Bone Marrow-Derived Mesenchymal Stromal Cells Enhance Chimeric Vessel Development Driven by Endothelial Cell-Coated Microtissues
Bibliographic record
Abstract
Adding bone marrow-derived mesenchymal stromal cells (bmMSCs) to endothelialized collagen gel modules resulted in mature vessel formation, presumably caused in part by the observed display of pericyte-like behavior for the transplanted GFP(+) bmMSCs. A previous study determined that rat aortic endothelial cells (RAECs) delivered on the surface of small (∼0.8 mm long×0.5 mm diameter) collagen gel cylinders (microtissues, modular tissue engineering) formed vessels after transplantation into immunosuppressed Sprague-Dawley (SD) rats. Although the RAECs formed vessels in this allogeneic transplant model, there was a robust inflammatory response and the vessels that formed were leaky as shown by microcomputed tomography (microCT) perfusion studies. In vitro assays showed that SD rat bmMSCs embedded into the collagen gel modules increased the extent of EC proliferation and enhanced EC sprouting. In vivo, although vessel number was not affected, the new vessels formed by the bmMSCs and RAECs were more stable and leaked less in the microCT perfusion analysis than vessels formed by implanted RAECs alone. Addition of the bmMSCs also decreased the total number of CD68(+) macrophages that infiltrated the implant and changed the distribution of CD163(+) (M2) macrophages so that they were found within the newly developed vascularized tissue. Most interestingly, the bmMSCs became smooth muscle actin positive and migrated to surround the EC layer of the vessel, which is the location typical of pericytes. The combination of these two effects was presumed to be the cause of improved vascularity when bmMSCs were embedded in the EC-coated modules. Further exploration of these observations is warranted to exploit modular tissue engineering as a means of forming large vascularized functional tissues using microtissue components.
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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 | 0.000 |
| 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".