Fate of Endothelialized Modular Constructs Implanted in an Omental Pouch in Nude Rats
Bibliographic record
Abstract
Modular tissue engineering is a novel microscale approach that aims to assemble tissue constructs with inherent vascularization. We transplanted endothelialized modules (sub-millimeter-sized collagen gel cylinders covered with human umbilical vein endothelial cell [HUVEC] on the outside surface) in the omental pouch of nude rats to characterize remodeling of the collagen gels and the fate of the transplanted HUVEC. Endothelialized modules randomly assembled in vivo to form channels among individual modules that persisted for at least 14 days. Transplanted HUVEC migrated and formed primitive vessels in these channels; however, host inflammation limited HUVEC survival beyond 3 days. Temporary depletion of peritoneal macrophages (by treatment with clodronate liposomes) prolonged the survival of HUVEC-derived vessels to 7 days, and some vessels appeared to be perfused with host erythrocytes and invested with host vascular cells (either rat von Willebrand factor or smooth muscle alpha-actin-positive cells). Despite treatment, HUVEC were presumed to be still subject to immune rejection. The presence of primitive HUVEC-derived vessels is encouraging in this first in vivo study of the modular approach, in a partially immune-compromised animal model. It suggests that with appropriate attention to the host response to transplanted endothelial cells and improved vessel survival, cells that would be embedded in modules could be adequately perfused.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".