Application of proteomics to prenatal screening and diagnosis for aneuploidies
Bibliographic record
Abstract
Current screening for fetal aneuploidies relies on biochemical and ultrasound measurements, and the sensitivity and specificity needs to be improved to reduce the number of pregnant women subjected to invasive diagnostic procedures, such as amniocentesis. Proteomic technologies enable new strategies for discovering biomarkers from complex biological fluids in a high-throughput and sensitive manner. Since mass spectrometry-based techniques allow for both qualitative and quantitative analysis of a given proteome, they have been widely used to resolve and compare the proteome of maternal plasma, serum, urine, cervical-vaginal fluid, and amniotic fluid. Comparisons of proteomes of normal fluids with those from aneuploidy pregnancies have revealed a host of candidate markers that still need to be verified. In parallel with proteomics, there is interest in other emerging techniques, such as RNA-SNP analysis or quantitation of fetal DNA by shotgun sequencing. Although these genomic techniques hold much promise, discovery of additional markers via quantitative proteomic comparisons could drastically improve current conventional screening at reasonable cost. Proteomics-based biomarker discovery is applicable to detection of not just aneuploidies, but also other pregnancy-related diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.002 | 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".