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Record W2031798904 · doi:10.1038/srep01388

Anesthesia-induced hypothermia mediates decreased ARC gene and protein expression through ERK/MAPK inactivation

2013· article· en· W2031798904 on OpenAlexafffund
Robert A. Whittington, Alexis Bretteville, László Virág, Charles W. Emala, Thomas Maurin, François Marcouiller, Carl Julien, Franck R. Petry, Noura B. El-Khoury, Françoise Morin, Jean Charron, Emmanuel Planel

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsUniversité LavalHôtel-Dieu de QuébecCentre hospitalier de l'Université Laval
FundersCanadian Institutes of Health ResearchNational Institute of General Medical SciencesAlzheimer Society
KeywordsHypothermiaArc (geometry)MAPK/ERK pathwayIsofluranePropofolAnesthesiaAnestheticIn vivoGene expressionMedicineGenePharmacologyBiologyCell biologySignal transductionGenetics

Abstract

fetched live from OpenAlex

Several anesthetics have been reported to suppress the transcription of a number of genes, including Arc, also known as Arg3.1, an immediate early gene that plays a significant role in memory consolidation. The purpose of this study was to explore the mechanism of anesthesia-mediated depression in Arc gene and protein expression. Here, we demonstrate that isoflurane or propofol anesthesia decreases hippocampal Arc protein expression in rats and mice. Surprisingly, this change was secondary to anesthesia-induced hypothermia. Furthermore, we confirm in vivo and in vitro that hypothermia per se is directly responsible for decreased Arc protein levels. This effect was the result of the decline of Arc mRNA basal levels following inhibition of ERK/MAPK by hypothermia. Overall, our results suggest that anesthesia-induced hypothermia leads to ERK inhibition, which in turns decreases Arc levels. These data give new mechanistic insights on the regulation of immediate early genes by anesthesia and hypothermia.

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)0.001
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 score0.013
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.046
GPT teacher head0.275
Teacher spread0.229 · 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

Citations33
Published2013
Admission routes2
Has abstractyes

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