Effect of decellularized adipose tissue particle size and cell density on adipose-derived stem cell proliferation and adipogenic differentiation in composite methacrylated chondroitin sulphate hydrogels
Bibliographic record
Abstract
An injectable composite scaffold incorporating decellularized adipose tissue (DAT) as a bioactive matrix within a hydrogel phase capable of in situ polymerization would be advantageous for adipose-derived stem cell (ASC) delivery in the filling of small or irregular soft tissue defects. Building on previous work, the current study investigates DAT milling methods and the effects of DAT particle size and cell seeding density on the response of human ASCs encapsulated in photo-cross-linkable methacrylated chondroitin sulphate (MCS)-DAT composite hydrogels. DAT particles were generated by milling lyophilized DAT and the particle size was controlled through the processing conditions with the goal of developing composite scaffolds with a tissue-specific 3D microenvironment tuned to enhance adipogenesis. ASC proliferation and adipogenic differentiation were assessed in vitro in scaffolds incorporating small (average diameter of 38 ± 6 μm) or large (average diameter of 278 ± 3 μm) DAT particles in comparison to MCS controls over a period of up to 21 d. Adipogenic differentiation was enhanced in the composites incorporating the smaller DAT particles and seeded at the higher density of 5 × 10(5) ASCs/scaffold, as measured by glycerol-3-phosphate dehydrogenase (GPDH) enzyme activity, semi-quantitative analysis of perilipin expression and oil red O staining of intracellular lipid accumulation. Overall, this study demonstrates that decellularized tissue particle size can impact stem cell differentiation through cell-cell and cell-matrix interactions, providing relevant insight towards the rational design of composite biomaterial scaffolds for adipose tissue engineering.
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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".