Dynamic loading, matrix maintenance and cell injection therapy of human intervertebral discs cultured in a bioreactor
Bibliographic record
Abstract
Low back pain originating from intervertebral disc (IVD) degeneration affects the quality of life for millions of people, and it is a major contributor to global healthcare costs. Long-term culture of intact IVDs is necessary to develop ex vivo models of human IVD degeneration and repair, where the relationship between mechanobiology, disc matrix composition and metabolism can be better understood. A bioreactor was developed that facilitates culture of intact human IVDs in a controlled, dynamically loaded environment. Tissue integrity and cell viability was evaluated under 3 different loading conditions: low 0.1-0.3, medium 0.1-0.3 and high 0.1-1.2 MPa. Cell viability was maintained > 80 % throughout the disc at low and medium loads, whereas it dropped to approximately 70 % (NP) and 50 % (AF) under high loads. Although cell viability was affected at high loads, there was no evidence of sGAG loss, changes in newly synthesised collagen type II or chondroadherin fragmentation. Sulphated GAG content remained at a stable level of approximately 50 µg sGAG/mg tissue in all loading protocols. To evaluate the feasibility of tissue repair strategies with cell supplementation, human NP cells were transplanted into discs within a thermoreversible hyaluronan hydrogel. The discs were loaded under medium loads, and the injected cells remained largely localised to the NP region. This study demonstrates the feasibility of culturing human IVDs for 14 days under cyclic dynamic loading conditions. The system allows the determination a safe range-of-loading and presents a platform to evaluate cell therapies and help to elucidate the effect of load following cell-based therapies.
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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".