Mast cell stabilizer ketotifen fumarate reverses inflammatory but not neuropathic-induced mechanical pain in mice
Bibliographic record
Abstract
INTRODUCTION: Mast cell (MC) activation could establish a positive feedback loop that perpetuates inflammation and maintains pain. Stabilizing MCs with ketotifen fumarate (KF) may disrupt this loop and relieve pain. OBJECTIVE: We aimed to test the effect of treatment with KF in pain assays in mice and in a case series of patients with chronic widespread pain. METHODS: ) mice were injected with formalin or complete Freund's adjuvant and treated with KF. Patients with chronic widespread pain (n = 5; age: 13-16 years) who failed to respond to standard of care participated in a 16-week treatment trial with KF (6 mg/d). Ketotifen fumarate's therapeutic effect was evaluated using the patient global impression of change. RESULTS: In the mouse experiments, KF produced dose- and MC-dependent analgesic effects against mechanical allodynia in the acute and chronic inflammatory pain but not neuropathic pain assays. In the patient case series, 4 patients reported that activity limitations, symptoms, emotions, and overall quality of life related to their pain condition were "better" or "a great deal better" since beginning treatment with KF. This was accompanied by improvements in pain comorbid symptoms. CONCLUSION: Treatment with KF is capable of reducing established inflammatory-induced mechanical nociception in an MC-dependent manner in mice, and it may be beneficial for the treatment of chronic pain conditions.
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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) | 0.001 | 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) | 0.002 | 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".