Cell-Free Placental DNA in Maternal Plasma in Relation to Placental Health and Function
Bibliographic record
Abstract
BACKGROUND: While cell-free placental DNA (cfp-DNA) increases in response to certain pathological conditions, confounding variables, such as placental size, may also contribute to its release. Furthermore, the relationship between cfp-DNA and maternal serum proteins has not been well investigated. OBJECTIVE: To analyze plasma cfp-DNA levels and correlate with measurable placental parameters, maternal serum proteins, or pathologic conditions reflecting placental dysfunction. METHOD: Methylated fraction of RASSF1A was quantified in maternal plasma as a measure of cfp-DNA in a cohort of 86 pregnant women. RESULTS: Placental dimensions or weight had no impact on cfp-DNA levels in noncomplicated pregnancies (n = 63). However, an association between β-hCG and cfp-DNA levels (p = 0.0012) was detected. Complications occurred in 23 pregnancies including chromosomal abnormalities, gestational hypertension, intrauterine growth restriction, and preterm birth. There was overall a skewed distribution (<-1 SD or >1 SD from mean) for cfp-DNA in the abnormal group, although due to the small number of samples for each pathology, we provide only descriptive data to assess possible trends in cfp-DNA variation. CONCLUSION: While cfp-DNA levels outside of the normal range may reflect placental distress, this relationship may be masked by a number of physiological confounders. The independence of cfp-DNA from β-hCG levels commonly assessed in pregnancy need to be further addressed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".