Culture on Tissue‐Specific Coatings Derived from α‐Amylase‐Digested Decellularized Adipose Tissue Enhances the Proliferation and Adipogenic Differentiation of Human Adipose‐Derived Stromal Cells
Bibliographic record
Abstract
While extracellular matrix (ECM)-derived coatings have the potential to direct the response of cell populations in culture, there is a need to investigate the effects of ECM sourcing and processing on substrate bioactivity. To develop improved cell culture models for studying adipogenesis, the current study examines the proliferation and adipogenic differentiation of human adipose-derived stem/stromal cells (ASCs) on a range of ECM-derived coatings. Human decellularized adipose tissue (DAT) and commercially available bovine tendon collagen (COL) are digested with α-amylase or pepsin to prepare the coatings. Physical characterization demonstrates that α-amylase digestion generates softer, thicker, and more stable coatings, with a fibrous tissue-like ultrastructure that is lost in the pepsin-digested thin films. ASCs cultured on the α-amylase-digested ECM have a more spindle-shaped morphology, and proliferation is significantly enhanced on the α-amylase-digested DAT coatings. Further, the α-amylase-digested DAT provides a more pro-adipogenic microenvironment, based on higher levels of adipogenic gene expression, glycerol-3-phosphate dehydrogenase (GPDH) enzyme activity, and perilipin staining. Overall, this study supports α-amylase digestion as a new approach for generating bioactive ECM-derived coatings, and demonstrates tissue-specific bioactivity using adipose-derived ECM to enhance ASC proliferation and adipogenic differentiation.
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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".