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Record W2012161081 · doi:10.2754/avb200978030483

Influence of Midazolam and L-Arginine on Clinical Observations and Biochemical Changes in Rat Liver Induced by Pentylenetetrazole

2009· article· en· W2012161081 on OpenAlexaff
Ankica Jelenković, Dušan Jovanović, Dragan Đurđević, Danica Stanimirovic, Dubravko Bokonjić, Ivana Vasiljević, R. Mihajlović

Bibliographic record

VenueActa Veterinaria Brno · 2009
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsInstitute for Biological Sciences
Fundersnot available
KeywordsMidazolamPharmacologyConvulsantArginineCytochrome c oxidaseAnticonvulsantChemistryConvulsionMedicineAnesthesiaBiochemistryEnzymeEpilepsyReceptorSedationAmino acid

Abstract

fetched live from OpenAlex

Certain types of convulsions may lead to multiorgan dysfunction. We investigated whether the chemoconvulsant pentylenetetrazole (PTZ) could influence energy synthesis in the liver besides evoking convulsions in adult male Wistar rats. In 80% of the rats PTZ (100 mg/kg body weight, administered intraperitoneally – i.p.) evoked generalised clonic convulsions (GCCs) and in 60% of the rats generalised clonic-tonic convulsions (GCTCs) within 4 min after its administration. Cytochrome c oxidase activity was simultaneously reduced approximately three-fold compared to 0.9% NaCl-treated (control) rats ( p < 0.01). Midazolam administered before PTZ was an excellent anti-convulsant especially against GCCs ( p < 0.05). However, it did not protect against the decrease in cytochrome c oxidase activity induced by PTZ. In contrast to midazolam, pretreatment with L-arginine did not prevent PTZ-evoked convulsions. However, it offered some protection against the PTZ-mediated reduction in cytochrome c oxidase activity. Our results open new avenues of research that will focus on the mechanisms of action of PTZ, midazolam and L-arginine with particular reference to their direct and/or indirect effects on liver function.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.080
GPT teacher head0.363
Teacher spread0.283 · 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 designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

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