Low back pain and disc degeneration are decreased following chronic toll-like receptor 4 inhibition in a mouse model
Bibliographic record
Abstract
OBJECTIVE: Intervertebral disc degeneration is a leading cause of chronic low back pain (LBP) but current treatment is limited. Toll-like receptors (TLRs) on disc cells are activated by endogenous extracellular matrix (ECM) fragments and modulate degeneration in vitro. This study investigated whether inhibiting TLR4 slows disc degeneration and reduces behavioral signs of LBP in vivo. DESIGN: 7-9-month old wild-type and secreted protein acidic and rich in cysteine (SPARC)-null (a model of disc degeneration and LBP) male mice were treated with TAK-242 (TLR4 inhibitor) once, and following a 10-day washout, mice were treated 3 times/week for 8 weeks. Behavioral signs of axial discomfort and radiating leg pain were assessed weekly with the grip force assay and acetone test, respectively. Following treatment, pain-related spinal cord changes were evaluated and lumbar discs were excised and cultured. Cytokine secretion from discs was evaluated with protein arrays. RESULTS: SPARC-null mice displayed elevated signs of axial and radiating pain at baseline compared to wild-type. Chronic, but not acute, TLR4 inhibition reduced behavioral signs of pain compared to vehicle. SPARC-null mice have increased calcitonin gene-related peptide (CGRP)- and glial fibrillary acidic protein (GFAP)-immunoreactivity (astrocyte marker) in the dorsal horn compared to wild-type, which is reduced by chronic TLR4 inhibition. Ex vivo degenerating discs from SPARC-null mice secrete increased levels of many pro-inflammatory cytokines, which chronic TLR4 inhibition reduced. CONCLUSION: Chronic TLR4 inhibition decreased behavioral signs of LBP, pain-related neuroplasticity and disc inflammation in SPARC-null mice. TAK-242 inhibits TLR4 activation within discs, as evidenced by decreases in cytokine release. Therefore, TLRs are potential therapeutic targets to slow disc degeneration and reduce pain.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".