Bibliographic record
Abstract
Despite the increasing clinical demand in reconstructive, cosmetic and correctional surgery there remains no optimal strategy for the regeneration or replacement of adipose tissue. Previous approaches to adipose tissue engineering have failed to create an adipose tissue depot that maintains implant volume in vivo long-term (>3 months). This is due to inadequate mechanical properties of the biomaterial and insufficient vascularization upon implantation. Modular tissue engineering is a means to produce large volume functional tissues from small sub-mm sized tissues with an intrinsic vascularization. We first explored the potential of a semi-synthetic collagen/poloxamine hydrogel with improved mechanical properties to be used as the module biomaterial. We found this biomaterial to not be suitable for adipose tissue engineering because it did not support embedded adipose-derived stem cell (ASC) viability, differentiation and human microvascular endothelial cell (HMEC) attachment. ASC-embedded collagen gel modules coated with HMEC were then implanted subcutaneously in SCID mice to study its revascularization potential. ASC cotransplantation was shown to drive HMEC vascularization in vivo: HMEC were seen to detach from the surface of the modules to form vessels containing erythrocytes as early as day 3; vessels decreased in number but increased in size over 14 days; and persisted for up to 3 months. Early vascularization promoted fat development. Only in the case of ASC-HMEC cotransplantation was progressive fat accumulation observed in the module implants. Although implant volume was not maintained, likely due rapid collagen degradation, the key result here is that ASC-HMEC cotransplantation in the modular approach was successful in creating vascularized adipose tissue in vivo that persisted for 3 months. The modular system was then studied in vitro to further understand ASC-EC interaction. Coculture with ASC was shown to promote an angiogenic phenotype (e.g. sprouting, migration) from HUVEC on modules. RT-PCR analysis revealed that VEGF, PAI-1 and TNFα was involved in ASC-EC paracrine signalling. In summary, ASC-HMEC cotransplantation in modules was effective in rapidly forming a vascular network that supported fat development. Future work should focus on further elucidating ASC-EC interactions and developing a suitable biomaterial to improve adipose tissue development and volume maintenance of engineered constructs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".