The role and regulation of IGFBP-1 phosphorylation in fetal growth restriction
Bibliographic record
Abstract
Fetal growth restriction (FGR) increases the risk of perinatal complications and predisposes the infant to developing metabolic, cardiovascular, and neurological diseases in childhood and adulthood. The pathophysiology underlying FGR remains poorly understood and there is no specific treatment available. Biomarkers for early detection are also lacking. The insulin-like growth factor (IGF) system is an important regulator of fetal growth. IGF-I is the primary regulator of fetal growth, and fetal circulating levels of IGF-I are decreased in FGR. IGF-I activity is influenced by a family of IGF binding proteins (IGFBPs), which bind to IGF-I and decrease its bioavailability. During fetal development the predominant IGF-I binding protein in fetal circulation is IGFBP-1, which is primarily secreted by the fetal liver. IGFBP-1 binds IGF-I and thereby inhibits its bioactivity. Fetal circulating levels of IGF-I are decreased and concentrations of IGFBP-1 are increased in FGR. Phosphorylation of human IGFBP-1 at specific sites markedly increases its binding affinity for IGF-I, further limiting IGF-I bioactivity. Recent experimental evidence suggests that IGFBP-1 phosphorylation is markedly increased in the circulation of FGR fetuses suggesting an important role of IGFBP-1 phosphorylation in the regulation of fetal growth. Understanding of the significance of site-specific IGFBP-1 phosphorylation and how it is regulated to contribute to fetal growth will be an important step in designing strategies for preventing, managing, and/or treating FGR. Furthermore, IGFBP-1 hyperphosphorylation at unique sites may serve as a valuable biomarker for 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".