Inflammatory cytokines promote growth of intestinal smooth muscle cells by induced expression of <scp>PDGF</scp>‐Rβ
Bibliographic record
Abstract
Thickening of the inflamed intestinal wall involves growth of smooth muscle cells (SMC), which contributes to stricture formation. Earlier, the growth factor platelet-derived growth factor (PDGF)-BB was identified as a key mitogen for SMC from the rat colon (CSMC), and CSMC growth in colitis was associated with both appearance of its receptor, PDGF-Rβ and modulation of phenotype. Here, we examined the role of inflammatory cytokines in inducing and modulating the growth response to PDGF-BB. CSMC were enzymatically isolated from Sprague-Dawley rats, and the effect of tumour necrosis factor (TNF)-α, interleukin (IL)-1β, transforming growth factor (TGF), IL-17A and IL-2 on CSMC growth and responsiveness to PDGF-BB were assessed using proliferation assays, PCR and western blotting. Conditioned medium (CM) was obtained at 48 hrs of trinitrobenzene sulphonic acid-induced colitis. Neither CM alone nor cytokines caused proliferation of early-passage CSMC. However, CM from inflamed, but not control colon significantly promoted the effect of PDGF-BB. IL-1β, TNF-α and IL-17A, but not other cytokines, increased the effect of PDGF-BB because of up-regulation of mRNA and protein for PDGF-Rβ without change in receptor phosphorylation. PDGF-BB was identified in adult rat serum (RS) and RS-induced CSMC proliferation was inhibited by imatinib, suggesting that blood-derived PDGF-BB is a local mitogen in vivo. In freshly isolated CSMC, CM from the inflamed colon as well as IL-1β and TNF-α induced the early expression of PDGF-Rβ, while imatinib blocked subsequent RS-induced cell proliferation. Thus, pro-inflammatory cytokines both initiate and maintain a growth response in CSMC via PDGF-Rβ and serum-derived PDGF-BB, and control of PDGF-Rβ expression may be beneficial in chronic intestinal inflammation.
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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) | 0.002 | 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".