Precision of Fetal DNA Fraction Estimation by Quantitative Polymerase Chain Reaction Quantification of a Differently Methylated Target in Noninvasive Prenatal Testing
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
BACKGROUND: The performance of noninvasive prenatal testing (NIPT) assays is critically determined by the proportion of fetal DNA or fetal fraction (FF). Fetomaternal differential methylation of certain genomic regions has been proposed as a universal marker of fetal origin, and previous reports have suggested the use of methylation-sensitive restriction enzyme (MSRE) assays to estimate FF. METHODS: We analyzed the performance of FF estimation using an MSRE assay with duplex quantitative polymerase chain reaction (qPCR). Mixtures of genomic DNA from placental cells and from adult women were digested with 2 MSRE and FF estimates obtained, for a total of 221 pairwise treatment/control comparisons. RESULTS: The coefficient of variance (CV) of the MSRE assays was high, ranging from 24% to 60%. An alternative in silico FF estimation algorithm, SeqFF, displayed slightly lower variability, with a CV of 22%. CONCLUSION: These results cast doubts on the usefulness of the MSRE-based assay of differentially methylated markers for FF estimation. The lack of a universal method capable of precisely estimating FF remains an incompletely solved issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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