Comparing Scaffold-Free and Fibrin-Based Adipose-Derived Stromal Cell Constructs for Adipose Tissue Engineering: An In Vitro and in Vivo Study
Bibliographic record
Abstract
Success of adipose tissue engineering for soft tissue repair has been limited by insufficient adipogenic differentiation, an unfavorable host response, and insufficient vascularization. In this study, we examined how scaffold-free spheroid and fibrin-based environments impact these parameters in human adipose-derived stromal cell (ASC)-based adipose constructs. ASCs were differentiated in spheroids or fibrin-based constructs. After 7 days, conditioned medium was collected and spheroids/fibrin-based constructs were either harvested or implanted subcutaneously in athymic mice. Following 7 days of implantation, the number of blood vessels in fibrin-based constructs was significantly higher than in spheroids (93±45 vs. 23±11 vessels/mm(2)), and the inflammatory response to fibrin-based constructs was less severe. The reasons for these results were investigated further in vitro. We found that ASCs in fibrin-based constructs secreted significantly higher levels of the angiogenic factors VEGF and HGF and lower levels of the inflammatory cytokine IL-8. Furthermore, ASCs in fibrin-based constructs secreted significantly higher levels of leptin and showed a 2.5-fold upregulation of the adipogenic transcription factor PPARG and a fourfold to fivefold upregulation of the adipocyte-specific markers FABP4, perilipin, and leptin. These results indicate that fibrin-based ASC constructs are potentially more suitable for ASC-based adipose tissue reconstruction than scaffold-free spheroids.
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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) | 0.000 | 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".