Painful, degenerating intervertebral discs up‐regulate neurite sprouting and <scp>CGRP</scp> through nociceptive factors
Bibliographic record
Abstract
Intervertebral disc degeneration (IVD) can result in chronic low back pain, a common cause of morbidity and disability. Inflammation has been associated with IVD degeneration, however the relationship between inflammatory factors and chronic low back pain remains unclear. Furthermore, increased levels of nerve growth factor (NGF) and brain derived neurotrophic factor (BDNF) are both associated with inflammation and chronic low back pain, but whether degenerating discs release sufficient concentrations of factors that induce nociceptor plasticity remains unclear. Degenerating IVDs from low back pain patients and healthy, painless IVDs from human organ donors were cultured ex vivo. Inflammatory and nociceptive factors released by IVDs into culture media were quantified by enzyme-linked immunosorbent assays and protein arrays. The ability of factors released to induce neurite growth and nociceptive neuropeptide production was investigated. Degenerating discs release increased levels of tumour necrosis factor-α, interleukin-1β, NGF and BDNF. Factors released by degenerating IVDs increased neurite growth and calcitonin gene-related peptide expression, both of which were blocked by anti-NGF treatment. Furthermore, protein arrays found increased levels of 20 inflammatory factors, many of which have nociceptive effects. Our results demonstrate that degenerating and painful human IVDs release increased levels of NGF, inflammatory and nociceptive factors ex vivo that induce neuronal plasticity and may actively diffuse to induce neo-innervation and pain in vivo.
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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".