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Record W2423888748 · doi:10.1177/0269881115613518

The separate and combined effects of monoamine oxidase A inhibition and nicotine on resting state EEG

2015· article· en· W2423888748 on OpenAlexaff
Dylan Smith, Derek J. Fisher, Pierre Blier, Vadim Ilivitsky, Verner Knott

Bibliographic record

VenueJournal of Psychopharmacology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNicotinic Acetylcholine Receptors Study
Canadian institutionsMount Saint Vincent UniversityRoyal Ottawa Mental Health CentreUniversity of Ottawa
Fundersnot available
KeywordsMoclobemideNicotineMonoamine oxidasePharmacologyElectroencephalographyNicotinic agonistPsychologyMonoamine neurotransmitterMedicineNeuroscienceChemistryInternal medicineSerotoninBiochemistryHippocampusReceptor

Abstract

fetched live from OpenAlex

While nicotine is often associated with the neuropsychological effects of tobacco smoke, the robust monoamine oxidase (MAO) inhibition observed in chronic smokers is also likely to play a role. Electroencephalographically-indexed alterations in baseline neural oscillations by nicotine have previously been reported in both smokers and non-smokers, however, little is known about the effects of MAO inhibition in combination with nicotine on resting state EEG. In a sample of 24 healthy non-smoking males, the effects of 6 mg nicotine gum, as well as MAO-A inhibition via 75 mg moclobemide, were investigated in separate and combined conditions over four separate test sessions. Drug effects were observed in the alpha2, beta2, and theta band frequencies. Nicotine increased alpha2 power, and moclobemide decreased beta2 power. Theta power was decreased most robustly by the combination of both drugs. Therefore, this study demonstrated that the nicotinic and MAO inhibiting properties of tobacco may differentially influence fast-wave oscillations (alpha2 and beta2), while acting in synergy to influence theta oscillations.

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 score0.026
Threshold uncertainty score0.346

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 integrity0.0000.000
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 head0.009
GPT teacher head0.304
Teacher spread0.295 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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