Bioengineered Hydrogels for Restoring Cardiac Electronic Activity after Myocardial Infarction
Bibliographic record
Abstract
Myocardial infarction (MI) is a significant global public health challenge affecting millions of individuals every year. Cardiac tissue engineering (CTE), especially cardiac hydrogels, have emerged as a promising therapeutic strategy for MI. Formation of stiff and non-conductive fibrous scars in the infarcted area is a major cause of fatal ventricular arrhythmias and progressive heart failure. Therefore, restoration of cardiac electrical activity is an important research objective of CTE. Bioactive hydrogels are characterized by highly adjustable physicochemical properties, good biocompatibility, and excellent drug/material-loading capacity. Different types of bioactive hydrogels have been fabricated to improve heart function and restore electrophysiological integrity. This review describes pathophysiological changes after MI and summarizes the design principles and applications of various electroactive hydrogels that have been used to improve cardiac electronic activity after MI. The focus is on the importance of reactivating cardiac electronic activity and fabrication strategies for hydrogels based on the use of a variety of conductive biomaterials, including carbon-based nanomaterials, gold-based nanomaterials, and conductive polymers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".