ExSTA: External Standard Addition Method for Accurate High‐Throughput Quantitation in Targeted Proteomics Experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Targeted proteomics using MRM with stable-isotope-labeled internal-standard (SIS) peptides is the current method of choice for protein quantitation in complex biological matrices. Better quantitation can be achieved with the internal standard-addition method, where successive increments of synthesized natural form (NAT) of the endogenous analyte are added to each sample, a response curve is generated, and the endogenous concentration is determined at the x-intercept. Internal NAT-addition, however, requires multiple analyses of each sample, resulting in increased sample consumption and analysis time. EXPERIMENTAL DESIGN: To compare the following three methods, an MRM assay for 34 high-to-moderate abundance human plasma proteins is used: classical internal SIS-addition, internal NAT-addition, and external NAT-addition-generated in buffer using NAT and SIS peptides. Using endogenous-free chicken plasma, the accuracy is also evaluated. RESULTS: The internal NAT-addition outperforms the other two in precision and accuracy. However, the curves derived by internal vs. external NAT-addition differ by only ≈3.8% in slope, providing comparable accuracies and precision with good CV values. CONCLUSIONS AND CLINICAL RELEVANCE: While the internal NAT-addition method may be "ideal", this new external NAT-addition can be used to determine the concentration of high-to-moderate abundance endogenous plasma proteins, providing a robust and cost-effective alternative for clinical analyses or other high-throughput applications.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it