Preliminary insights into the effects of spinal manipulation therapy of different force magnitudes on blood biomarkers of oxidative stress and pro-resolution of inflammation mediators
Bibliographic record
Abstract
BACKGROUND: Evidence has been reported that spinal manipulation therapy (SMT) leads to spine segmental hypoalgesia through neurophysiological and peripheral mechanisms related to regulating inflammatory biomarker function. However, these studies also showed substantial inter-individual variability in the biomarker responses. Such variability may be due to the incomplete understanding of the fundamental effects of force-based manipulations (e.g., patient-specific force-time characteristics) on a person's physiology in health and disease. This study investigated the short-term effects of distinct SMT force-time characteristics on blood oxidative stress and pro-resolution of inflammation biomarkers. METHODS: ) determined the SMT force-time characteristics. Blood samples were collected at pre-intervention, immediately after SMT, and 20 min post-intervention. Parameters of the oxidant system (total oxidant status, lipid peroxidation and lipid hydroperoxide), the antioxidant system (total antioxidant capacity and bilirubin), and lipid-derived resolvin D1 were evaluated in plasma and erythrocytes through enzyme-linked immunosorbent assay and colorimetric assays. RESULTS: The COVID-19 global pandemic impacted recruitment, and our pre-established target sample size could not be reached. As a result, there was a small sample size, which decreased the robustness of the statistical analysis. Despite the limitations, we observed that 400 N seemed to decrease systemic total oxidant status and lipid peroxidation biomarkers. However, 800 N appeared to transitorily increase these pro-oxidant parameters with a further transitory reduction in plasma total antioxidant capacity and resolvin D1 mediator. CONCLUSION: Despite the small sample size, which elevates the risk of type II error (false negatives), and the interruption of recruitment caused by the pandemic, our findings appeared to indicate that different single SMT force-time characteristics presented contrasting effects on the systemic redox signalling biomarkers and pro-resolution of inflammation mediators in healthy participants. The findings need to be confirmed by further research; however, they provide baseline information and guidance for future studies in a clinical population.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".