Large adipose tissue generation in a mussel-inspired bioreactor of elastic–mimetic cryogel and platelets
Bibliographic record
Abstract
Soft tissue generation, especially in large tissue, is a major challenge in reconstructive surgery to treat congenital deformities, posttraumatic repair, and cancer rehabilitation. The concern is along with the donor site morbidity, donor tissue shortage, and flap necrosis. Here, we report a dissection-free adipose tissue chamber-based novel guided adipose tissue regeneration strategy in a bioreactor of elastic gelatin cryogel and polydopamine-assisted platelet immobilization intended to improve angiogenesis and generate large adipose tissue in situ. In order to have matched tissue mechanics, we used 5% gelatin cryogel as growth substrate of bioreactor. Platelets from the platelet-rich plasma were then immobilized onto the gelatin cryogel with the aid of polydopamine to form a biomimetic bioreactor (polydopamine/gelatin cryogel/platelet). Platelets on the substrate led to a sustained high release in both platelet-derived growth factor and vascular endothelial growth factor compared with non-polydopamine-assisted group. The formed bioreactor was then transferred to a tissue engineering chamber and then inserted above inguinal fat pad of rats without flap dissection. This integrate strategy significantly boomed the vessel density, stimulated cellular proliferation, and upregulated macrophage infiltration. There was a noticeable rise in the expression of dual-angiogenic growth factors (platelet-derived growth factor and vascular endothelial growth factor) in chamber fluid; host cell migration and host fibrous protein secretion coordinated with gelatin cryogel degradation. The regenerated adipose tissue volume gained threefold larger than control group (p < 0.05) with less fibrosis tissue. These results indicate that a big well-vascularized three-dimensional mature adipose tissue can be regenerated using elastic gel, polydopamine, platelets, and small fat tissue.
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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) | 0.000 | 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".