The role of matrix metalloprotease (MMP) to the autolysis of sea cucumber (<i>Stichopus japonicus</i>)
Bibliographic record
Abstract
BACKGROUND: Sea cucumber (Stichopus japonicus) is easy to autolysis in response to a variety of environmental and mechanical factors. In the current study, collagen fibres were extracted from fresh sea cucumber body wall and then incubated with endogenous matrix metalloprotease (MMP) of sea cucumber. Scanning electron microscopy (SEM), differential scanning calorimetry (DSC), chemical analysis and sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) analysis were utilized to demonstrate the changes in collagen fibres, collagen fibrils and collagen proteins. Moreover, a verification experiment was also carried out to confirm the contribution of MMP to the autolysis of sea cucumber. RESULTS: Endogenous MMP caused complete depolymerization of collagen fibres into smaller collagen fibril bundles and collagen fibrils due to the fracture of proteoglycan interfibrillar bridges. Meanwhile, endogenous MMP also caused partial degradation of collagen fibrils by releasing soluble hydroxyproline and pyridinium cross-links. Furthermore, the treatment with MMP inhibitor (1,10-phenanthroline) prevented the autolysis of tissue blocks from S. japonicus dermis. CONCLUSION: Endogenous MMP was the key enzyme in the autolysis of sea cucumber, while its action still focused on high-level structures of collagens especially collagen fibres. © 2019 Society of Chemical Industry.
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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) | 0.001 | 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".