A Streamlined Method for Quantification of Apolipoprotein A1 in Human Plasma by LC-MS/MS
Bibliographic record
Abstract
To the Editor: Apolipoprotein A1 (apoA1) is the major protein component of high-density lipoprotein particles in blood, and its concentration in serum and plasma is a marker of atherosclerotic cardiovascular diseases. Clinical laboratories commonly use immunometric methods to measure apoA1; however, an alternative approach, LC-MS/MS, has been proposed (1–3). Uptake of these LC-MS/MS methods for routine testing in a clinical setting has been challenging owing to complex and time-intensive sample preparation work flows. Overnight digestion has been previously applied for quantification of apoA1 by use of proteolytic peptides VQPYLDDFQK, DYVSQFEGSALGK, and THLAPYSDELR (1, 2) and a 3-h digest using peptide VQPYLDDFQK (3). Toward the design of a streamlined work flow for implementation in a clinical laboratory, we simplified sample preparation by using only additive steps and eliminating the use of chemical denaturants, reduction, and alkylation. Herein we demonstrate that such a simple and rapid work flow, targeting a fast-forming proteolytic peptide, can be used to develop a quantitative apoA1 LC-MS/MS assay in agreement with an established immunonephelometric method. Our optimized protocol generated several proteotypic peptides suitable for apoA1 quantification by using a brief 20-min digestion without reduction or alkylation steps or the use of chemical denaturants. On the basis of several factors including the 24-h digestion profile (4), THLAPYSDELR was selected for further development. To generate the external 6-point calibration curve, the unlabeled THLAPYSDELR peptide was synthesized, with purity determined by high-performance LC and amino acid analysis (New England Peptide). The lyophilized peptide stocks were initially solubilized in a 5% acetonitrile and 0.1% formic acid solution and further diluted with …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".