Polycaprolactone nanofibrous mesh reduces foreign body reaction and induces adipose flap expansion in tissue engineering chamber
Bibliographic record
Abstract
Tissue engineering chamber technique can be used to generate engineered adipose tissue, showing the potential for the reconstruction of soft tissue defects. However, the consequent foreign body reaction induced by the exogenous chamber implantation causes thick capsule formation on the surface of the adipose flap following capsule contracture, which may limit the internal tissue expansion. The nanotopographical property and architecture of nanofibrous scaffold may serve as a promising method for minimizing the foreign body reaction. Accordingly, electrospinning porous polycaprolactone (PCL) nanofibrous mesh, a biocompatible synthetic polymer, was attached to the internal surface of the chamber for the reducing local foreign body reaction. Adipose flap volume, level of inflammation, collagen quantification, capsule thickness, and adipose tissue-specific gene expression in chamber after implantation were evaluated at different time points. The in vivo study revealed that the engineered adipose flaps in the PCL group had a structure similar to that in the controls and normal adipose tissue structure but with a larger flap volume. Interleukin (IL)-1β, IL-6, and transforming growth factor-β expression decreased significantly in the PCL group compared with the control. Moreover, the control group had much more collagen deposition and thicker capsule than that observed in the PCL group. These results indicate that the unique nanotopographical effect of electrospinning PCL nanofiber can reduce foreign body reaction in a tissue engineering chamber, which maybe a promising new method for generating a larger volume of mature, vascularized, and stable adipose 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".