Amniotic Fluid Proteome Analysis from Down Syndrome Pregnancies for Biomarker Discovery
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
Down syndrome (DS) is an anomaly caused by an extra chromosome 21, and it affects 1 in 750 live births. Phenotypes include cognitive impairment, congenital defects, and increased risk for several diseases such as Alzheimer's disease and leukemia. Current DS-screening tests subject many women to invasive procedures for accurate diagnosis due to insufficient specificity. Since amniotic fluid (AF) surrounds the developing fetus, understanding the changes in AF composition in the presence of DS may provide insights into genotype-phenotype associations, and aid in discovery of novel biomarkers for better screening. On the basis of our previous study, in which we reported an extensive proteome of AF, we performed two-dimensional liquid chromatography followed by MS/MS to analyze triplicates of pooled AF of chromosomally normal and DS-affected pregnancies (10 samples per pool). A total of 542 proteins were identified from the two sets of triplicate analyses by the LTQ-Orbitrap mass spectrometer and data were compared semiquantitatively by spectral counting. Candidate biomarkers were selected based on the spectral count differences between the two conditions after normalization. Comparison between the two groups revealed 60 candidates that showed greater than 2-fold increase or decrease in concentration in the presence of DS. Among these candidates, amyloid precursor protein and tenascin-C were verified by ELISA, and both showed a 2-fold increase, on average, in DS-AF samples compared to controls. All proteins that showed significant differences between the two conditions were bioinformatically analyzed to preliminarily understand their biological implications in DS.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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