Influence of Nociception and Stress-induced Antinociception on Genetic Variation in Isoflurane Anesthetic Potency among Mouse Strains
Bibliographic record
Abstract
BACKGROUND: Genetic background influences anesthetic potency to suppress motor response to noxious stimulation (minimum alveolar concentration [MAC]) as well as nociceptive sensitivity in unmedicated animals. However, the influence on MAC of baseline sensitivity to the noxious stimuli used to assess MAC has virtually never been studied. The authors assessed room air nociceptive sensitivity and isoflurane MAC in multiple mouse strains. Isoflurane requirement for loss of righting response (MACLORR) was also measured. METHODS: One outbred and 10 inbred mouse strains were tested for latency to respond (in room air) to a tail clip (either 500 g or 2,000 g). Naive mice of the same 11 strains were tested for isoflurane MAC and MACLORR. To assess the role of opioid-mediated stress-induced antinociception, mice were also tested for nociceptive sensitivity after injection of naloxone (10 mg/kg) or saline. RESULTS: Robust strain differences were observed for all measures. The authors found that tail-clip latency (using a 500-g or 2,000-g clip, respectively) correlated significantly with MAC (r = -0.76 and -0.58, respectively) but not MACLORR (r = -0.10 and -0.26). Naloxone produced strain-dependent reductions in open air tail-clip latencies, and these reductions were also strongly correlated with MAC (r = -0.67 and -0.71). CONCLUSIONS: The authors suggest that genetic variability in isoflurane MAC (but not MACLORR) may reflect genetic variability in the underlying sensitivity to the noxious stimulus being used to measure MAC. This variable sensitivity to nociception in the awake state is at least partially mediated by endogenous antinociceptive mechanisms activated by the tail-clip stimulus itself.
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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 | 0.000 | 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 | 0.000 |
| 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".