ADAM12 and PAPP-A: Candidate regulators of trophoblast invasion and first trimester markers of healthy trophoblasts
Bibliographic record
Abstract
Proper placental development and function is crucial for a healthy pregnancy, and there has been substantial research to identify markers of placental dysfunction for the early detection of pregnancy complications. Low first-trimester levels of a disintegrin and metalloproteinase 12 (ADAM12) and pregnancy-associated plasma protein-A (PAPP-A) have been consistently associated with the subsequent development of preeclampsia and fetal growth restriction. These molecules are both metalloproteinases secreted by the placenta that cleave insulin-like growth factor binding proteins (IGFBPs), although ADAM12 also has numerous other substrates. Recent work has identified ADAM12, and particularly its shorter variant, ADAM12S, as a regulator of the migration and invasion of trophoblasts into the lining of the uterus, a critical step in normal placental development. While the mechanisms underlying this regulation are not yet clear, they may involve the liberation of heparin-binding EGF-like growth factor (HB-EGF) and/or IGFs from IGFBPs. In contrast, there has been relatively little functional work examining PAPP-A or the IGFBP substrates of ADAM12 and PAPP-A. Understanding the functions of these markers and the mechanisms underlying their association with disease could improve screening strategies and enable the development of new therapeutic interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".