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
Abstract Introduction Evidence has 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 Nineteen healthy adults were clustered into three groups: control (preload only), target total peak force of 400N, and 800N. A validated force-sensing table technology (FSTT ® ) determined the SMT force-time characteristics. Blood samples were collected at pre-intervention, immediately after SMT, and 20 minutes 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 Overall, 400N seemed to decrease blood oxidants, and 800N generally increased blood oxidants, decreased antioxidants and resolvin D1 mediator Conclusion Our findings 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, providing baseline information and direction for future studies in a clinical population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".