Interleukin-4 and Interleukin-13 Enhance Human Bronchial Smooth Muscle Cell Proliferation
Bibliographic record
Abstract
BACKGROUND: T(H)2 inflammation and bronchial smooth muscle cell (BSMC) hyperplasia are characteristic features of asthma, but whether these phenomena are linked remains unknown. This study aims to define the effect of the T(H)2 cytokines IL-4 and IL-13 on human BSMC proliferation when administered alone or in combination with the fibroblast growth factor 2 (FGF2) growth factor. In addition, the effects of the proinflammatory mediators TNFalpha and IL-1 beta and the involvement of members of the well-known family of platelet-derived growth factor (PDGF) mitogens were tested. METHODS: BSMC proliferation was measured by crystal violet staining and PDGF and PDGF receptor (PDGFR) expression were determined by RT-PCR, immunocytochemistry, ELISA, flow cytometry and dot plot analysis. RESULTS: Neither IL-4 nor IL-13 alone induced BSMC proliferation, despite both being potent inducers of PDGF-CC. However, following a pretreatment with FGF2, which increased PDGFR alpha chain expression, both IL-4 and IL-13 increased FGF2-induced BSMC proliferation in a time- and concentration-dependent manner. TNFalpha and IL-1 beta did not affect basal or FGF2-induced BSMC proliferation, but both proinflammatory mediators enhanced the proliferative synergism between FGF2 and the T(H)2 cytokines. CONCLUSIONS: IL-4 and IL-13 potently induce FGF2-primed BSMC proliferation via an autocrine loop involving PDGFRalpha and PDGF-CC, and this proliferative synergism is amplified by proinflammatory cytokines.
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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) | 0.003 | 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".