Microenvironmental Control of Inflammatory Cell Differentiation
Bibliographic record
Abstract
A wide body of information now exists on hemopoietic and proinflammatory cytokine production by structural cells of the microenvironment. Included among these are epithelial cells, endothelial cells and fibroblasts which can, either constitutively or upon stimulation with other cytokines or lipopoly-saccharide, express the genes for, and produce, IL-6, IL-8, G-CSF, GM-CSF, and M-CSF, as well as yet unidentified cytokines with prominent cell differentiation-inducing activities. Inflammatory cells which accumulate at sites of allergic-type reactions include granulocytes such as basophils, eosinophils and mast cells, as well as neutrophils and cells of the monocyte-macrophage lineage. Combinations of cytokines produced by tissue structural cells have been studied with reference to their capacity to induce differentiation, activate and prolong the survival of inflammatory cells. Evidence can be adduced for the differentiation process being intimately connected to phenotype switch and activation of cells, such as eosinophils and mast cells, which themselves can feed back upon this by production of cytokines such as TGF-β and GM-CSF; the production of T cell-derived cytokines such as IL-3, IL-5 and GM-CSF can be shown to contribute to basophil and eosinophil differentiation and activation. Work from our laboratory will be summarized with reference to the syntax and language of structural cell-derived cytokines in terms of inflammatory cell differentiation pathways, using a variety of in vitro and in vivo detection techniques. Application of these findings to the control of inflammatory reactions as well as wound repair will also be discussed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".