Response to pregabalin and progesterone differs in male and female rat models of neuropathic and cancer pain
Bibliographic record
Abstract
Background: Cancer pain involves nervous system damage and pathological neurogenesis. Neuropathic pain arises from damage to the nervous system and is driven by ectopic signaling. Both progesterone and pregabalin are neuroprotective in animal models, and there is evidence that both drugs bind to and inhibit voltage-gated calcium channels.Aims: This study was designed to characterize the effects of progesterone and pregabalin in preclinical models of cancer and neuropathic pain in both sexes.Methods: We measured peripheral sensory signaling by intracellular in vivo electrophysiology and behavioral indicators of pain in rat models of cancer-induced bone pain and neuropathic pain.Results: Female but not male models of cancer pain showed a behavioral response to treatment and pregabalin reduced excitability in C and A high-threshold but not low-threshold sensory neurons of both sexes. Male models of neuropathic pain treated with pregabalin demonstrated higher signaling thresholds only in A high-threshold neurons, and behavioral data indicated a clear recovery to baseline mechanical withdrawal thresholds in all treatment groups. Female rat treatment groups did not show excitability changes in sensory neurons, but all demonstrated higher mechanical withdrawal thresholds than vehicle-treated females, although not to baseline levels. Athymic female rat models of neuropathic pain showed no behavioral or electrophysiological responses to treatment.Conclusions: Both pregabalin and progesterone showed evidence of efficacy in male models of neuropathic pain. These results add to the evidence demonstrating differential effects of treatments for pain in male and female animals and widely differing responses in models of cancer and neuropathic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.000 |
| 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".