuloma maintenance.
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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 | 0.001 |
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".