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Record W3153274311 · doi:10.24908/iqurcp.10056

4. The Role of Glycosylation in Cardiac Arrhythmias

2018· article· en· W3153274311 on OpenAlexvenueno aff
Maggie Hulbert

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordshERGGlycosylationChemistrySerine proteaseLong QT syndromePotassium channelProteaseBiochemistryPharmacologyQT intervalBiologyEnzymeMedicineBiophysicsInternal medicine

Abstract

fetched live from OpenAlex

Long QT syndrome (LQTS) is a debilitating cardiac arrhythmia, and mutations or malfunctions in the human ether-a-go-go–related gene (hERG) are the most common cause of LQTS. hERG is responsible for the cardiac potassium current (IKr), which is important for cardiac repolarization. It has 6 transmembrane domains, ranging from S1 to S6, and a complex N-linked oligosaccharide chain resides on the extracellular S5 pore linker region. The role of this glycosylation chain in hERG function has not yet been well distinguished and in the current study we aim to investigate the relationship between hERG protease susceptibility and the presence of the glycosylation chain. Using molecular techniques, we demonstrate that de-glycosylated hERG channels are degraded by the serine protease Proteinase K at a much faster rate than their glycosylated counter parts. Additionally, through removal of the end-chain sialic acid residues using the enzyme neuraminidase, we conclude that it is likely the physical blockade of susceptible protease sites that grants wild type hERG this glycosylation-dependent protection as opposed to charge repulsion facilitated by specific sugars in the carbohydrate chain. Our data show that the glycosylation chain found on hERG plays a role in protecting the channel from protease degradation, and could provide valuable insight into the development of the cardiac arrhythmia Long QT Syndrome.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.276
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.030
GPT teacher head0.343
Teacher spread0.313 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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