Assessment of the Impact of Decellularization Methods on Mechanical Properties of Biocomposites Used as Skin Substitute
Bibliographic record
Abstract
This work aimed to assess the impact of acellularization and sterilization methods on the mechanical properties of biocomposites used as a skin substitute. On the basis of the statistical analysis, it was ascertained that the values of the Young modulus for the samples before the sterilization process-only in the cases of substances such as: trypsin, 15% glycerol and dispase-changed in a statistically significant way. In the case of dispase, the Young modulus value before the sterilization process amounted to 66.6 MPa, for trypsin this value equalled 33.9 MPa, whereas for 15% glycerol it was 11 MPa. In the case of samples after the completion of the sterilization process, the analysis did not show any statistically significant differences between the obtained results of Young's modulus depending on the respective reagents applied. It was confirmed that different methods of acellularization and the process of sterilization effect the alteration of mechanical properties of allogeneic skins. In the case of the decellularization method using SDS (Sodium Dodecyl Sulfate), liquid nitrogen and 85% glycerol the highest values of strain were observed. In the authors' opinion, it is the above-mentioned methods that should be recommended in the process of preparation of skin substitutes.
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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 | 0.000 | 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".