Characterization of Extracellular Matrix Derived From Porcine Organs Decellularized Using Different Methods
Bibliographic record
Abstract
Regenerative medicine has extended the capacity of medicine to a point where tissues and organs could potentially be manufactured. This could resolve issues associated with organ transplantation. The extracellular matrix (ECM) provides a supportive scaffold and biochemical cues allowing cells to attach, proliferate, and differentiate. The ECM is composed of different fibrous proteins and proteoglycans. The extensive value of ECM lies in its dynamic microenvironment that aids in cell proliferation, differentiation, and regulation of intercellular communication. In this study, four methods were applied to decellularize porcine organs. The ECMs were characterized by histological methods illustrating the absence of nuclei and the presence of glycosaminoglycans (GAGs) and collagen. Hematoxylin and eosin analysis of native pancreas revealed necrosis by auto-digestion, also supported by a reduced dsDNA content, and could have led to the destruction of Type IV collagen, laminins, and other proteins in the resulting ECMs, as confirmed by mass spectrometry. DNA quantification of ECM revealed residual dsDNA contents lower than those of the native organs. Bicinchoninic acid (BCA) assay showed a difference in protein content between organs. Mass spectrometry coupled with proteomic analysis highlighted a significant difference in the protein composition. The number of different proteins, in some cases with more than 2700, in the produced ECM depended on the applied decellularization technique. Polarization microscopy indicated differences in the orientation of collagen fibers. This study provides a multimodal approach to characterize ECMs produced using different decellularization techniques, aiding in finding a balance between maintaining the ultrastructure and composition of ECM, while removing cellular components.
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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 | 0.001 | 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".