Peptide surface modification of methacrylamide chitosan for neural tissue engineering applications
Bibliographic record
Abstract
Nerve fibres are guided to their targets by the combined actions of chemotactic and haptotactic stimuli; however, translating these stimuli to a scaffold that will promote nerve regeneration is nontrivial. In pursuit of this goal, we synthesized and characterized cell-adhesive, biodegradable chitosan scaffolds. Chitosan amine groups were reacted with methacrylic anhydride resulting in a water soluble methacrylamide chitosan (MC) that was then crosslinked by radical polymerization resulting in a scaffold. Biodegradability by lysozyme and penetrability of the scaffold by rat superior cervical ganglion (SCG) neurons were studied. Maleimide-terminated cell adhesive peptides, mi-GDPGYIGSR and mi-GQASSIKVAV, were coupled to a thiolated form of MC to promote cell adhesion. The MC scaffold was found to be porous, biodegradable, and to allow neurite penetration. Interestingly, all of these properties were found to depend upon the amount of initiator used in crosslinking. Covalent modification of the MC scaffold with cell adhesive peptides significantly improved neuronal adhesion and neurite outgrowth. The MC can be crosslinked to form a novel scaffold, where our results demonstrate its suitability in neural tissue engineering and its potential for other engineered tissues, such as cartilage repair, where chitosan has already demonstrated some utility.
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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".