Decorin production by the human decidua: role in decidual cell maturation
Bibliographic record
Abstract
Decidualization involves the proliferation and differentiation of fibroblast-like endometrial stromal cells into epithelioid-shaped and secretory 'decidual' cells in response to steroid hormones. Human decidual cells produce insulin-like growth factor-binding protein-1 and prolactin (PRL), two well-recognized markers of decidual cell maturation and a proteoglycan decorin (DCN). We reported that DCN restrains the human trophoblast renewal, migration, invasion and endovascular differentiation needed for uterine arterial remodeling during normal pregnancy. DCN overproduction by the decidua is associated with a hypo-invasive placenta and a serious pregnancy disorder, pre-eclampsia (PE). Furthermore, elevated maternal plasma DCN levels during the second trimester is a predictive biomarker of PE. While these paracrine roles of decidua-derived DCN on trophoblast physiology and pathology have been well-defined, it remains unknown whether DCN plays any autocrine role in decidual cell development. The objectives of this study were to examine: the kinetics of DCN production during decidualization of human endometrial stromal cells; gestational age-related changes in DCN production by the first trimester decidua; and a possible autocrine role of DCN on decidual cell maturation. We found that DCN production is enhanced during decidualization of both primary and immortalized human endometrial stromal cells in vitro and during early gestation in decidual samples tested ex vivo, and that it is important for endometrial stromal cell maturation into a decidual phenotype. Decorin-depleted human endometrial stromal cells exposed to decidualizing stimuli failed to mature fully, as evidenced by fibroblastoid morphology, reduced insulin-like growth factor-binding protein-1 and PRL expression, and reduction in cellular ploidy. We identified heart and neural crest derivatives-expressed protein 2, and progesterone receptor as potential downstream mediators of DCN effects.
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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 | 0.000 |
| 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".