Development of a Multiplex Selected Reaction Monitoring Assay for Quantification of Biochemical Markers of Down Syndrome in Amniotic Fluid Samples
Bibliographic record
Abstract
Down syndrome (DS) is one of the most common chromosomal abnormalities affecting about 1 of every 700 fetuses. Current screening strategies have detection rates of 90-95% at a 5% false positive rate. The aim of this study was to discover new biomarkers of DS in amniotic fluid by using a multiplex selected reaction monitoring assay. Nine proteins were analyzed: CEL, CPA1, MUC13, CLCA1, MUC5AC, PLUNC, and HAPLN1, and CGB as positive control and serotransferrin as negative control. One proteotypic peptide for each protein was selected, and internal heavy isotope-labeled peptide standards were spiked into the samples. Fifty-four samples from pregnant women carrying normal (n = 37) or DS-affected (n = 17) fetuses were analyzed. The median protein concentrations for DS and normal samples, respectively, were as follows: 20 and 49 ng/mL (p < 0.01) for CEL; 3.7 and 14 ng/mL (p < 0.001) for CPA1; 80 and 263 ng/mL (p < 0.001) for MUC13; 46 and 135 ng/mL (p < 0.001) for CLCA1; 0.65 and 0.93 μg/mL (p < 0.05) for MUC5AC; 61 and 73 ng/mL (p > 0.05) for PLUNC; 144 and 86 ng/mL (p < 0.01) for HAPLN1; 0.89 and 0.54 μg/mL (p = 0.05) for CGB; 91 and 87 μg/mL (p > 0.05) for serotransferrin. Statistically significant differences were found in six out of the seven candidate proteins analyzed, reflecting a different regulation in DS.
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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 | 0.001 |
| 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".