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Record W2913609695 · doi:10.1139/cjpp-2018-0494

Involvement of leukotriene pathway in the development of sevoflurane-induced pica in rats

2019· article· en· W2913609695 on OpenAlexvenueno aff
Kouichi Yamamoto, Atsushi Yamatodani

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2019
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
Fundersnot available
KeywordsZileutonSevofluraneLeukotrieneMontelukastPharmacologyLeukotriene E4Leukotriene receptorProstaglandin E2CyclooxygenaseAntagonistChemistryProstaglandin D2AnesthesiaProstaglandinArachidonate 5-lipoxygenaseMedicineEndocrinologyInternal medicineArachidonic acidReceptorBiochemistryAsthma

Abstract

fetched live from OpenAlex

We previously reported that sevoflurane-induced pica, kaolin ingestion behavior, in rats has the potential to reflect postoperative nausea and vomiting (PONV) in humans. It is well-known that corticosteroids, which inhibit both prostaglandin and leukotriene syntheses due to phospholipase A 2 inhibition, are effective for reducing PONV; however, the precise mechanisms remain unclear. We investigated the involvement of the prostaglandin or leukotriene pathway in the development of sevoflurane-induced pica. We found that sevoflurane-induced pica was effectively inhibited by pretreatment with a leukotriene receptor antagonist (montelukast) or an inhibitor of 5-lipoxygenase (zileuton), rather than an inhibitor of cyclooxygenase (flurbiprofen). Furthermore, we observed that sevoflurane significantly increased urinary leukotriene excretion and 5-lipoxygenase mRNA expression in the spleen, but not hypothalamus. These results suggest that the production of leukotriene may lead to the development of sevoflurane-induced pica in rats, and that inhibition of the leukotriene pathway could be potentially useful for the treatment of PONV.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.071
GPT teacher head0.390
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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