Porous, Ventricular Extracellular Matrix-Derived Foams as a Platform for Cardiac Cell Culture
Bibliographic record
Abstract
To more closely mimic the native cellular microenvironment, 3D scaffolds derived from the extracellular matrix (ECM) are being developed as alternatives to conventional 2D culture systems. In the present study, we established methods to fabricate nonchemically cross-linked 3D porous foams derived entirely from decellularized porcine left ventricle (DLV) for use as an in vitro cardiac cell culture platform. Furthermore, we explored the effects of physically preprocessing the DLV through mechanical mincing versus cryomilling, as well as varying the ECM concentration on the structure, composition, and physical properties of the foams. Our results indicate that the less highly processed minced foams had a more cohesive and complex network of ECM components, enhanced mechanical properties, and improved stability under simulated culturing conditions. To validate the DLV foams, a proof-of-concept study was conducted to explore the early cardiomyogenic differentiation of pericardial fat adipose-derived stem/stromal cells (pfASCs) on the minced DLV foams relative to purified collagen I gel controls. Differentiation was induced using a modified cardiomyogenic medium (MCM) or through stimulation with 5-azacytidine (5-aza), and cardiomyocyte marker expression was characterized by immunohistochemistry and real-time reverse transcriptase-polymerase chain reaction. Our results indicate that early markers of cardiomyogenic differentiation were significantly enhanced on the DLV foams cultured in MCM, suggesting a synergistic effect of the cardiac ECM-derived scaffolds and the culture medium on the induction of pfASC differentiation. Furthermore, in analyzing the response in the noninduced control groups, the foams were observed to provide a mildly inductive microenvironment for pfASC cardiomyogenesis, supporting the rationale for using tissue-specific ECM as a substrate for cardiac cell culture applications.
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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) | 0.000 | 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".