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Record W1982916236 · doi:10.1038/srep01465

High dose folic acid supplementation of rats alters synaptic transmission and seizure susceptibility in offspring

2013· article· en· W1982916236 on OpenAlexafffund
Fernando Girotto, Lucas Scott, Yosef Avchalumov, Jacqueline K. Harris, Stephanie Iannattone, Rose Tobias, Luis Bello‐Espinosa, Jong M. Rho, Jörn Davidsen, G. Campbell Teskey, Michael A. Colicos

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsOffspringBurstingMetaboliteFolic acidGestationFolic acid supplementationNeural tubeBiological neural networkBiologyPharmacologyInternal medicineEndocrinologyPregnancyNeuroscienceMedicineCell biologyGenetics

Abstract

fetched live from OpenAlex

Maternal folic acid supplementation is essential to reduce the risk of neural tube defects. We hypothesize that high levels of folic acid throughout gestation may produce neural networks more susceptible to seizure in offspring. We hence administered large doses of folic acid to rats before and during gestation and found their offspring had a 42% decrease in their seizure threshold. In vitro, acute application of folic acid or its metabolite 4Hfolate to neurons induced hyper-excitability and bursting. Cultured neuronal networks which develop in the presence of a low concentration (50 nM) of 4Hfolate had reduced capacity to stabilize their network dynamics after a burst of high-frequency activity, and an increase in the frequency of mEPSCs. Networks reared in the presence of the folic acid metabolite 5M4Hfolate developed a spontaneous, distinctive bursting pattern, and both metabolites produced an increase in synaptic density.

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)0.000
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 score0.351
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.261
Teacher spread0.248 · 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

Citations49
Published2013
Admission routes2
Has abstractyes

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