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Record W2002533819 · doi:10.1002/jnr.1115

GABA<sub>A</sub> receptor gene expression in rat cortex: Differential effects of two chronic diazepam treatment regimes

2001· article· en· W2002533819 on OpenAlexaff
Michelle I. Arnot, Martin Davies, Ian L. Martin, Alan N. Bateson

Bibliographic record

VenueJournal of Neuroscience Research · 2001
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDiazepamFlunitrazepamGABAA receptorBenzodiazepineReceptorPharmacologyEndocrinologyInternal medicineAnxiolyticGene expressionIn vivoDosingChemistryMedicineBiologyGeneBiochemistry

Abstract

fetched live from OpenAlex

Diazepam is widely prescribed as an anxiolytic but its therapeutic application is limited because with daily use tolerance develops to certain aspects of its pharmacological profile. We compared the effects of two dosing paradigms on GABA(A) receptor gene expression and benzodiazepine binding characteristics. Equivalent daily doses of 15 mg/kg/day diazepam were delivered either via constant infusion or daily subcutaneous injection for 14 days. The two distinct treatment regimes produced significantly different changes in GABA(A) receptor alpha4-, beta2-, beta3- and gamma1-subunit mRNA steady-state levels. Similar changes in the GABA enhancement of flunitrazepam binding and the BZ3/BZ2 subtype ratio determined ex vivo were produced, however, significant differences were found in [(3)H]-Ro 15-4513 binding between cortical tissue from diazepam injected animals compared with diazepam infused animals. Our data suggest that it is the diurnal fluctuations in receptor occupancy that are responsible for the different effects produced by these two dosing regimes.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.083
GPT teacher head0.409
Teacher spread0.326 · 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

Citations34
Published2001
Admission routes1
Has abstractyes

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