Hyaluronic acid-based hydrogel induces neovascularization and improves cardiac function in a rat model of myocardial infarction
Bibliographic record
Abstract
OBJECTIVES: The use of stem cells in cardiac regeneration is still limited due to low cellular integration and engraftment rates. Consequently, there has been a spurt in research on developing alternative regenerative therapies. Hyaluronic acid (HA) is a major component of the extracellular matrix that is non-immunogenic, and has been implicated in various wound-healing functions such as angiogenesis and inflammation modulation, making it an ideal candidate for regenerative biomaterials. In this study, we examine the potential of acellular hyaluronic acid-based hydrogel in improving cardiac function post-myocardial infarction in a rat model. METHODS: Hyaluronic acid-based hydrogel was injected into the peri-infarct region post-myocardial infarction induction in Lewis rats. Cardiac function in control (n = 10) and gel-injected groups (n = 10) was evaluated up to 4 weeks post-myocardial infarction. Evaluation of cardiac function was conducted using transthoracic echocardiography. Histological analysis of scar area was evaluated via haematoxylin and eosin (H & E), and Sirius red staining. Neovascularization was detected using vascular endothelial growth factor (VEGF) staining. RESULTS: Evaluation of cardiac function using transthoracic echocardiography revealed a 18.2% (P < 0.01) increase in ejection fraction in gel-injected groups when compared with the control group, almost returning the ejection fraction to baseline levels (preop). Histological analysis of scar area by haematoxylin and eosin (H&E), and Sirius red staining demonstrated decreased scarring, and a 22.6% (P < 0.01) decrease in collagen deposition in the gel-injected group compared with the control group. VEGF staining indicated a significant increase in novel vasculature formation in hydrogel-injected groups when compared with control. CONCLUSIONS: Due to its regenerative potential, hyaluronic acid-based hydrogel provides a promising novel therapy to be used alone, or as a scaffold delivering a variety of drugs or cells to combat heart disease in a multifaceted approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".