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Record W4403071716 · doi:10.1093/clinchem/hvae106.513

B-153 Improving Accuracy of IgG subclasses Quantitation in Serum Using Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) on SCIEX 7500

2024· article· en· W4403071716 on OpenAlexaff
Rongxing Yi, Daniel T. Holmes, André Mattman

Bibliographic record

VenueClinical Chemistry · 2024
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversity of British ColumbiaSt. Paul's Hospital
Fundersnot available
KeywordsChromatographyChemistryLiquid chromatography–mass spectrometryTandem mass spectrometryMass spectrometry

Abstract

fetched live from OpenAlex

Abstract Background Serum IgG subclasses (IgGSC) are most often measured for identification of selective immunodeficiency disease and IgG4-related disease (IgG4RD). Traditional nephelometric (IN) assays suffer from errors related to antigen excess and cross-reactivity that can impact the accuracy of results. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been the routine testing methodology for serum IgG subclasses at St. Paul’s Hospital since 2015 due to the simplicity and freedom from inter-subclass cross-reactivity problems. In brief, 20 μL of serum sample is denatured, reduced, alkylated and digested, processed semi-automatically on Hamilton Starlet liquid handler. This is followed by analysis on a SCIEX 5500 QTRAP LC-MS/MS. However, when the method was migrated to the SCIEX 7500, it suffered from calibration curve splitting and poor between-device comparability. It was hypothesized that this could be caused by detuning of some high concentration analytes, suboptimal internal standard (IS) concentrations and, suboptimal IS multiple reaction monitoring (MRM) transition choices, and matrix effects from artificial calibrators. Methods Several modifications were made to improve accuracy and robustness: 1) new in-house calibrators were prepared using patient sample pool instead of using Binding Site IgG Subclass Kit calibrators. 2) More IS MRM transitions were added to the method with each analyte MRM quantitated using its own IS MRM using the same fragment ion. The IS concentration was also increased 5-fold for IgG1 and Total IgG. 3) To avoid problems arising from selectively ion detuning, the injection volume was reduced from 10 μL to 1 μL which permitted collision energies (CEs) to be normalized for each analyte/IS MRM pair. Results The new SCIEX 7500 method compares well with the original method (N=168) with Passing Bablok regression slopes of 0.94, 1.1, 1.0, 0.98, and 0.98 for IgG1-4 and IgG total respectively with corresponding intercepts of 0.3, -0.1, 0.0, 0.0, and 0.41 g/L and R^2 of 0.96, 0.95, 0.99, 0.98, and 0.95. Median differences were -1.76%, 1.07%, 1.06%, 6.13% and 1.91%. In a typical batch of 90 patient samples and four sets of calibrators, the calibration curves overlap well. Use of patient-based calibrators allowed calibration values to be centered in the range of typical patient sample values, particularly for IgG4, where the calibration curve was extended to 3.46 g/L compared with the previous high calibrator value of 0.48 g/L. Conclusions The new IgG subclass quantitation method on SCEIX 7500 LC-MS/MS system is robust with excellent reproducibility and between-device comparability.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.184
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.441
Teacher spread0.347 · 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.

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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