Combining Ketamine with Astrocytic Inhibitor as a Potential Analgesic Strategy for Neuropathic Pain. Ketamine, Astrocytic Inhibitor and Pain
Bibliographic record
Abstract
BACKGROUND: Neuropathic pain is an intractable clinical problem. Intrathecal ketamine, a noncompetitive N-methyl-D-aspartate receptor (NMDAR) antagonist, is reported to be useful for treating neuropathic pain in clinic by inhibiting the activity of spinal neurons. Nevertheless, emerging studies have disclosed that spinal astrocytes played a critical role in the initiation and maintenance of neuropathic pain. However, the present clinical therapeutics is still just concerning about neuronal participation. Therefore, the present study is to validate the coadministration effects of a neuronal noncompetitive N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine and astrocytic cytotoxin L-α-aminoadipate (LAA) on spinal nerve ligation (SNL)-induced neuropathic pain. RESULTS: Intrathecal ketamine (10, 100, 1000 μg/kg) or LAA (10, 50, 100 nmol) alleviated SNL-induced mechanical allodynia in a dose-dependent manner respectively. Phosphorylated NR1 (pNR1) or glial fibrillary acidic protein (GFAP) expression was down-regulated by intrathecal ketamine (100, 1000 μg/kg) or LAA (50, 100 nmol) respectively. The combination of ketamine (100 μg/kg) with LAA (50 nmol) showed superadditive effects on neuropathic pain compared with that of intrathecal administration of either ketamine or LAA alone. Combined administration obviously relieved mechanical allodynia in a quick and stable manner. Moreover, down-regulation of pNR1 and GFAP expression were also enhanced by drugs coadministration. CONCLUSIONS: These results suggest that combining NMDAR antagonist ketamine with an astrocytic inhibitor or cytotoxin, which is suitable for clinical use once synthesized, might be a potential strategy for clinical management of neuropathic pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".