Combinatorial Effect of Biomaterials and Extracellular Vesicle Therapy for Heart Failure with Reduced Ejection Fraction: A Systematic Review of Preclinical Studies
Bibliographic record
Abstract
Heart failure, a pervasive global health burden, necessitates innovative therapeutic strategies. Extracellular vesicles (EVs) have emerged as promising contenders for cardiac repair, owing to their profound influence on fibrosis and inflammation. Merging EVs with biomaterials holds the potential for a synergistic leap in therapeutic efficacy. In this review, the impact of combining EVs with biomaterials in preclinical heart failure models is scrutinized. Fifteen studies, predominantly employing mesenchymal stromal cell-derived EVs along with hyaluronic acid or peptides in coronary ligation models, meet these stringent criteria. The amalgamation of EVs and biomaterials consistently enhances cardiac ejection fraction (1.39; 95% CI: 0.68, 2.11; p = 0.0001) and fractional shortening (1.46, 95% CI: 0.70, 2.22; p = 0.0002) compared to EV monotherapy. Secondary outcomes similarly showcased improvement in the combined treatment group. Although the number of studies analyzed is modest, no indications of publication bias surface. In summary, combination therapy with EVs and biomaterials enhances therapeutic benefit in preclinical heart failure models. The consistent improvement observed across diverse EV sources, biomaterials, and animal models underscores the exciting potential of this synergistic approach.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".