The Effects of the Toll-Like Receptor 4 Antagonist, Ibudilast, on Sevoflurane’s Minimum Alveolar Concentration and the Delayed Remifentanil-Induced Increase in the Minimum Alveolar Concentration in Rats
Bibliographic record
Abstract
BACKGROUND: Ultralow doses of naloxone, an opioid and toll-like receptor 4 antagonist, blocked remifentanil-induced hyperalgesia and the associated increase in the minimum alveolar concentration (MAC), but not tolerance. The aim was to determine the effects of the toll-like receptor 4 antagonist, ibudilast, on the MAC in the rat and how it might prevent the effects of remifentanil. METHODS: Male Wistar rats were randomly allocated to 5 treatment groups (n = 7 per group): 10 mg/kg ibudilast intraperitoneally, 240 µg/kg/h remifentanil IV, ibudilast plus remifentanil, remifentanil plus naloxone IV, or saline. The sevoflurane MAC was determined 3 times in every rat and every day (days 0, 2, and 4): baseline (MAC-A) and 2 further determinations were made after treatments, 1.5 hours apart (MAC-B and MAC-C). RESULTS: A reduction in baseline MAC was produced on day 0 by ibudilast, remifentanil, remifentanil plus ibudilast, remifentanil plus naloxone (P < 0.01), but not saline. Similar effects were found on days 2 and 4. A tolerance to remifentanil was found on days 0, 2, and 4, which neither ibudilast nor naloxone prevented. The MAC increase produced by remifentanil on day 4 (P = 0.001) was prevented by either ibudilast or naloxone. CONCLUSIONS: Ibudilast, besides reducing the MAC, prevented the delayed increase in baseline MAC produced by remifentanil but not the increase in MAC caused by tolerance to remifentanil.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".