α11 integrin stimulates myofibroblast differentiation in diabetic cardiomyopathy
Bibliographic record
Abstract
AIMS: Diabetic cardiomyopathy is characterized by the production of a disorganized fibrotic matrix in the absence of coronary atherosclerosis and hypertension. We examined whether adhesion of cardiac fibroblasts to glycated collagens mediates the differentiation of pro-fibrotic myofibroblasts, which may contribute to cardiac fibrosis. METHODS AND RESULTS: By microarray, we found that methylglyoxal-treated collagen selectively enhanced α11 integrin expression in human cardiac fibroblasts, while levels of other collagen-binding integrins (α1, α2, and α10) were unchanged. Similar increases in α11 integrin mRNA and protein expression were observed in cardiac fibroblasts from streptozotocin (STZ)-treated Sprague-Dawley rats. In human cardiac fibroblasts plated on methyglyoxal-treated collagen and in cardiac fibroblasts from diabetic rats, transforming growth factor (TGF)-β2 but not TGF-β1 or TGF-β3 was increased compared with controls. Knock-down of α11 integrin and TGF-β receptors with small-interfering RNA blocked the increased expression of TGF-β2, α-smooth muscle actin (α-SMA), and α11 integrin that were induced in cells plated on methylglyoxal-treated collagen. Further, inhibition of Smad3 signalling blocked methylglyoxal-collagen up-regulation of α11 integrin and α-SMA expression. Rats with STZ-induced diabetes exhibited increased phosphorylation of Smad3 in cardiac tissues compared with control rats. CONCLUSION: Interactions between α11 integrins and the Smad-dependent TGF-β2 signalling may contribute to the formation of pro-fibrotic myofibroblasts and the development of a fibrotic interstitium in diabetic cardiomyopathy.
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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) | 0.002 | 0.000 |
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".