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Mechanisms of Morphine Enhancement of Spontaneous Seizure Activity

2007· article· en· W1974537164 on OpenAlexaff
Ehsan Saboory, Miron Derchansky, Mohammed Ismaili, S. S. Jahromi, Richard Brull, Peter L. Carlen, Hossam El Beheiry

Bibliographic record

VenueAnesthesia & Analgesia · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsToronto Western HospitalToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsMorphineMedicineOpioid(+)-NaloxonePharmacologyOpiateStimulationReceptorOpioid receptorAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: High-dose opioid therapy can precipitate seizures; however, the mechanism of such a dangerous adverse effect remains poorly understood. The aim of our study was to determine whether the neuroexcitatory activity of high-dose morphine is mediated by selective stimulation of opioid receptors. METHODS: Mice hippocampi were resected intact and bathed in low magnesium artificial cerebrospinal fluid to induce spontaneous seizure-like events recorded from CA1 neurons. RESULTS: Application of morphine had a biphasic effect on the recorded spontaneous seizure-like events. In a low concentration (10 microM), morphine depressed electrographic seizure activity. Higher morphine concentrations (30 and 100 microM) enhanced seizure activity in an apparent dose-dependent manner. Naloxone, a nonselective opiate antagonist blocked the proconvulsant action of morphine. Selective mu and kappa opiate receptor agonists and antagonists enhanced and suppressed the spontaneous seizure activity, respectively. On the contrary, delta opioid receptor ligands did not have an effect. CONCLUSIONS: The proseizure effect of morphine is mediated through selective stimulation of mu and kappa opiate receptors but not the activation of the delta receptor system. The observed dose-dependent mechanism of morphine neuroexcitation underscores careful adjustment and individualized opioid dosing in the clinical setting.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.315
Teacher spread0.282 · 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

Citations82
Published2007
Admission routes1
Has abstractyes

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