Development and characterization of matrix‐derived microcarriers from decellularized tissues using electrospraying techniques
Bibliographic record
Abstract
Stirred bioreactor systems integrating microcarriers represent a promising approach for therapeutic cell manufacturing. While a variety of microcarriers are commercially available, current options do not integrate the tissue-specific composition of the extracellular matrix (ECM), which can play critical roles in directing cell function. The current study sought to generate microcarriers comprised exclusively of ECM from multiple tissue sources. More specifically, porcine decellularized dermis, porcine decellularized myocardium, and human decellularized adipose tissue were digested with α-amylase to obtain ECM suspensions that could be electrosprayed into liquid nitrogen to generate 3D microcarriers that were stable over a range of ECM concentrations without the need for chemical crosslinking or other additives. Characterization studies confirmed that all three microcarrier types had similar soft and compliant mechanical properties and were of a similar size range, but that their composition varied depending on the native tissue source. In vivo testing in immunocompetent mice revealed that the microcarriers integrated into the host tissues, supporting the infiltration of host cells including macrophages and endothelial cells at 2 weeks post-implantation. In vitro cell culture studies validated that the novel microcarriers supported the attachment of tissue-specific stromal cell populations under dynamic culture conditions within spinner flasks, with a significant increase in live cell numbers observed over 1 week on the dermal- and adipose-derived microcarriers. Overall, the findings demonstrate the versatility of the electrospraying methods and support the further development of the microcarriers as cell culture and delivery platforms.
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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 | 0.001 |
| 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".