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Record W4281841137 · doi:10.3389/fnbeh.2022.908630

Neuroligin Plays a Role in Ethanol-Induced Disruption of Memory and Corresponding Modulation of Glutamate Receptor Expression

2022· article· en· W4281841137 on OpenAlexafffund
Jacqueline K. Rose, Michael Butterfield, Joseph Liang, Mahraz Parvand, Conny H. S. Lin, Catharine H. Rankin

Bibliographic record

VenueFrontiers in Behavioral Neuroscience · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEicosanoids and Hypertension Pharmacology
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsNeuroliginGlutamate receptorChemistryCell biologyNMDA receptorNeuroscienceModulation (music)ReceptorBiophysicsBiologyBiochemistryPhysicsExcitatory postsynaptic potential

Abstract

fetched live from OpenAlex

Exposure to alcohol causes deficits in long-term memory formation across species. Using a long-term habituation memory assay in Caenorhabditis elegans , the effects of ethanol on long-term memory (> 24 h) for habituation were investigated. An impairment in long-term memory was observed when animals were trained in the presence of ethanol. Cues of internal state or training context during testing did not restore memory. Ethanol exposure during training also interfered with the downregulation of AMPA/KA-type glutamate receptor subunit (GLR-1) punctal expression previously associated with long-term memory for habituation in C. elegans . Interestingly, ethanol exposure alone had the opposite effect, increasing GLR-1::GFP punctal expression. Worms with a mutation in the C. elegans ortholog of vertebrate neuroligins ( nlg-1 ) were resistant to the effects of ethanol on memory, as they displayed both GLR-1::GFP downregulation and long-term memory for habituation after training in the presence of ethanol. These findings provide insights into the molecular mechanisms through which alcohol consumption impacts memory.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.372

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 head0.021
GPT teacher head0.290
Teacher spread0.269 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

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