Chitosan-based hydrogels supplemented with gelatine and Link N enhance extracellular matrix deposition by encapsulated cells in a degenerative intervertebral disc environment
Bibliographic record
Abstract
Injectable therapies for intervertebral disc (IVD) repair are gaining much interest. Recently, a chitosan (CH)-based injectable scaffold has been developed that has similar mechanical properties to human nucleus pulposus (NP) and provides a suitable environment for encapsulated NP cell survival and proteoglycan production. The hypothesis of the study was that the biological response of the encapsulated cells can be further increased by adding gelatine and Link N (LN, a naturally occurring peptide present in cartilage and IVD extracellular matrix), known to increase cell adhesion and proteoglycan production, respectively. The effect of gelatine on the mechanical properties of a CH hydrogel was evaluated through rheological and compressive mechanical tests. Production of proteoglycan [assessed as glycosaminoglycan (GAG)] by encapsulated NP cells was determined in the presence or absence of gelatine in normal or degenerative medium supplemented with LN. Normal and degenerative media replicate the healthy and degenerative disc environment, respectively. Gelatine slightly reduced the gelation rate of CH hydrogel but improved its final mechanical properties in compression. LN had a minimal effect in normal medium but induced significantly more GAG production in degenerative medium (p < 0.001, 4.7-fold superior to the control), reaching similar results to transforming growth factor (TGF)-β (used as a positive control). GAG production was further increased in CH-gelatine hydrogels, confirming an additive effect of LN and gelatine in a degenerative environment. The results supported the concept that CH-gelatine hydrogels supplemented with LN can help restore the function of the NP during the early stages of IVD degeneration.
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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) | 0.000 | 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".