Perfusion bioreactor culture of adipose-derived stromal cells on decellularized adipose tissue scaffolds enhances <i>in vivo</i> regeneration
Bibliographic record
Abstract
Abstract Adipose tissue engineering holds promise to address the unmet need in plastic and reconstructive surgery for strategies that promote the stable and predictable regeneration of adipose tissue for volume augmentation applications. Previous studies have demonstrated that decellularized adipose tissue (DAT) scaffolds can provide a pro-adipogenic microenvironment, and that seeding with adipose-derived stromal cells (ASCs) can enhance in vivo angiogenesis and adipogenesis within DAT implants. Recognizing that bioreactor systems can promote cell expansion and infiltration on tissue-engineered scaffolds, this study evaluated the effects of culturing human ASCs on DAT scaffolds within a perfusion bioreactor. Using this system, the impact of both shear stress stimulation and hypoxic preconditioning were explored in vitro and in vivo . Initial studies compared the effects of 14 days of culture within the perfusion bioreactor under 2% O 2 or ~20% O 2 on human ASC expansion and hypoxia inducible factor 1 alpha (HIF-1α) expression in vitro relative to static cultured controls. The findings indicated that culturing within the bioreactor under 2% O 2 significantly increased ASC proliferation on the DAT, with a higher cell density observed in the scaffold periphery. HIF-1α expression was significantly higher when the scaffolds were cultured under 2% O 2 . Subsequent characterization in a subcutaneous implant model in athymic nude mice revealed that in vivo angiogenesis and adipogenesis were markedly enhanced when the ASCs were cultured on the DAT within the perfusion bioreactor under 2% O 2 for 14 days prior to implantation relative to the other culture conditions, as well as additional freshly-seeded and unseeded DAT control groups. Overall, dynamic culture within the perfusion bioreactor system under hypoxia represents a promising approach for preconditioning ASCs on DAT scaffolds to enhance their capacity to stimulate blood vessel formation and infiltration, as well as host-derived adipose tissue regeneration.
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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 | 0.000 |
| 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".