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Record W2103592692 · doi:10.1556/aphysiol.90.2003.3.8

Effect of diazepam treatment on metabolic indices in trained and untrained rats

2003· article· en· W2103592692 on OpenAlexaff
Mélanie Tremblay, LM Bussieres, F. P. Pettigrew, W C Fielding, EG Noble, Zsolt Radák, Anne Taylor

Bibliographic record

VenueActa Physiologica Hungarica · 2003
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsWestern UniversityUniversité de Montréal
Fundersnot available
KeywordsDiazepamGlycogenEndocrinologyInternal medicineCarbohydrate metabolismMetabolismSkeletal muscleLipid metabolismChemistryCarbohydrateCalciumMedicinePharmacology

Abstract

fetched live from OpenAlex

Exercise training, like diazepam, is commonly employed as a means of reducing anxiety. Both diazepam and exercise training have been shown to modify carbohydrate and lipid metabolism as well as influence calcium metabolism in skeletal muscle. As receptor binding and thereby efficacy of diazepam has been demonstrated to be modulated by the lipid environment of the receptor, and changes in calcium levels can affect a number of intracellular signalling pathways, we sought to determine if the interaction of both chronic diazepam and exercise training would modify selected metabolic indices in an animal model. For this purpose, muscle and liver glycogen, blood glucose and plasma free fatty acids (FFA) were measured in sedentary, exercise trained and exercise trained, acutely exhausted animals. Alterations in lipid and carbohydrate metabolism were observed in all experimental groups. Diazepam treatment alone exerts metabolic consequences, such as elevated muscle glycogen and plasma FFA and depressed blood glucose levels, which are similar to those observed with exercise training. When animals are acutely exercised to exhaustion, however, differences appear, including a reduced rise in plasma FFA, which suggests that long-term diazepam treatment does influence exercise metabolism, possibly as a result of effects on the sympatho-adrenal system.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.288
Teacher spread0.272 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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