Perisurgical amitriptyline produces a preventive effect on afferent hypersensitivity following spared nerve injury
Bibliographic record
Abstract
Following surgery, nerve injury can lead to persistent neuropathic pain. Pre-emptive and preventive analgesic treatments in the perioperative period aim to minimize nerve injury-induced pain. Here we demonstrate that a perioperative regimen of amitriptyline (10 mg/kg i.p. 30 min before and immediately after surgery, followed by oral amitriptyline 15-18 mg/kg/day in the drinking water for 7 days post-surgery) prevents hypersensitivity to a chemogenic stimulus (alphabeta-MeATP, a ligand for P2X3 receptors, together with noradrenaline or NA) in the spared nerve injury (SNI) model in rats. It also prevents hyposensitivity to capsaicin and NA. However, amitriptyline treatment had no effect on the development of mechanical allodynia. We investigated the role of NA mechanisms in the action of amitriptyline by using the neurotoxin 6-hydroxydopamine (6-OHDA) and by examining desipramine. Intrathecal treatment with 6-OHDA on the day of surgery reversed the preventive effect of amitriptyline on hypersensitivity to alphabeta-MeATP/NA, and desipramine exhibited a similar effect to amitriptyline. We also examined the effect of antibodies to the nerve growth factors glial-derived neurotrophic factor (GDNF) and brain-derived neurotrophic factor (BDNF), given intrathecally three times (days 0, 3 and 7) on the action of amitriptyline and observed that the interruption of GDNF and BDNF signaling impaired the prevention of hypersensitivity to alphabeta-MeATP/NA. This study indicates that tricyclic antidepressants given in the perioperative period may be useful in preventing nerve injury-induced sensory changes that contribute to the development of chronic post-surgical 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) | 0.000 | 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) | 0.001 | 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".