The influence of aging and duration of nerve injury on the antiallodynic efficacy of analgesics in laboratory mice
Bibliographic record
Abstract
INTRODUCTION: Increasing attention is being paid to the effects of organismic factors like age on pain sensitivity. However, very little data exist on this topic using modern algesiometric assays and measures in laboratory rodents. OBJECTIVES: We investigated the effect of age and duration of nerve injury on baseline mechanical thresholds, neuropathic allodynia, and the antiallodynic and analgesic efficacy of 4 systemically administered analgesics: amitriptyline, diclofenac, morphine, and pregabalin. METHODS: Mice of both sexes and 3 conditions were compared: Young-Young, in which baseline testing (von Frey thresholds), the injury producing neuropathic pain (spared nerve injury [SNI]) and subsequent drug testing occurred while mice were young (8-10 weeks); Young-Old, in which mice received the nerve injury while young but were tested for drug efficacy over 10 months later; and Old-Old, in which both the nerve injury and drug testing occurred at approximately 1 year of age. RESULTS: Old-Old mice were found to display higher baseline mechanical sensitivity than other groups. No group differences were seen in SNI-induced allodynia in males; female Young-Old mice were found to display greatly reduced allodynia. With respect to drug efficacy, no differences among conditions were observed for amitriptyline, diclofenac, or morphine. For pregabalin, however, Young-Old mice displayed significantly reduced antiallodynia, and the drug was completely ineffective in Old-Old mice. CONCLUSION: Novel findings include the apparent remission of SNI-induced allodynia in female mice 10 months after injury and reduced pregabalin antiallodynic effects produced by both the passage of time after nerve injury and aging.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".