A New Role for Extracellular Vesicles in Cardiac Tissue Engineering and Regenerative Medicine
Bibliographic record
Abstract
Cardiovascular diseases are the leading cause of death worldwide. Discovering new therapies to treat heart disease requires improved understanding of cardiac physiology at a cellular level. Extracellular vesicles (EVs) are plasma membrane-bound nano- and microparticles secreted by cells and known to play key roles in intercellular communication, often through transfer of biomolecular cargo. Advances in EV research have established techniques for EV isolation from tissue culture media or biofluids, as well as standards for quantitation and biomolecular characterization. EVs released by cardiac cells are known to be involved in regulating cardiac physiology as well as in the progression of myocardial diseases. Due to difficulty accessing the heart in vivo, advanced in vitro cardiac 'tissues-on-a-chip' have become a recent focus for studying EVs in the heart. These physiologically relevant models are producing new insight into the role of EVs in cardiac physiology and disease while providing a useful platform for screening novel EV-based therapeutics for cardiac tissue regeneration post-injury. Numerous hurdles have stalled the clinical translation of EV therapeutics for heart patients, but tissue-on-a-chip models are playing an important role in bridging the translational gap, improving mechanistic understanding of EV signalling in cardiac physiology, disease, and repair.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".