Local fetal signal is not required for maintaining IGFBP gene expression in the human decidua: evidence from extrauterine pregnancies
Bibliographic record
Abstract
Insulin-like growth factor-II (IGF-II) from the invading extravillous cytotrophoblasts (EVTs) and insulin-like growth factor binding proteins (IGFBPs) from the maternal decidua interact at the feto-maternal interface and regulate implantation and placentation. To determine whether a local stimulus from the fetus is important in the regulation of IGFBP gene expression in the human decidua, we compared the expression of IGFBP genes in intra- and extrauterine (tubal) pregnancies. The expression of IGF-II and IGFBP-1 to IGFBP-6 mRNAs was determined by in-situ hybridization in the Fallopian tubes of extrauterine pregnancies and concurrent decidua (n = 6), and in the placentae and Fallopian tubes of intrauterine pregnancies (n = 6). All six IGFBP mRNAs were identified in the decidualized endometrium and decidualized Fallopian tubes of intra- and extrauterine pregnancies, with IGFBP-1 mRNA being the predominant mRNA. IGFBP-4 was the second most predominant mRNA and was slightly more abundant in the decidua of extrauterine pregnancies than of intrauterine pregnancies. IGF-II mRNA was expressed mainly in cells of fetal origin. The fact that the IGFBP mRNAs were expressed similarly in both intra- and extrauterine pregnancies indicates that the local physical stimulus from an implanting fetus is not necessary to induce or maintain decidual IGFBP gene expression.
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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 | 0.000 | 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 | 0.000 |
| 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".