Methacrylic Acid-based Biomaterials and their Applications in Diabetes and Soft Tissue Repair
Bibliographic record
Abstract
Regenerative medicine holds the promise to restore the functionality of damaged tissue through the use of medical devices that can enhance mechanical performance or deliver therapeutic cells. Clinical implementation of these devices has been limited by challenges associated with fibrosis and/or poor vascularization. Improved device design and materials are required to accelerate healing and reduce inflammation post-implantation. Methacrylic acid (MAA)-based materials are promising candidates for improving the functionality of medical devices due to their ability to enhance vascularization and tissue healing. The present work explores this proposition by evaluating the performance of two MAA-coated implants and a MAA-collagen gel: a polypropylene surgical mesh, a pancreatic islet transplantation device and a wound dressing. The first two devices were coated with MAA-co-isodecyl acrylate and implanted in mice subcutaneously; the last was produced by immobilizing polyMAA to collagen using carbodiimide chemistry. The coating lowered the inflammation around the polypropylene mesh and generated constructive remodeling by biasing the tissue response towards vascularization instead of fibrosis. To assess the effect of the coating on therapeutic cell survival, coated and uncoated devices were implanted in diabetic mice. Pancreatic islets were transplanted into implanted devices and the glucose levels were compared across both groups. Animals with MAA coated devices had the highest islet survival rate and became normoglycemic within 3 weeks of transplantation. Islet survival rate for animals with uncoated devices was low, and they remained diabetic. For wound healing polyMAA was used in a regenerative medicine application that did not require a permanent implant by utilizing a bio-degradable delivery vehicle. The topical application of this gel on hard-to-heal diabetic wounds increased vascularization, and hastened closure. Overall, the ability of MAA-based materials to overcome challenges associated with fibrosis and poor vascularization has been demonstrated in three different areas of regenerative medicine. Future work should explore the use of polyMAA-collagen gels for other tissue engineering applications
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".