Cotransplantation of Adipose-Derived Mesenchymal Stromal Cells and Endothelial Cells in a Modular Construct Drives Vascularization in SCID/bg Mice
Bibliographic record
Abstract
A modular approach to adipose tissue engineering was explored by embedding adipose-derived mesenchymal stromal cells (adMSC) in sub-mm-sized collagen rods or "modules" and coating with human microvascular endothelial cells (HMEC). After subcutaneous injection into a SCID/Bg mouse, HMEC on modules containing embedded adMSC appeared to detach from the modules to form vessels as early as day 3, as confirmed by the human EC-specific UEA-1 lectin stain, and these vessels persisted for up to 90 days. Vessel numbers decreased over 14 days, but vessel size increased suggesting a maturing of the vasculature. Vessel perfusion with the host was confirmed at 21 days by microCT. HMEC on modules without embedded adMSC remained attached to the module surface at day 3 and UEA-1 staining disappeared over 14 days suggesting cell death. It appeared that cotransplantation with adMSC had an anti-apoptotic and proangiogenic effect on HMEC. The early revascularization strategy may be successful in supporting adMSC viability and differentiation, as a preliminary study suggests progressive fat accumulation in the HMEC+adMSC implants: ∼60% of the implant area stained positive for Oil Red O by day 90. adMSC-embedded modules without HMEC surface coating did not show similar levels of Oil Red O staining. All implant volumes decreased over the time course of the experiment, yet HMEC+adMSC module implants were larger than adMSC-only implants at day 90. Collagen gel is mechanically weak and contracts in vivo making it unsuitable as a biomaterial for adipose tissue engineering where volume maintenance is critical. When combined with an appropriate biomaterial, the modular approach to adipose tissue engineering may represent a successful strategy to engineer soft tissue substitutes of clinical relevance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".