Chimeric Vessel Tissue Engineering Driven by Endothelialized Modules in Immunosuppressed Sprague-Dawley Rats
Bibliographic record
Abstract
Modular tissue engineering is a means of building functional, vascularized tissues using small (∼1 mm long×0.5 mm diameter) components. While this approach is being explored for its utility in adipose and cardiac tissue engineering and in islet transplantation, the initial question in this study was to assess the fate of the endothelial cells (EC) after transplantation delivered on the surface of modules, without an embedded cell. Rat aortic EC-covered collagen gel modules were transplanted into the omental pouch of allogeneic (outbred) Sprague-Dawley rats with and without immunosuppressive drug treatment (atorvastatin and tacrolimus) for 3-60 days. There was a significant increase in vessel density at all time points in the drug treated rats as compared to untreated rats. Green fluorescent protein (GFP)-positive donor rat aortic EC migrated from the surface of the modules and formed primitive vessels by day 7. In the untreated rats, the GFP-positive cells were not seen after day 7. In drug-treated rats, GFP-positive vessels matured over time, accumulated erythrocytes, were supported by host smooth muscle cells, and formed chimeric vessels that survived until day 60. This resulted in the formation of a densely vascularized, perfusable network by day 60. To our knowledge, this is the first study that demonstrates that primary unmodified EC, without the addition of supporting cells, form a chimeric and stable vascular bed in allogeneic, although drug-treated, animals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".