Effect of MK‐801, memantine and lamotrigine on cold and heat hypernociception in trigeminal neuropathic pain
Bibliographic record
Abstract
This study evaluated the effects of the anticonvulsant lamotrigine (LAM) and NMDA receptor antagonists memantine (MEM) and MK‐801 in the trigeminal hypernociception triggered by heat or cold after infraorbital nerve (ION) constriction. Trigeminal neuropathy was induced in male Wistar rats by two ligatures around the ION. Sham animals were submitted to the same procedure without ION ligatures. At 4 and 6 days after surgery (maximal hypernociception for cold and heat, respectively), sham and constricted groups received LAM (30 mg/Kg, ip), MEM (0.3–3 mg/Kg, po) or MK‐801 (5 nM/50 μL, in the lip) or vehicle and noxious cold (tetrafluorethane spray) or heat (radiant heat) stimulus was applied at 30 min intervals for 6h, ipsilaterally to the surgery. Facial grooming time and latency for flicking snout/vibrissae was recorded as an index of nociceptive responsiveness. ION injury increased the total facial grooming time for cold stimulation from 8.6 ± 1.3 to 28.9 ± 2.9 s and reduced the reaction time after heat stimulation from 10.6 ± 0.4 to 4.2 ± 0.4 s. LAM, MEN and MK‐801 reduced the cold hypernociception by 95%, 97% and 51% and the heat hypernociception by 87%, 92% and 49%, respectively. The results suggest that LAM, MEM and MK‐801 are effective in the reversal of the cold and heat hypernociception after ION contriction and that local glutamate release may have a role in the maintenance of this condition. Sup. CAPES/Fund. Araucária.
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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 | 0.001 |
| 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".