Living Nanofiber-Enabled Cardiac Patches for Myocardial Injury
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Because the adult heart has only minimal regenerative capacity, its inability to induce regeneration is well-known in patients with myocardial infarction. However, based on multidisciplinary approaches, it is possible to restore myocardial capability with regenerative medicine via living cardiac patches seeded with therapeutic ingredients ranging from multiple cell types to bioactive molecules, including growth factors, microRNA, and extracellular vesicles to the affected site. Biomaterials, natural and/or synthesized polymers, or in vivo sources such as collagen, fibrin, and decellularized extracellular matrix are used to form these cardiac patches. Herein, we review various techniques where seeded cells and bioactive agents are incorporated within porous nanofibers to create functional cardiac patches that provide myocardial extracellular matrix-like features, mechanical support, and a large surface-to-volume ratio for promoting cellular metabolism as well as compensation for the loss of cardiomyocytes in the infarcted region. We summarize recent advances through electrospinning-generated nanofibers of synthetic and/or natural polymers combined with biological material to create cardiac patches to repair and improve the function of infarcted myocardium. As tailoring designs on cardiac patches have been shown to exhibit deformation mechanisms and enhanced myocardial tissue regeneration, significant roles of various patterns and associated parameters are also discussed. The enhanced delivery of therapeutics offered by tailored nanofiber cardiac patches to treat myocardial infarction and overcome challenges of existing cardiac regeneration therapies such as low stability, short half-lifetime, and delivery methods may promote the potential for their clinical impact on myocardial regeneration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it