Effect of Chitosan Incorporation and Scaffold Geometry on Chondrocyte Function in Dense Collagen Type I Hydrogels
Bibliographic record
Abstract
Tissue engineering approaches for articular cartilage (AC) repair using collagen type I (Coll)-based hydrogels are limited by their low collagen fibril density (CFD; <0.5 wt%) and their poor capacity to support chondrocyte differentiation. Chitosan (CTS) is a well-characterized polysaccharide that mimics the glycosaminoglycans (GAGs) present in native AC extracellular matrix and exhibits chondroprotective properties. Here dense Coll/CTS hydrogel discs (16 mm diameter, 140-250 μm thickness) with CFD (∼6 wt%) approaching that of AC were developed to investigate the effect of CTS content on the growth and differentiation of three-dimensionally seeded RCJ3.1C5.18 chondroprogenitor cells. Compared to dense Coll alone, cells seeded within Coll/CTS showed increased viability and metabolic activity, as well as a decrease in cell-mediated gel contraction. Immunohistochemistry for collagen type II, in combination with Safranin O staining and GAG quantification, indicated greater chondroprogenitor differentiation within Coll/CTS, compared to cells seeded within Coll alone. The complex interplay between scaffold geometry, microstructure, composition, mechanical properties and cell function was further evaluated by rolling dense planar sheets to prepare cylindrically shaped constructs having clinically relevant diameters (3-5 mm diameter, 9 mm height). The compressive modulus of the cylindrically shaped constructs decreased significantly after 7 days in culture, and remained unchanged up to 21 days for each scaffold composition. Unlike Coll, cells seeded within Coll/CTS showed greater viability along the entire radial extent of the cylindrical rolls and increased GAG production at each time point. While GAG content decreased over time and reduced cell viability was observed within the core region of all cylindrical rolls, the incorporation of CTS diminished both these effects. In summary, these findings provide insight into the challenges involved when scaling up scaffolds designed and optimised in vitro for tissue repair.
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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) | 0.001 | 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".