Injectable, redox-polymerized carboxymethylcellulose hydrogels promote nucleus pulposus-like extracellular matrix elaboration by human MSCs in a cell density-dependent manner
Bibliographic record
Abstract
Low back pain is a major cause for disability and is closely linked to intervertebral disc degeneration. Mechanical and biological dysfunction of the nucleus pulposus in the disc has been found to initiate intradiscal degenerative processes. Replacing or enriching the diseased nucleus pulposus with an injectable, stem cell-laden biomaterial that mimics its material properties can provide a minimally invasive strategy for biological and structural repair of the tissue. In this study, injectable, in situ-gelling carboxymethylcellulose hydrogels were developed for nucleus pulposus tissue engineering using encapsulated human marrow-derived mesenchymal stromal cells (hMSCs). With the goal of obtaining robust extracellular matrix deposition and faster construct maturation, two cell-seeding densities, 20 × 10 6 cells/ml and 40 × 10 6 cells/ml, were examined. The constructs were fabricated using a redox initiation system to yield covalently crosslinked, cell-seeded hydrogels via radical polymerization. Chondrogenic culture of the hydrogels over 35 days exhibited high cell viability along with deposition of proteoglycan and collagen-rich extracellular matrix, and mechanical and swelling properties similar to native human nucleus pulposus. Further, the matrix production and distribution in the carboxymethylcellulose hydrogels was found to be strongly influenced by hMSC-seeding density, with the lower cell-seeding density yielding a more favorable nucleus pulposus-specific matrix phenotype, while the rate of construct maturation was less dependent on the cell-seeding density. These findings are the first to demonstrate the utility of redox-polymerized carboxymethylcellulose hydrogels as hMSC carriers for potential minimally invasive treatment strategies for nucleus pulposus replacement.
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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) | 0.001 | 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".