Heightened presence of inflammatory mediators in the cerebrospinal fluid of patients with trigeminal neuralgia
Bibliographic record
Abstract
Introduction: Trigeminal neuralgia (TN) is a chronic, debilitating facial pain disease causing stabbing pain attacks in the sensory distribution of the trigeminal nerve. The underlying pathophysiology of TN is incompletely understood, although microstructural abnormalities consistent with focal demyelination of the trigeminal nerve root have been shown in patients with TN. Studies of the cerebrospinal fluid (CSF) in patients with TN suggest an increased prevalence of inflammatory mediators, potentially implicating neuroinflammation in the pathophysiology of TN, as it has been implicated in other chronic pain conditions. Objectives: This study aimed to further assess the inflammatory profile of CSF in TN. Methods: Cerebrospinal fluid was collected from 8 medically refractory patients with TN undergoing microvascular decompression surgery and 4 pain-free controls (2 with hemifacial spasm; 2 with normal pressure hydrocephalus). Cerebrospinal fluid was collected from the cerebellopontine angle cistern intraoperatively in the patients with TN. Inflammatory profiles of CSF samples were analyzed using a 71-plex cytokine and chemokine multiplex assay. Results: Ten inflammatory markers were found to be significantly higher in TN CSF, and no analytes were significantly lower. Elevated factors can be classified into pro-inflammatory cytokines (IL-9, IL-18, and IL-33), chemokines (RANTES and ENA-78), the tumor necrosis factor superfamily (TRAIL and sCD40L), and growth factors (EGF, PDGF-AB/BB, and FGF-2). Conclusion: This study further supports the notion that neuroinflammation is present in TN, and that multiple molecular pathways are implicated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".