<scp>MRM</scp>‐based multiplexed quantitation of 67 putative cardiovascular disease biomarkers in human plasma
Bibliographic record
Abstract
A highly-multiplexed MRM-based assay for determination of cardiovascular disease (CVD) status and disease classification has been developed for clinical research. A high-flow system using ultra-high performance LC and an Agilent 6490 triple quadrupole mass spectrometer, equipped with an ion funnel, provided ease of use and increased the robustness of the assay. The assay uses 135 stable isotope-labeled peptide standards for the quantitation of 67 putative biomarkers of CVD in tryptic digests of whole plasma in a 30-min assay. Eighty-five analyses of the same sample showed no loss of sensitivity (<20% CV for 134/135 peptides) and no loss of retention time accuracy (<0.5% CV for all peptides). The maximum linear dynamic range of the MRM assays ranged from 10(3) -10(5) for 106 of the assays. Excellent linear responses (r >0.98) were obtained for 117 of the 135 peptide targets with attomole level limits of quantitation (<20% CV and accuracy 80-120%) for 81 of the 135 peptides. The assay presented in this study is easy to use, robust, sensitive, and has high-throughput capabilities through short analysis time and complete automated sample preparation. It is therefore well suited for CVD biomarker validation and discovery in plasma.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".