MétaCan
Menu
Back to cohort
Record W2942214830 · doi:10.1080/01616412.2019.1609204

Benzyl alcohol suppresses seizures in two different animal models

2019· article· en· W2942214830 on OpenAlexafffund
Yinhao Wu, Junhan Liu, Ziying Chen, W. McIntyre Burnham

Bibliographic record

VenueNeurological Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsOntario Brain InstituteUniversity of Toronto
FundersOntario Brain Institute
KeywordsED50Benzyl alcoholChemistryIntraperitoneal injectionPharmacologyKindlingAlcoholBarbituratePentylenetetrazolAnesthesiaAnticonvulsantStimulationEpilepsyMedicinePsychologyInternal medicineOrganic chemistryNeuroscienceBiochemistry

Abstract

fetched live from OpenAlex

Introduction: We have been exploring the effects of dihydroprogesterone in female amygdala-kindled rats. For intraperitoneal (i.p.) time–response studies, we used a vehicle containing the common solvent, benzyl alcohol (BnOH). The vehicle containing BnOH was also tested alone as a control.Method and Results: Unexpectedly, it was found that the vehicle containing BnOH had clear-cut anti-seizure effects in the kindling model, with an ED50 of 100 mg/kg. In a follow-up study, dose- and time–response studies of i.p. BnOH were done in male mice in the maximal pentylenetetrazol (PTZ) model. BnOH suppressed PTZ seizures in a dose-dependent manner, with an ED50 of 300 mg/kg against hindlimb tonic extension. Effects were fully established at 5-min post injection and lasted for an hour.Conclusion: BnOH is not an inert solvent. It has clear-cut anti-seizure effects against both focal and generalized seizures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.386
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueNeurological ResearchSame topicEstrogen and related hormone effectsFrench-language works237,207