Being a Target for Glycation by Methylglyoxal Contributes to Therapeutic Efficacy of Injectable Collagen Hydrogels Post-Myocardial Infarction
Bibliographic record
Abstract
Despite the advances in medical therapies for treating myocardial infarction (MI), morbidity and mortality rates remain high. Following MI, increased methylglyoxal (MG) production leads to the accumulation of advanced glycation end-products (AGEs), which contribute to adverse remodeling and to the deterioration of cardiac function. We previously reported that an injectable collagen type I hydrogel improves the repair and function of mouse hearts post-MI. Notably, we observed that the injected hydrogel was a target for MG-AGE glycation, and that there were less MG-modified proteins in the myocardium. In this study, we further evaluated this protective mechanism by pre-glycating the hydrogels and assessing their therapeutic efficacy for treating MI. In vitro experiments showed that the viability of macrophages was reduced when cultured with the glycated hydrogel in the presence of MG. In vivo, female C57BL/6 mice were randomly assigned to receive intramyocardial injections of one of three treatments: phosphate-buffered saline, normal collagen hydrogel, or MG-glycated hydrogel. After 28 days, echocardiography was performed to evaluate cardiac function, and hearts were harvested for immunohistochemistry. Our results showed that the MG-glycated hydrogel had a reduced treatment effect (greater scar size, fewer wound-healing macrophages, less viable myocardium and decreased cardiac function) compared to mice that received the normal collagen hydrogel. In summary, this study demonstrates that the ability of the collagen hydrogel to act as a target for glycation and remove MG from the environment contributes to its therapeutic effect in treating the post-MI heart.
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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".