Clinical Biomarker of Sterile Inflammation, HMGB1, in Patients with Chronic Non-Specific Low Back Pain: A Pilot Cross-Sectional Study
Bibliographic record
Abstract
The present study explores whether the inflammatory biomarker of sterile inflammation, high mobility box 1 (HMGB1), contributes to the inflammatory/nociceptive pathophysiology that characterizes chronic non-specific low back pain (LBP). Patients with chronic LBP (N = 10, >3 pain score on a 11-point Visual Analogue Scale, VAS) and asymptomatic participants (N = 12) provided peripheral blood (PB) samples. The proportion of classical CD14++ monocytes within PB leukocytes was determined by flow cytometry. The plasma and extracellular HMGB1 levels in unstimulated adherent cell (AC) cultures were measured using specific immunoassays. HMGB1 localization in ACs was assessed by immunofluorescent staining. The relative gene expression levels of tumor necrosis factor α (TNFα), interleukin-1 beta (IL-1β) and HMGB1 were determined by quantitative polymerase chain reaction (qRT-PCR) in relation to the pain intensity (11-point VAS scores) in patients with LBP. The extracellular release of HMGB1 in the LBP patient AC cultures was significantly elevated (p = 0.001) and accompanied by its relocation into the cytoplasm from the nuclei. The number of CD14++ monocytes in the patients’ PB was significantly (p = 0.03) reduced, while the HMGB1 plasma levels remained comparable to those of the controls. The mRNA levels of TNFα, IL-1β and HMGB1 were overexpressed relative to the controls and those of HMGB1 and IL-1β were correlated with the VAS scores at a significant level (p = 0.01–0.03). The results suggest that HMGB1 may play an important role in the pathophysiology of chronic non-specific LBP.
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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 | 0.001 |
| 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".