Interferon γ Contributes to Initiation of Uterine Vascular Modification, Decidual Integrity, and Uterine Natural Killer Cell Maturation during Normal Murine Pregnancy
Bibliographic record
Abstract
The dominant lymphocytes in human and murine implantation sites are transient, pregnancy-associated uterine natural killer (uNK) cells. These cells are a major source of interferon (IFN)-gamma. Implantation sites in mice lacking uNK cells (alymphoid recombinase activating gene [RAG]-2(-/)- common cytokine receptor chain gamma [gamma(c)](-/)-) or IFN-gamma signaling (IFN-gamma(-/)- or IFN-gammaRalpha(-/)-) fail to initiate normal pregnancy-induced modification of decidual arteries and display hypocellularity or necrosis of decidua. To investigate the functions of uNK cell-derived IFN-gamma during pregnancy, RAG-2(-/)-gamma(c)(-/)- females were engrafted with bone marrow from IFN-gamma(-/)- mice, IFN-gamma signal-disrupted mice (IFN-gammaRalpha(-/)- or signal transducer and activator of transcription [Stat]-1(-/)-), or from mice able to establish normal uNK cells (severe combined immunodeficient [SCID] or C57BL/6). Mated recipients were analyzed at midgestation. All grafts established uNK cells. Grafts from IFN-gamma(-/)- mice did not reverse host vascular or decidual pathology. Grafts from all other donors promoted modification of decidual arteries and decidual cellularity. Grafts from IFN-gammaRalpha(-/)- or Stat-1(-/)- mice overproduced uNK cells, all of which were immature. Grafts from IFN-gamma(-/)-, SCID, or C57BL/6 mice produced normal, mature uNK cells. Administration of murine recombinant IFN-gamma to pregnant RAG-2(-/)-gamma(c)(-/)- mice initiated decidual vessel modification and promoted decidual cellularity in the absence of uNK cells. These in vivo findings strongly suggest that uNK cell-derived IFN-gamma modifies the expression of genes in the uterine vasculature and stroma, which initiates vessel instability and facilitates pregnancy-induced remodeling of decidual arteries.
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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".