Expanding the linear dynamic range in quantitative high performance liquid chromatography/tandem mass spectrometry by the use of multiple product ions
Bibliographic record
Abstract
A strategy for expanding the linear working range in bioanalysis using quantitative high performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS) is presented. The strategy involves monitoring multiple product ions. Herein we demonstrate the strategy on a rat plasma assay for a proprietary experimental drug where the linear range is expanded from 2 to 4 orders of magnitude. A primary sensitive ion was monitored to obtain a high sensitivity range calibration curve (0.400 to 100 ng/mL) while a less sensitive secondary ion was monitored to obtain a low sensitivity range calibration curve (90.0 to 4000 ng/mL). Each calibration curve gave acceptable linearity (r >0.990). Quality control samples at low, mid and high levels within each calibration curve demonstrated acceptable precision and accuracy (within 20% for all levels). The technique was successfully applied to rat pre-clinical sample analysis.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".