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Record W1486914501 · doi:10.1109/ner.2015.7146766

Gene reactivation diminishes delta-modulated high frequency oscillations during seizure-like events in Mecp2-deficient mice

2015· article· en· W1486914501 on OpenAlexafffund
Sinisa Colic, Min Lang, Robert G. Wither, Liang Zhang, James H. Eubanks, Berj L. Bardakjian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMECP2EpileptogenesisRett syndromeEpilepsyNeuroscienceBiologyChemistryGeneGenetics

Abstract

fetched live from OpenAlex

Genetically modified Mecp2-deficient mice provide a unique model of epilepsy associated with Rett syndrome. Examination of intracranial electroencephalogram (iEEG) recordings from mice lacking mecp2 function have revealed the presence of spontaneous epileptiform activity. To date the majority of these studies have focused on the low frequencies oscillations (LFOs), however, recent findings suggest there may be a link between high frequency oscillations (HFOs) and epileptogenesis. In this study the coupling of LFO phase to HFO amplitude was examined and identified modulation of HFO amplitude (400-600 Hz) by the phase of the high delta band (3-6 Hz) in male Mecp2-deficient mice diminishes after mecp2 gene reactivation therapy. These delta-HFO modulations were found to be strongly associated with long duration epileptiform discharges found primarily in the male non-rescue Mecp2-deficient mice. Differences in HFO interactions with high delta LFOs in male non-rescue and rescue mice could potentially be used as a biomarker for identifying the presence of seizure activity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes2
Has abstractyes

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