MétaCan
Menu
Back to cohort
Record W4298145990 · doi:10.3390/ph15101208

Epilepsy-Induced High Affinity Blockade of the Cardiac Sodium Current INa by Lamotrigine; A Potential for Acquired Arrythmias

2022· article· en· W4298145990 on OpenAlexafffund
Juan Antonio Contreras Vite, Carlos Vega Valle, Happi Biekeu Mbem, Sarah-Maude Boivin, Robert Dumaine

Bibliographic record

VenuePharmaceuticals · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsUniversité de Sherbrooke
FundersHeart and Stroke Foundation of Canada
KeywordsLamotrigineBrugada syndromeMedicineSodium channelEpilepsyEffective refractory periodBlockadePharmacologyAnticonvulsantAnesthesiaRefractory periodInternal medicineCardiologySodiumChemistryPsychiatryReceptor

Abstract

fetched live from OpenAlex

Lamotrigine is widely prescribed to treat bipolar neurological disorder and epilepsy. It exerts its antiepileptic action by blocking voltage-gated sodium channels in neurons. Recently, the US Food and Drug Administration issued a warning on the use of Lamotrigine after observations of conduction anomalies and Brugada syndrome patterns on the electrocardiograms of epileptic patients treated with the drug. Brugada syndrome and conduction disturbance are both associated with alterations of the cardiac sodium current (INa) kinetics and amplitude. In this study, we used the patch clamp technique on cardiomyocytes from epileptic rats to test the hypothesis that Lamotrigine also blocks INa in the heart. We found that Lamotrigine inhibited 60% of INa peak amplitude and reduced cardiac excitability in epileptic rats but had little effect in sham animals. Moreover, Lamotrigine inhibited 67% of INaL and, more importantly, prolonged the action potential refractory period in epileptic animals. Our results suggest that enhanced affinity of Lamotrigine for INa may in part explain the clinical phenotypes observed in epileptic patients.

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 score0.238
Threshold uncertainty score0.452

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 head0.028
GPT teacher head0.309
Teacher spread0.281 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

Explore more

Same venuePharmaceuticalsSame topicIon channel regulation and functionFrench-language works237,207