Altered proteome profiles in maternal plasma in pregnancies with fetal growth restriction
Bibliographic record
Abstract
Abstract Fetal growth restriction (FGR) affects 3–5% of pregnancies and is associated with increased perinatal morbidity and mortality. Currently, there is no reliable biochemical test to differentiate a pathological FGR from a nonpathological one. The objective of this study was to screen whole maternal plasma to identify differentially expressed relatively abundant proteins associated with FGR. We analyzed maternal plasma from FGR (n=28) and healthy (n=22) pregnancies using two-dimensional gel electrophoresis (2D-GE) followed by software image analysis. Three spots with molecular weight (Mr) 18 kDa corresponding to haptoglobin (hp) α2, as identified by LC-MS/MS and immunoblotting, showed differential expression patterns in FGR. The distribution of hp α2 variants in maternal plasma samples showed the hp α2 variant 1 was low in 72% of FGR, medium in 16%, whereas high in 12%. In comparison, hp α2 variant 1 was high in (41%) of controls, medium in 41%, and low in 18% of cases. Based on the software image analysis, the mean spot volume for hp α2 variant 1 was 0.12 (SD=0.18) for FGR compared to 0.26 (SD=0.19) for control (p=0.006). Given that hp turnover is indicative of its maturation process and is traceable in plasma by its dominant/suppressed variants, we propose that hp α2 is an important potential target for evaluation of its clinical and pathophysiological role and as a diagnostic biomarker in FGR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".