Mussel‐Inspired Conductive Cryogel as Cardiac Tissue Patch to Repair Myocardial Infarction by Migration of Conductive Nanoparticles
Bibliographic record
Abstract
The engineered cardiac patch (ECP) is a promising strategy to repair infarct myocardium and restore the cardiac function. An ideal ECP should be able to mimic the primary attributes of native myocardium, which includes a high resilience, good cardiomyocyte adhesion, and synchronous contraction. Here, a mussel‐inspired dopamine crosslinker is used to integrate polypyrrole (Ppy) nanoparticles, gelatin‐methyacrylate, and poly(ethylene glycol) diacrylate into a cryogel form. The dopamine crosslinker and Ppy nanoparticles are coordinated to obtain optimal mechanical and superelastic properties for the ECP. The dopamine facilitates the uniform distribution of the Ppy nanoparticles, which migrate and fuse from the scaffold to the surface of the cardiomyocytes, revealing a potential mechanism for restoring infarct myocardium. The incorporated Ppy nanoparticles thus significantly enhance the functionalization of the cardiomyocytes, resulting in excellent synchronous contraction by increasing the expression of α‐actinin and CX‐43. Cardiomyocytes‐loaded ECP can improve the cardiac function in myocardial‐infarction (MI) affected rat models. The results show that the fractional shortening and ejection fraction are elevated by about 50% and that the infarct size is reduced by 42.6%. Collectively, this study highlights an effective cardiac patch based on mussel‐inspired conductive particle adhesion and a superelastic cryogel promising for the restoration of infarcted myocardium.
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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) | 0.000 | 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".