VEGF induces stress fiber formation in fibroblasts isolated from dystrophic muscle
Bibliographic record
Abstract
Treatment with vascular endothelial growth factor (VEGF) to reduce ischemia and enhance both endogenous muscle repair and regenerative cell therapy in Duchenne muscular dystrophy (DMD) has been widely proposed in recent years. However, the interaction between angiogenesis and fibrosis, a hallmark feature of DMD, remains unclear. To date, it has not been determined whether VEGF exerts a pro-fibrotic effect on DMD-derived fibroblasts, which may contribute to further disease progression. Thus, the purpose of this study was to investigate the effect of exogenous VEGF on fibroblast cultures established from a murine model of DMD. Primary fibroblast cultures were established from gastrocnemius and diaphragm muscles of 10 week-old mdx/utrn+/- mice. Quantitative polymerase chain reaction (qPCR) was employed to assess changes in transcript expression of alpha-smooth muscle actin (Acta2), type-1 collagen (Col1a1), connective tissue growth factor (Ctgf/ccn2) and fibronectin (Fn1). Immunofluorescence and Western blot analysis was further employed to visualize changes in protein expression of alpha-smooth muscle actin (α-SMA), CTGF/CCN2 and fibronectin. mRNA levels of Col1a1, Ctgf/ccn2, and FN did not increase following treatment with VEGF in fibroblasts derived from either diaphragm or gastrocnemius muscles. Acta2 expression increased significantly in diaphragm-derived fibroblasts following treatment with VEGF. Morphological assessment revealed increased stress fiber formation in VEGF-treated fibroblasts compared to the untreated control fibroblasts. The findings from this study suggest that further investigation into the effect of VEGF on fibroblast function is required prior to the utilization of the growth factor as a treatment for DMD.
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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) | 0.001 | 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".