Analgesic Effect of Bumetanide on Neuropathic Pain in Patients with Spinal Cord Injury
Bibliographic record
Abstract
Objectives: The current study evaluated the analgesic effects of bumetanide as an adjunctive treatment in the management of neuropathic pain following spinal cord injury. The peripheral expression of Na-K-Cl-cotransporter-1 1 and K-Cl-cotransporter-2 (KCC2)2 genes in polymorphonuclear lymphocytes (PMLs) assessed as a possible biomarker indicating central mechanisms underlying the observed response. Methods: Through an open-label, single arm, pilot trial of bumetanide (2mg/day), as add-on treatment conducted in 14 SCI patients for 19 weeks. This study consisted of 3 phases: pre-treatment (1month), titration (3 weeks), and active treatment (4 months). Ultimately, 9 patients completed the study. The primary outcome variables were the endpoint pain score using the numeric rating scale (NRS), and also the short-form McGill Pain Questionnaire. Secondary endpoints included the Short-Form Health Survey that assesses the quality of life. Blood samples were collected and used for determining the expression of NKCC1 and KCC2 genes in transcription and translation levels. Results: Bumetanide treatment significantly decreased average pain intensity according to the NRS and the short form of the McGill Pain Questionnaire scores. Baseline expression of KCC2 protein was low between groups and increased significantly following treatment (P<0.05). In the current study, pain improvement accompanied by the greater mean change from the baseline (improvement) for the overall quality of life. Conclusions: These data highlighted the analgesic effect of bumetanide on neuropathic pain and indicated the probable role of upregulation of KCC2 protein and involvement of GABAergic disinhibition in producing neuropathic pain.
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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) | 0.001 | 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) | 0.001 | 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".