Ex vivo intervertebral disc cultures: degeneration-induction methods and their implications for clinical translation
Bibliographic record
Abstract
Because low back pain is frequently a result of intervertebral disc degeneration (IVDD), strategies to regenerate or repair the IVD are currently being investigated. Often, ex vivo disc cultures of non-human IVD organs or tissue explants are used that usually do not exhibit natural IVDD. Therefore, degenerative changes mimicking those reported in human IVDD need to be induced. To support researchers in selecting ex vivo disc cultures, a systematic search was performed for them and their potential use for studying human IVDD reviewed. Five degeneration induction categories (proinflammatory cytokines, injury/damage, degenerative loading, enzyme, and other) were identified in 129 studies across 7 species. Methods to induce degeneration are diverse and can induce mild to severe degenerative changes that progress over time, as described for human IVDD. The induced degenerative changes are model-specific and there is no "one-fits-all" IVDD induction method. Nevertheless, specific aspects of human IVDD can be well mimicked. Currently, spontaneously degenerated disc cultures from large animals capture human IVDD in most aspects. Combinatorial approaches of several induction methods using discs derived from large animals are promising to recapitulate pathological changes on several levels, such as cellular behaviour, extracellular matrix composition, and biomechanical function, and therefore better mimic human IVDD. Future disc culture setups might increase in complexity, and mimic human IVDD even better. As ex vivo disc cultures have the potential to reduce and even replace animal trials, especially during preclinical development, advancement of such models is highly relevant for more efficient and cost-effective clinical translation from bench-to-bedside.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".