MétaCan
Menu
Back to cohort
Record W2403571521 · doi:10.1111/epi.13404

Involvement of micro<scp>RNA</scp>s in epileptogenesis

2016· review· en· W2403571521 on OpenAlexfundno aff
Adriano Cattani, Camille Allène, Volker Seifert, Felix Rosenow, David C. Henshall, Thomas M. Freiman

Bibliographic record

VenueEpilepsia · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsnot available
FundersScience Foundation IrelandEuropean CommissionConcordia University of Edmonton
KeywordsEpileptogenesisEpilepsyNeuroscienceNeuroinflammationmicroRNATemporal lobeDiseaseMedicineBiologyBioinformaticsPathologyGeneGenetics

Abstract

fetched live from OpenAlex

Patients who have sustained brain injury or had developmental brain lesions present a non-negligible risk for developing delayed epilepsy. Finding therapeutic strategies to prevent development of epilepsy in at-risk patients represents a crucial medical challenge. Noncoding microRNA molecules (miRNAs) are promising candidates in this area. Indeed, deregulation of diverse brain-specific miRNAs has been observed in animal models of epilepsy as well as in patients with epilepsy, mostly in temporal lobe epilepsy (TLE). Herein we review deregulated miRNAs reported in epilepsy with potential roles in key molecular and cellular processes underlying epileptogenesis, namely neuroinflammation, cell proliferation and differentiation, migration, apoptosis, and synaptic remodeling. We provide an up-to-date listing of miRNAs altered in epileptogenesis and assess recent functional studies that have interrogated their role in epilepsy. Last, we discuss potential applications of these findings for the future development of disease-modifying therapeutic strategies for antiepileptogenesis.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.024
GPT teacher head0.292
Teacher spread0.268 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations52
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEpilepsiaSame topicMicroRNA in disease regulationFrench-language works237,207