Biochemical and biomechanical comparisions of decellularized scaffolds derived from porcine subcutaneous and visceral adipose tissue
Bibliographic record
Abstract
Decellularized adipose tissue (DAT) is a promising biomaterial for adipose tissue engineering. However, there is a lack of research of DAT prepared from xenogeneic porcine adipose tissue. This study aimed to compare the adipogenic ability of DAT derived from porcine subcutaneous (SDAT) and visceral adipose tissue (VDAT). The retention of key collagen in decellularized matrix was analysed to study the biochemical properties of SDAT and VDAT. For the biomechanical study, both DAT materials were fabricated into three-dimensional (3D) porous scaffolds for rheology and compressive tests. Human adipose-derived stem cells (ADSCs) were cultured on both scaffolds to further investigate the effect of matrix stiffness on cellular morphology and on adipogenic differentiation. ADSCs cultured on soft VDAT exhibited significantly reduced cellular area and upregulated adipogenic markers compared to those cultured on SDAT. In vivo results revealed higher adipose regeneration in the VDAT compared to the SDAT. This study further demonstrated that the relative expression of collagen IV and laminin was significantly higher in VDAT than in SDAT, while the collagen I expression and matrix stiffness of SDAT was significantly higher in comparison to VDAT. This result suggested that porcine adipose tissue could serve as a promising candidate for preparing DAT.
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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) | 0.001 | 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".