The Effect of Chondroitin Sulphate and Hyaluronic Acid on Chondrocytes Cultured within a Fibrin-Alginate Hydrogel
Bibliographic record
Abstract
Osteoarthritis is a painful degenerative joint disease that could be better managed if tissue engineers can develop methods to create long-term engineered articular cartilage tissue substitutes. Many of the tissue engineered cartilage constructs currently available lack the chemical stimuli and cell-friendly environment that promote the matrix accumulation and cell proliferation needed for use in joint cartilage repair. The goal of this research was to test the efficacy of using a fibrin-alginate hydrogel containing hyaluronic acid (HA) and/or chondroitin sulphate (CS) supplements for chondrocyte culture. Neonatal porcine chondrocytes cultured in fibrin-alginate hydrogels retained their phenotype better than chondrocytes cultured in monolayer, as evidenced by analysis of their relative expression of type II versus type I collagen mRNA transcripts. HA or CS supplementation of the hydrogels increased matrix glycosaminoglycan (GAG) production during the first week of culture. However, the effects of these supplements on matrix accumulation were not additive and were no longer observed after two weeks of culture. Supplementation of the hydrogels with CS or a combination of both CS and HA increased the chondrocyte cell population after two weeks of culture. Statistical analysis indicated that the HA and CS treatment effects on chondrocyte numbers may be additive. This research suggests that supplementation with CS and/or HA has positive effects on cartilage matrix production and chondrocyte proliferation in three-dimensional (3D) fibrin-alginate hydrogels.
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) | 0.001 | 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".