An Anisotropic and Stable‐Conductance Patch for Mechanical–Electrical Coupling With Infarcted Myocardium
Bibliographic record
Abstract
ABSTRACT Polymeric conductive patches have conventionally been employed to facilitate the repair of infarcted myocardium by enhancing myocardial electrical conduction and providing mechanical support. However, it remains a challenge to restore the electrical conduction and diastolic–systolic functions with stable and anisotropic mechanical and electrical cues in the dynamic physiological environment. Herein, inspired by the hierarchical myocardial fiber microscopic striated structure, we established a weaving‐based processing method to compound a striated polypyrrole conductive coating on the surface of highly oriented elastic fiber bundles. This unique design endows the patch with exceptional stretchability (elongation at break > 400%), stable conductance (Δ R / R 0 = 0.04 within 20% strain), and excellent fatigue resistance (Δ R / R 0 = 0.01 after 1,000,000 cycles). In addition, the precision process grounded on woven molding accomplished the tunable mechanical and electrical properties of the patch, which facilitates the achievement of long‐term, stable, and anisotropic mechanical–electrical coupling with the infarcted myocardium. The rat MI model experiments demonstrated that this anisotropic conductive patch can not only improve cardiac function and electrical activity over an extended period, but also effectively inhibit myocardial inflammation and fibrosis and promote angiogenesis. This study proposes a promising MI‐treatment patch and highlights the potential of woven technology in processing biomaterials composed of both rigid and elastic materials.
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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".