The influence of molecular weight of chitosan on the physical and biological properties of collagen/chitosan scaffolds
Bibliographic record
Abstract
Biopolymer blends between collagen and chitosan have the potential to produce cell scaffolds with biocompatible properties. However, the relationship between the molecular weight of chitosan and its effect on physical and biological properties of collagen/chitosan scaffolds has not been elucidated yet. Porous scaffolds were fabricated by freeze-drying the solution of collagen and chitosan, followed by cross-linking by dehydrothermal treatment. Various types of scaffolds were prepared using chitosan with various molecular weights and blending ratios. Fourier transform infrared spectroscopy proved that collagen and chitosan scaffolds at all blending ratios contained mainly electrostatic interactions at the molecular level. The compressive modulus decreased with increasing the concentration of chitosan. Equilibrium swelling ratios of approximately 6-8, determined in phosphate-buffered saline at physiological pH (7.4), were found in case of collagen-dominated scaffolds. The lysozyme biodegradation test demonstrated that the presence of chitosan, especially the high-molecular-weight species, could significantly prolong the biodegradation of collagen/chitosan scaffolds. In vitro culture of L929 mouse connective tissue fibroblast evidenced that low-molecular-weight chitosan was more effective to promote and accelerate cell proliferation, particularly for scaffolds containing 30 wt% chitosan. The results elucidated that the blends of collagen with low-molecular-weight chitosan have a high potential to be applied as new materials for skin-tissue engineering.
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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 | 0.001 |
| 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".