Multiplexed panel of precisely quantified salivary proteins for biomarker assessment
Bibliographic record
Abstract
An increasingly popular "absolute" quantitative technique involves the SRM or MRM approach with stable isotope-labeled standards (SIS). Using this approach, many proteins in human plasma/serum have been quantified for biomarker assessment and disease stratification. Due to the complexity of plasma and the invasive nature of its collection, alternative biosamples are currently being explored. Here, we present the broadest panel of multiplexed MRM assays with SIS peptides for saliva proteins developed to date. The validated panel consists of 158 candidate human saliva protein biomarkers, inferred from 244 interference-free peptides. The resulting concentrations were reproducibly quantified over a 6 order-of-magnitude concentration range (from 218 μg/mL to 88 pg/mL; average CVs of 12% over analytical triplicates). All concentrations were determined from reverse standard curves, which were generated using a constant concentration of endogenous material with varying concentrations of spiked-in SIS peptides. The large-scale screening of the soluble and membrane-associated proteins contained within the 158-plex assay could present new opportunities for biomarker assessment and clinical diagnostics.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".