IUGR Is Associated With Marked Hyperphosphorylation of Decidual and Maternal Plasma IGFBP-1
Bibliographic record
Abstract
Context: The mechanisms underpinning intrauterine growth restriction (IUGR), as a result of placental insufficiency, remain poorly understood, no specific treatment is available, and clinically useful biomarkers for early detection are lacking. Objective: We hypothesized that human IUGR is associated with inhibition of mechanistic target of rapamycin (mTOR) and activation of amino acid response (AAR) signaling, increased protein kinase casein kinase-2 (CK2) activity, and increased insulin-like growth factor-binding protein 1 (IGFBP-1) expression and phosphorylation in decidua and that maternal plasma IGFBP-1 hyperphosphorylation in the first trimester predicts later development of IUGR. Design, Setting, and Participants: Decidua [n = 16 appropriate-for-gestational age (AGA); n = 16 IUGR] and maternal plasma (n = 13 AGA; n = 13 IUGR) were collected at delivery from two different cohorts. In addition, maternal plasma was obtained in the late first trimester from a third cohort of women (n = 7) who later delivered an AGA or IUGR infant. Main Outcome Measures: Total IGFBP-1 expression and phosphorylation (Ser101/Ser119/Ser169), mTOR, AAR, and CK2 activity in decidua and IGFBP-1 concentration and phosphorylation in maternal plasma. Results: We show that decidual IGFBP-1 expression and phosphorylation are increased, mTOR is markedly inhibited, and AAR and CK2 are activated in IUGR. Moreover, IGFBP-1 hyperphosphorylation in first-trimester maternal plasma is associated with the development of IUGR. Conclusions: These data are consistent with the possibility that the decidua functions as a nutrient sensor linking limited oxygen and nutrient availability to increased IGFBP-1 phosphorylation, possibly mediated by mTOR and AAR signaling. IGFBP-1 hyperphosphorylation in first-trimester maternal plasma may serve as a predictive IUGR biomarker, allowing early intervention.
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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".