Differential expression patterns of cytokines in complex regional pain syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| 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) | 0.000 | 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 it