Methacrylic Acid Copolymer Coating Enhances Constructive Remodeling of Polypropylene Mesh by Increasing the Vascular Response
Bibliographic record
Abstract
This study reports that a methacrylic acid (MAA)-based copolymer coating generates constructive remodeling of polypropylene (PP) surgical mesh in a subcutaneous model. This coating is non-bioresorbable and follows the architecture of the mesh without impeding connective tissue integration. Following implantation, the tissue response is biased toward vascularization instead of fibrosis. The vessel density around the MAA mesh is double that of the uncoated mesh two weeks after implantation. This initial vasculature regresses after two weeks while mature vessels remain, suggesting an enhanced healing response. Concurrently, the MAA coating alters the foreign body response to the mesh. Fewer infiltrating cells, macrophages, and foreign body giant cells are found at the tissue-material interface three weeks after implantation. The coating also dampens inflammation, with lower expression levels of pro-inflammatory and fibrogenic signals (e.g., Tgf-β1, Tnf-α, and Il1-β) and similar expression levels of anti-inflammatory cytokines (e.g., Il10 and Il6) compared to the uncoated mesh. Contrary to other coatings that aim to mitigate the foreign body response to PP mesh, a MAA coating does not require the addition of any biological agents to have an effect, making the coated mesh an attractive candidate for soft tissue repair.
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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".