Flexible and highly interconnected, multi-scale patterned chitosan porous membrane produced in situ from mussel shell to accelerate wound healing
Bibliographic record
Abstract
Utilization of the underlying mechanisms of biological systems is the principal endeavor of biomimetics, the primary goal of which is to treat on-going biological processes. From the perspective of tissue engineering, one purpose of biomimetics is to create highly cellular- or tissue-favored environments for bio-defect repair. Marine creatures such as mussels have inspired bioengineers to design ideal cellular substrates, strong adhesives, and other bioengineering materials. Herein, we report a novel mussel shell-derived membrane for wound dressing. Mussel shell in situ manufactured a highly flexible membrane with a regular porous pattern after the direct action of acid (A-shell) followed by base treatment (B-shell). The SEM images display elegantly patterned polygons with nanowalls (about 710 nm). Compared with the A-shell, the B-shell has a more defined and flexible structure. FTIR characterization of the structures indicates that deacetylation occurred on the B-shell. A cellular toxicity study was conducted to determine the optimized processing parameters before applying the wound healing model. The B-shell significantly closed the wound at an early stage (day 10) followed by complete contraction at a later stage (day 21). This is completely consistent with the higher level of α-SMA protein, which accelerates wound contraction in the wound sites. As a key index of the integration between host and guest, a high blood vessel density was detected in both the A-shell and B-shell groups. The treated shells can improve epidermal migration, the formation of granulation tissue, neovascularization and hair follicles, and reduce scar tissue. Our mussel shell-derived membrane could have potential as a wound dressing and other biomedical uses.
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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".