Differential expression patterns of cytokines in complex regional pain syndrome
Bibliographic record
Abstract
Complex regional pain syndromes (CRPS) are characterized by persistent and severe pain after trauma or surgery. Neuro-immune alterations are assumed to play a pathophysiological role. Here we set out to investigate whether patients with CRPS have altered systemic pro- and anti-inflammatory cytokine profiles compared to controls on mRNA and protein level. We studied blood cytokine mRNA and protein levels of the pro-inflammatory cytokines tumor necrosis factor-alpha (TNF), interleukin-2 (IL-2) and IL-8 and the anti-inflammatory cytokines IL-4, IL-10, and transforming growth factor-beta1 (TGF beta 1) in 40 prospectively recruited patients with CRPS I, two patients with CRPS II, and 34 controls. Quantitative real-time PCR and enzyme linked immunosorbent assay were used. Additionally, the patients underwent quantitative sensory testing and were assessed with the McGill pain questionnaire and the Hospital anxiety and depression scale. Patients with CRPS had higher blood TNF and IL-2 mRNA levels (p=0.005; p=0.04) and lower IL-8 mRNA levels (p<0.001) than controls. The mRNA for the anti-inflammatory cytokines IL-4 and IL-10 was reduced in the patient group (p=0.004; p=0.006), whereas TGF beta 1 mRNA levels did not differ between groups. These results were paralleled by serum protein levels, except for TGF beta 1, which was reduced in patients with CRPS, and for IL-8, which gave similar protein values in both groups. Sensory testing showed a predominant loss of small fiber-related modalities in the patient group. The shift towards a pro-inflammatory cytokine profile in patients with CRPS suggests a potential pathogenic role in the generation of 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 | 0.001 |
| 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) | 0.001 | 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".