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Record W4400975525 · doi:10.1021/acsomega.4c04541

Development of an Absolute Quantification Method for hERG Using PRM with Single Isotopologue in-Sample Calibration

2024· article· en· W4400975525 on OpenAlexafffund
Chang Ge, Fabusuyi Akindele Aroge, Ravichandra Venkateshappa, Tom W. Claydon, Bingyun Sun

Bibliographic record

VenueACS Omega · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsSimon Fraser University
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada Foundation for InnovationCompute Canada
KeywordshERGIsotopologueCalibrationChannel (broadcasting)ChemistryComputational biologyChromatographyComputer scienceBiologyPotassium channelMathematicsStatisticsBiophysicsComputer networkMolecule

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The human ether-à-go-go-related gene (KCNH2)-encoded protein hERG constitutes the α subunit of the Kv11.1 channel and contributes to the I kr current, which plays an important role in the cardiac action potential. Genetically and xenobiotically triggered malfunctions of hERG can cause arrhythmia. The expression of hERG in various study systems was assessed mainly as the fold change relative to the corresponding control. Here, we developed a simple and sensitive quantitation method using targeted mass spectrometry, i.e., the parallel reaction monitoring approach, to measure the absolute quantity of hERG in copy number. Such measurements do not require controls, and the obtained values can be compared with similar results for any other protein. To effectively avoid matrix effects, we used the heavy-match-light (HML) in-sample calibration approach that requires only a single isotopologue to achieve copy-number quantitation. No significant difference was observed in the results obtained by HML and by the classic standard addition in-sample calibration approach. Using four proteotypic peptides, we quantified the average number of copies of hERG in the HEK293T heterologous expression system as 3.6 ± 0.5 × 10 6 copies/cell, i.e., 1 million copies/cell for the fully assembled Kv11.1 channel.

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 score0.062
Threshold uncertainty score0.246

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 head0.048
GPT teacher head0.342
Teacher spread0.294 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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