Interleukin-8 as a therapeutic target for chronic low back pain: Upregulation in human cerebrospinal fluid and pre-clinical validation with chronic reparixin in the SPARC-null mouse model
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is the leading global cause of disability and is associated with intervertebral disc degeneration (DD) in some individuals. However, many adults have DD without LBP. Understanding why DD is painful in some and not others may unmask novel therapies for chronic LBP. The objectives of this study were to a) identify factors in human cerebrospinal fluid (CSF) associated with chronic LBP and b) examine their therapeutic utility in a proof-of-concept pre-clinical study. METHODS: Pain-free human subjects without DD, pain-free human subjects with DD, and patients with chronic LBP linked to DD were recruited and lumbar MRIs, pain and disability levels were obtained. CSF was collected and analyzed by multiplex cytokine assay. Interleukin-8 (IL-8) expression was confirmed by ELISA in CSF and in intervertebral discs. The SPARC-null mouse model of progressive, age-dependent DD and chronic LBP was used for pre-clinical validation. Male SPARC-null and control mice received systemic Reparixin, a CXCR1/2 (receptors for IL-8 and murine analogues) inhibitor, for 8 weeks. Behavioral signs of axial discomfort and radiating pain were assessed. Following completion of the study, discs were excised and cultured, and conditioned media was evaluated with a protein array. FINDINGS: IL-8 was elevated in CSF of chronic LBP patients with DD compared to pain-free subjects with or without DD. Chronic inhibition with reparixin alleviated low back pain behaviors and attenuated disc inflammation in SPARC-null mice. INTERPRETATION: These studies suggest that the IL-8 signaling pathway is a viable therapy for chronic LBP. FUND: Supported by NIH, MMF, CIHR and FRQS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".