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Record W3126948517 · doi:10.1186/s13041-021-00745-2

The de novo CACNA1A pathogenic variant Y1384C associated with hemiplegic migraine, early onset cerebellar atrophy and developmental delay leads to a loss of Cav2.1 channel function

2021· article· en· W3126948517 on OpenAlexafffund
María A. Gandini, Ivana A. Souza, Laurent Ferron, A. Micheil Innes, Gerald W. Zamponi

Bibliographic record

VenueMolecular Brain · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersCanadian Institutes of Health ResearchCanada Excellence Research Chairs, Government of Canada
KeywordsFamilial hemiplegic migraineMissense mutationLoss functionAtrophyNeuroscienceMutationBiologyGeneticsMedicineInternal medicineMigraine with auraGeneMigrainePhenotype

Abstract

fetched live from OpenAlex

Abstract CACNA1A pathogenic variants have been linked to several neurological disorders including familial hemiplegic migraine and cerebellar conditions. More recently, de novo variants have been associated with severe early onset developmental encephalopathies. CACNA1A is highly expressed in the central nervous system and encodes the pore-forming Ca V α 1 subunit of P/Q-type (Cav2.1) calcium channels. We have previously identified a patient with a de novo missense mutation in CACNA1A (p.Y1384C), characterized by hemiplegic migraine, cerebellar atrophy and developmental delay. The mutation is located at the transmembrane S5 segment of the third domain. Functional analysis in two predominant splice variants of the neuronal Cav2.1 channel showed a significant loss of function in current density and changes in gating properties. Moreover, Y1384 variants exhibit differential splice variant-specific effects on recovery from inactivation. Finally, structural analysis revealed structural damage caused by the tyrosine substitution and changes in electrostatic potentials.

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 score0.082
Threshold uncertainty score0.627

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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
Published2021
Admission routes2
Has abstractyes

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