Design and Characterization of Tissue-Specific Extracellular Matrix-Derived Microcarriers
Bibliographic record
Abstract
The three-dimensional (3D) extracellular matrix (ECM) environment plays a critical role in mediating normal cellular behavior and tissue organization. While commercially available microcarriers have shown promise, limited research has been conducted on the design of tissue-specific, custom-fabricated microcarriers, engineered to mimic the composition of the native ECM of cells or tissues of interest. Moving toward this goal, methods were developed to fabricate microcarriers from decellularized adipose tissue (DAT) via minimally-cytotoxic protocols. Characterization by microscopy confirmed the production of stable spherical microcarriers, with a microporous surface topography and porous interior. The mean diameter of the DAT microcarriers was 934±51 μm, while the porosity was estimated as 29%±4% using liquid displacement. Stability and swelling behavior over 4 weeks indicated that the DAT microcarriers were effectively stabilized with the photochemical crosslinking agent rose bengal, with total protein release in a simulated physiological environment remaining below 10 μg/mL at all time points. Preliminary cell culture studies with human adipose-derived stem cells (ASCs) in a spinner flask system indicated enhanced cell attachment and proliferation of ASCs on DAT microcarriers over 14 days, as compared with gelatin control microcarriers fabricated using similar methods. Testing confirmed injectability of the DAT microcarriers, further supporting the clinical potential of the approach for localized cell delivery and small volume augmentation in plastic and reconstructive surgery. Overall, tissue-specific microcarriers prepared from solubilized DAT were found to be highly supportive of human ASCs cultured in a 3D dynamic environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".