Fractalkine Signaling in Microglia Contributes to Ectopic Orofacial Pain following Trapezius Muscle Inflammation
Bibliographic record
Abstract
Fractalkine (FKN) signaling is involved in mechanical allodynia in the facial skin following trapezius muscle inflammation. Complete Freund's adjuvant (CFA) injection into the trapezius muscle produced mechanical allodynia in the ipsilateral facial skin that was not associated with facial skin inflammation and resulted in FKN but not FKN receptor (CX3CR1) expression, and microglial activation was enhanced in trigeminal spinal subnucleus caudalis (Vc) and upper cervical spinal cord (C1-C2). Intra-cisterna magna anti-CX3CR1 or anti-interleukin (IL)-1β neutralizing antibody administration decreased the enhanced excitability of Vc and C1-C2 neurons in CFA-injected rats, whereas intra-cisterna magna FKN administration induced microglial activation and mechanical allodynia in the facial skin. IL-1β expression and p38 mitogen-activated protein kinase phosphorylation were enhanced in activated microglia after CFA injection. The excitability of neurons whose receptive fields was located in the facial skin was significantly enhanced in CFA-injected rats, and the number of cells expressing phosphorylated extracellular signal-regulated kinase (pERK) following noxious mechanical stimulation of the facial skin was significantly increased in Vc and C1-C2. We also observed mechanical allodynia of the trapezius muscle as well as microglial activation and increased pERK expression in C2-C6 after noxious stimulation of the trapezius muscle in facial skin-inflamed rats. These findings suggest that FKN expression was enhanced in Vc and C1-C2 or C2-C6 following trapezius muscle or facial skin inflammation, microglia are activated via FKN signaling, IL-1β is released from the activated microglia, and the excitability of neurons in Vc and C1-C2 or C2-C6 is enhanced, resulting in the ectopic mechanical allodynia.
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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 | 0.001 |
| 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".