Methacrylic Acid Based Hydrogels for Enhanced Skeletal Muscle Regeneration
Bibliographic record
Abstract
Skeletal muscles make up 40-50% of the human body’s mass and are used in most daily activities. While muscles have a remarkable ability to regenerate following small injuries, massive loss of muscle tissue, termed volumetric muscle loss (VML), results in the formation of scar tissue and persistent loss of function. The current treatment for these injuries, autologous tissues grafts, suffers from limited availability, donor site morbidity, infection, and necrosis. A novel method of enhancing skeletal muscle regeneration is through regenerative biomaterials, such as those containing methacrylic-acid (MAA). MAA-based materials have been found to promote wound healing, new vessel formation, and macrophage polarization; however, MAA has yet to be studied outside the skin. The goal of this thesis was to expand the applications of MAA-based materials from beyond the skin into skeletal muscle. In the first aim, the effects of MAA-poly(ethylene glycol) (MAA-PEG) hydrogels on immune cell recruitment and macrophage polarization were examined. Non-degradable hydrogels were found to damage muscle. Thus, degradable hydrogels with degradation rates of either 2 (fast-degrading) or 7 days (slow-degrading) were synthesized. When injected into the tibialis anterior muscle of mice, both slow and fast degrading hydrogels increased the expression of Tnfα, Il10, and pro-regenerative M2 macrophage markers. Moreover, the slow degrading hydrogel decreased the number of pro-inflammatory MHCII+ macrophages. An unbiased t-distributed stochastic neighbor embedding (tSNE) analysis suggested the involvement of additional immune cells (e.g., dendritic cells) in the effect of MAA on skeletal muscle. In the last aim, the ability of MAA-PEG and MAA-collagen hydrogels to promote functional regeneration after a VML injury was evaluated. The MAA-collagen hydrogel increased muscle regeneration both morphologically and functionally. Treatment with this hydrogel also enhanced vascularization and innervation of the injured muscle. These effects were attributed to the hydrogel’s immunomodulatory function as it reduced the recruitment of pro-inflammatory M1 macrophages (MHCII+CD206-). Moreover, the regenerative ability of MAA depended on the carrier material: the MAA-PEG hydrogel did not show the same regenerative potential in this context. It is hoped that the MAA-collagen hydrogel could be a simple treatment to restore muscle function to patients who would not otherwise recover.
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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".