Fast and straightforward simultaneous quantification of multiple apolipoproteins in human serum on a high‐throughput LC‐MS/MS platform
Bibliographic record
Abstract
PURPOSE: Apolipoprotein monitoring is useful for diagnosing cardiovascular diseases, as they are risk factors of arteriosclerosis and other neutral fat-related diseases. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is advantageous for simultaneous apolipoprotein quantification, differentiation, and standardization including their isoforms. However, fast and straightforward sample preparation that retains quantification accuracy remains challenging in clinical MS. EXPERIMENTAL DESIGN: We developed a simultaneous assay for serum apolipoprotein A-I (ApoA-I), apolipoprotein B100 family, and apolipoprotein C-III (ApoC-III) using a high-throughput LC-MS/MS platform coupled with a BRAVO system. The assay was simplified by using sodium deoxycholate and trypsin/lys-C without reduction and alkylation steps. RESULTS: Simple sample preparation reduced turnaround time by 1.5 h and neat goat serum was chosen as an optimal calibration matrix for accurate protein quantification. Assay precision, linearity, correlation, accuracy, limit of detection (LOD), limit of quantitation (LOQ), and carryover were validated according to CLSI guidelines over 41 days using more than 100 human serum samples. Good correlation compared with turbidimetric immunoassay (TIA) was observed by Deming regression for all analytes. CONCLUSIONS AND CLINICAL RELEVANCE: A high-throughput LC-MS/MS and BRAVO assay for simultaneous apolipoprotein analysis was validated using a simple preparation method with a human serum calibrator in goat serum matrix. The assay is readily expandable to include other target serum proteins and/or their isoforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".