Microencapsulation to reduce mechanical loss of microspheres: implications in myocardial cell therapy
Bibliographic record
Abstract
OBJECTIVE: Previous regenerative studies have demonstrated massive cell losses after intramyocardial cellular delivery. Therefore, efforts at reducing mechanical losses may prove more successful in optimising cellular therapy. In this study, we hypothesized that escalating mesenchymal stem cells (MSCs) dose will not produce corresponding improvement in cardiac function due to washout of the small cells in microcirculation. Using microspheres similar in size to MSCs, that are encapsulated in alginate-poly-l-lysine-alginate (APA), we tested the hypothesis that size is an important factor in early losses. METHODS: In experiment I, five groups of rats (n=9 each) underwent coronary ligation; group I had no treatment; the other groups received escalating 0.5 × 10(6), 1.5 × 10(6), 3 × 10(6) and 5 × 10(6) of MSCs each. Echocardiogram was performed at baseline, 2 days and 7 weeks after surgery. In experiment II, cell-sized microspheres (10 μm) were encapsulated in APA microcapsules. In group I (n=16), rats received bare microspheres, group II (n=16) microspheres within 200 μm microcapsules and in group III (n=16), microspheres within 400 μm microcapsules. After 20 min, hearts were quantified for the amount retained. RESULTS: Myocardial function did not improve further with escalating cell doses beyond an initial response at 1.5 × 10(6) cells. Encapsulated microspheres in 200 μm and 400 μm microcapsules demonstrated a fourfold increase in retention rate compared with 10 μm microspheres. CONCLUSION: We concluded that suboptimal functional improvement in this animal model starts at 1.5 × 10(6) cells and does not respond to escalating cell doses. Improving mechanical retention is possible by increasing the size of the injectate. Microencapsulation could be used to encapsulate donor cells and facilitate functional improvement in cellular heart failure therapy.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".