Chitosan-based asymmetric topological membranes with cell-like features for healthcare applications
Bibliographic record
Abstract
Chitosan-based guided tissue regeneration (GTR) membranes are extensively used in orthopedic/stomatological regenerative medicine since chitosan shares many chemical and structural similarities with glycosaminoglycans (GAGs) in the extracellular matrix. However, the available chitosan-based GTR membranes mostly lack topological features of natural tissues, resulting in unsatisfactory biocompatibility. To address this limitation, we developed a novel biologically-inspired asymmetric topological chitosan (ATCS) membrane supported by a nanoporous anodic aluminum oxide (AAO) template. We, thereafter, investigated the mechanical properties, degradation, and cytocompatibility of the ATCS membranes and compared them with those of the symmetric chitosan (SyCS) membranes, produced with a smooth Al template. The asymmetric topological structure significantly increased the tensile strength but decreased the extent of degradation of the ATCS membranes compared to those of SyCS. In the in vitro studies, the ATCS membranes outperformed the SyCS membranes in cytocompatibility due to their cell-like features. In addition to the ATCS membranes, the ethylene vinyl acetate (EVA) membranes with a similar cell-like structure were successfully fabricated using the AAO template to verify the universality of the AAO template-assisted technique. Accordingly, the AAO template-assisted strategy, defined in this study, is an innovative, universal, and facile way to fabricate polymeric asymmetric membranes with cell-like features. The bioengineered ATCS membranes with tunable degradability, prominent mechanical properties and biocompatibility are promising candidates for orthopedic healthcare 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 | 0.001 | 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".