Thermoreversible hyaluronan-hydrogel and autologous nucleus pulposus cell delivery regenerates human intervertebral discs in an ex vivo, physiological organ culture model
Bibliographic record
Abstract
Numerous studies show promise for cell-based tissue engineering strategies aiming to repair painful intervertebral disc (IVD) degeneration. However, clinical translation to human IVD repair is slow. In the present study, the regenerative potential of an autologous nucleus pulposus (NP)-cell-seeded thermoresponsive hyaluronic acid hydrogel in human lumbar IVDs was assessed under physiological conditions. First, agarose-encased in vitro constructs were developed, showing greater than 90 % NP cell viability and high proteoglycan deposition within HA-pNIPAM hydrogels following 3 weeks of dynamic loading. Second, a bovine-induced IVD degeneration model was used to optimise and validate T1ρ magnetic resonance imaging (MRI) for detection of changes in proteoglycan content in isolated intact IVDs. Finally, isolated intact human lumbar IVDs were pre-scanned using the established MRI sequence. Then, IVDs were injected with HA-pNIPAM hydrogel alone or autologous NP-cell-seeded. Next, the treated IVDs were cultured under cyclic dynamic loading for 5 weeks. Post-treatment T1ρ values were significantly higher as compared to pre-treatment scans within the same IVD and region of interest. Histological evaluation of treated human IVDs showed that the implanted hydrogel alone accumulated proteoglycans, while those that contained NP cells also displayed neo-matrix-surrounded cells within the gel. The study indicated a clinical potential for repairing early degenerative human IVDs using autologous cells/hydrogel suspensions. This unique IVD culture set-up, combined with the long-term physiological culture of intact human IVDs, allowed for a more clinically relevant evaluation of human tissue repair and regeneration, which otherwise could not be replicated using the available in vitro and in vivo models.
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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".