The plating of rat scar myofibroblasts on matrigel unmasks a novel phenotype; the self assembly of lumen‐like structures
Bibliographic record
Abstract
During tissue healing, the primary role of myofibroblasts involves the synthesis and deposition of collagen. However, it has also been reported that selective populations of myofibroblasts can acquire the phenotype and/or differentiate to other cells types. The present study tested the hypothesis that myofibroblasts isolated from the scar of the ischemically damaged rat heart can recapitulate an endothelial cell-like response when plated in a permissive in vitro environment. Scar myofibroblasts, neonatal and adult ventricular fibroblasts express smooth muscle α-actin, collagen α(1) type 1 and a panel of pro-fibrotic and pro-angiogenic peptide growth factor mRNAs. Myofibroblasts plated alone on matrigel led to the self assembly of lumen-like structures whereas neonatal and adult rat ventricular fibroblasts were unresponsive. Myofibroblasts labeled with the fluorescent cell tracker CM-DiI were injected in the viable myocardium of 3-day post-myocardial infarcted Sprague-Dawley rats and sacrificed 7 days later. Injected CM-DiI-labeled myofibroblasts were detected predominantly in the peri-infarct/infarct region, highlighting their migration to the damaged region. However, engrafted myofibroblasts in the peri-infarct/infarct region were unable to adopt an endothelial cell-like phenotype or lead to the de novo formation of CM-DiI-labeled blood vessels. The non-permissive nature of the infarct region may be attributed at least in part to the presence of growth-promoting stimuli as TGF-β and the β-adrenergic agonist isoproterenol inhibited the self assembly of lumen-like structures by myofibroblasts. Thus, when plated in a permissive in vitro environment, scar myofibroblasts can self assemble and form lumen-like structures providing an additional novel phenotype distinguishing this population from normal ventricular fibroblasts.
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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) | 0.001 | 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".