Chitosan-Coated Collagen Membranes Promote Chondrocyte Adhesion, Growth, and Interleukin-6 Secretion
Bibliographic record
Abstract
Designing scaffolds made from natural polymers may be highly attractive for tissue engineering strategies. We sought to produce and characterize chitosan-coated collagen membranes and to assess their efficacy in promoting chondrocyte adhesion, growth, and cytokine secretion. Porous collagen membranes were placed in chitosan solutions then crosslinked with glutaraldehyde vapor. Fourier transform infrared (FTIR) analyses showed elevated absorption at 1655 cm-1 of the carbon–nitrogen (N=C) bonds formed by the reaction between the (NH2) of the chitosan and the (C=O) of the glutaraldehyde. A significant peak in the amide II region revealed a significant deacetylation of the chitosan. Scanning electron microscopy (SEM) images of the chitosan-coated membranes exhibited surface variations, with pore size ranging from 20 to 50 µm. X-ray photoelectron spectroscopy (XPS) revealed a decreased C–C groups and an increased C–N/C–O groups due to the reaction between the carbon from the collagen and the NH2 from the chitosan. Increased rigidity of these membranes was also observed when comparing the chitosan-coated and uncoated membranes at dried conditions. However, under wet conditions, the chitosan coated collagen membranes showed lower rigidity as compared to dried conditions. Of great interest, the glutaraldehyde-crosslinked chitosan-coated collagen membranes promoted chondrocyte adhesion, growth, and interleukin (IL)-6 secretion. Overall results confirm the feasibility of using designed chitosan-coated collagen membranes in future applications, such as cartilage repair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".