Uterine natural killer cells: supervisors of vasculature construction in early decidua basalis
Bibliographic record
Abstract
Mammalian pregnancy involves tremendous de novo maternal vascular construction to adequately support conceptus development. In early mouse decidua basalis (DB), maternal uterine natural killer (uNK) cells oversee this process directing various aspects during the formation of supportive vascular networks. The uNK cells recruited to early implantation site DB secrete numerous factors that act in the construction of early decidual vessels (neoangiogenesis) as well as in the alteration of the structural components of newly developing and existing vessels (pruning and remodeling). Although decidual and placental development sufficient to support live births occur in the absence of normally functioning uNK cells, development and structure of implantation site are optimized through the presence of normally activated uNK cells. Human NK cells are also recruited to early decidua. Gestational complications including recurrent spontaneous abortion, fetal growth restriction, preeclampsia, and preterm labor are linked with the absence of human NK cell activation via paternally inherited conceptus transplantation antigens. This review summarizes the roles that mouse uNK cells normally play in decidual neoangiogenesis and spiral artery remodeling in mouse pregnancy and briefly discusses changes in early developmental angiogenesis due to placental growth factor deficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".