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Record W4282598113 · doi:10.1038/s41467-022-31088-8

Variants in ASPH cause exertional heat illness and are associated with malignant hyperthermia susceptibility

2022· article· en· W4282598113 on OpenAlexafffund
Yukari Endo, Linda Groom, Alper Celik, Natalia Kraeva, Chang Seok Lee, Sung Yun Jung, Lois Gardner, Marie‐Anne Shaw, Susan L. Hamilton, Philip M. Hopkins, Robert T. Dirksen, Sheila Riazi, James J. Dowling

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsSickKids FoundationUniversity of TorontoToronto General HospitalHospital for Sick Children
FundersCanadian Institutes of Health ResearchNational Institutes of HealthGovernment of CanadaNational Institute of Arthritis and Musculoskeletal and Skin DiseasesU.S. Department of Health and Human Services
KeywordsMalignant hyperthermiaGeneticsBiologyMedicineCancer researchPathology

Abstract

fetched live from OpenAlex

Exertional heat illness (EHI) and malignant hyperthermia (MH) are life threatening conditions associated with muscle breakdown in the setting of triggering factors including volatile anesthetics, exercise, and high environmental temperature. To identify new genetic variants that predispose to EHI and/or MH, we performed genomic sequencing on a cohort with EHI/MH and/or abnormal caffeine-halothane contracture test. In five individuals, we identified rare, pathogenic heterozygous variants in ASPH, a gene encoding junctin, a regulator of excitation-contraction coupling. We validated the pathogenicity of these variants using orthogonal pre-clinical models, CRISPR-edited C2C12 myotubes and transgenic zebrafish. In total, we demonstrate that ASPH variants represent a new cause of EHI and MH susceptibility.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.260
Teacher spread0.242 · 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 designObservational
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

Citations17
Published2022
Admission routes2
Has abstractyes

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Same venueNature CommunicationsSame topicIon channel regulation and functionFrench-language works237,207