Surface treatment with amino acids of porous collagen based scaffolds to improve cell adhesion and proliferation
Bibliographic record
Abstract
Abstract The purpose of this study was to improve the biocompatibility of glutaraldehyde (GA) cross‐linked chitosan coated collagen scaffold for cartilage tissue regeneration. In order to prevent the potential toxicity of GA, we treated the designed scaffold with either glutamic acid or glycine. Amino acid treated scaffolds were characterized by scanning electron microscopy (SEM) techniques. Afterward, chondrocyte interaction with the composite scaffold was investigated assessing cell adhesion and proliferation using Hoechst staining and MTT cell proliferation assay, respectively. The SEM analyses of the scaffolds’ surface and cross‐section confirmed the adhesion of amino acids on the surface of the scaffolds. We also observed that scaffolds’ porosity was reduced due to the coverage of the pores by chitosan and amino acids, leading to low porosity. The use of amino acid improved the chondrocyte adhesion and proliferation inside the scaffolds’ pores when cells were cultured onto the chitosan‐coated collagen scaffolds. Overall, our in vitro results suggest the use of amino acid to improve the biocompatibility of natural polymer composite scaffold being crosslinked with glutaraldehyde. Such scaffold has improved mechanical properties; biocompatibility thus may be useful for tissue regeneration such as cartilage.
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 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".