New role of Human myofibroblasts on neovascularisation during wound healing
Bibliographic record
Abstract
Myofibroblasts are involved in healing mechanism and on tumor development. In both cases, these processes are carried out in synergy with revascularization ensuring the survival of the tissue. We used a three‐dimensional model to investigate the role of myofibroblasts on neovascularization in a context of wound healing. The angiogenic development was analyzed in a fibrin‐based matrix after 14 days via a co‐culture of human skin microvascular endothelial cells (MVEC) and normal skin wound myofibroblasts (Wmyo) isolated from the same patient. Human skin fibroblasts (Fb) was used as control. In our culture conditions, we observed a significant increase of capillary‐like structure number and length when MVEC were co‐cultured with Wmyo instead of Fb. In the same way, we used myofibroblasts (Hmyo) and microvascular endothelial cells (MVECH) isolated from human hypertrophic scars. A positive effect of Hmyo addition on the angiogenic process was also observed but to a lesser extent than with Wmyo. The MVEC and MVECH had similar responses in the presence of dermal cells. From these results, we hypothesize that myofibroblasts could have a more important role on neovascularization than expected on normal wound healing but also in hypertrophic scarring characterized by a large number of myofibroblasts and an extensive microvascular network. This study was supported by CIHR and a scholarship from FRSQ (VM).
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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".