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Record W2026794527 · doi:10.3390/brainsci3031076

Effects of Ethanol Exposure during Distinct Periods of Brain Development on Hippocampal Synaptic Plasticity

2013· article· en· W2026794527 on OpenAlexaff
Anna Patten, Joana Gil‐Mohapel, Ryan C. Wortman, Athena Noonan, Patrícia S. Brocardo, Brian R. Christie

Bibliographic record

VenueBrain Sciences · 2013
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
Fundersnot available
KeywordsLong-term potentiationHippocampal formationDentate gyrusSynaptic plasticityOffspringEthanolHippocampusGestationEndocrinologyInternal medicineNeurosciencePregnancyPsychologyMedicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

Fetal alcohol spectrum disorders occur when a mother drinks during pregnancy and can greatly influence synaptic plasticity and cognition in the offspring. In this study we determined whether there are periods during brain development that are more susceptible to the effects of ethanol exposure on hippocampal synaptic plasticity. In particular, we evaluated how the ability to elicit long-term potentiation (LTP) in the hippocampal dentate gyrus (DG) was affected in young adult rats that were exposed to ethanol during either the 1st, 2nd, or 3rd trimester equivalent. As expected, the effects of ethanol on young adult DG LTP were less severe when exposure was limited to a particular trimester equivalent when compared to exposure throughout gestation. In males, ethanol exposure during the 1st, 2nd or 3rd trimester equivalent did not significantly reduce LTP in the DG. In females, ethanol exposure during either the 1st or 2nd trimester equivalents did not impact LTP in early adulthood, but following exposure during the 3rd trimester equivalent alone, LTP was significantly increased in the female DG. These results further exemplify the disparate effects between the ability to elicit LTP in the male and female brain following perinatal ethanol exposure (PNEE).

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)0.004
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 score0.491
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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