Toll‐like receptor involvement in adolescent scoliotic facet joint degeneration
Bibliographic record
Abstract
Facet joint osteoarthritis is prevalent in young patients with adolescent idiopathic scoliosis (AIS) and might contribute to back pain. Toll-like receptors (TLR) have been linked to cartilaginous tissue degeneration but their involvement in facet joint osteoarthritis in AIS patients is still unknown. We compared baseline gene expression levels of TLRs -1, -2, -4, and -6 in scoliotic and non-scoliotic chondrocytes and found higher expression levels in scoliotic chondrocytes with significantly higher TLR2 levels. Furthermore, TLR expression correlated strongly and significantly with inflammatory and catabolic markers in scoliotic but not in non-scoliotic chondrocytes. TLR activation with a synthetic TLR2/6 agonist resulted in a robust induction and release of pro-inflammatory and catabolic factors which exacerbated proteoglycan loss in scoliotic but not in non-scoliotic cartilage. We also detected a higher abundance of alarmins including S100A8/9 and biglycan in scoliotic cartilage. Finally, the small-molecule antagonists Sparstolonin B and o-Vanillin reduced catabolism following induction with naturally occurring alarmins and the synthetic TLR2/6 agonist. The high baseline expression, robust responsiveness and strong and significant correlation with proteases and pro-inflammatory cytokines suggest that TLRs are key regulators of facet joint degeneration in AIS. Blocking their activity could therefore potentially modify disease progression.
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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 | 0.001 | 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) | 0.002 | 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".