Pain-associated Mediators and Axon Pathfinders in Fibromyalgia Skin Cells
Bibliographic record
Abstract
OBJECTIVE: To investigate whether the expression of cytokine, nociception-associated ion channel, and axon guidance genes in patients with skin cell fibromyalgia syndrome (FMS) differs from healthy controls, potentially contributing to pain and small-fiber degeneration in FMS. METHODS: We prospectively recruited 128 patients and 26 healthy controls. All study participants underwent neurological examination, and a skin punch biopsy was obtained from the lateral calf and thigh. Skin samples were processed to histologically determine intraepidermal nerve fiber density (IENFD) and for primary fibroblast and keratinocyte cell cultures. Gene expression of selected pro- and antiinflammatory cytokines, nociception-associated ion channels, and axon guidance cues was assessed with quantitative real-time PCR. RESULTS: In fibroblasts, transforming growth factor-ß1 (TGF-ß1) gene expression was higher in patients with FMS compared to controls (calf and thigh: p < 0.001). Also, expression was higher in patients than in controls for these variables: hyperpolarization-activated cyclic nucleotide-gated ion channel 2 (calf: p < 0.01), ephrin-A4 (EFNA4; calf: p < 0.05, thigh: p < 0.001), and ephrin receptor-A4 (EPHA4; thigh: p < 0.05). In keratinocytes, interleukin 10 gene expression was higher in patients with FMS than in controls (thigh: p < 0.05). While no intergroup difference was found for nociception-associated ion channels, EFNA4 and EPHA4 (calf: p < 0.01 each) expression was higher in patients with FMS than in controls. Axon guide expression did not correlate with IENFD. CONCLUSION: In FMS, skin cells may contribute to cutaneous nociception by differentially expressing membrane-bound and soluble pain mediators and axon pathfinders.
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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 | 0.000 |
| 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) | 0.002 | 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".