Maternal obesity alters uterine NK cell activity through a functional KIR2DL1/S1 imbalance
Bibliographic record
Abstract
ABSTRACT In pregnancy, uterine natural killer cells (uNK) play essential roles in coordinating uterine angiogenesis, blood vessel remodeling, and promoting maternal tolerance to fetal tissue. Deviances from a normal uterine microenvironment are thought to modify uNK function(s), limiting their ability to establish a healthy pregnancy. While maternal obesity has become a major health concern due to associations with adverse effects on fetal and maternal health, our understanding into how obesity contributes to poor pregnancy disorders is essentially unknown. Given the importance of uNK in pregnancy, this study sets out to examine if obesity affects uNK function. Using a cohort of pregnant women, we show that baseline activity of uNK from obese women is elevated, but that enhanced activity does not equate to increased killing potential. Instead, obesity associates with altered uNK production of angiogenic VEGF-A and PlGF. These changes coincide with alterations in NKp46 + and NKG2A + uNK subsets and elevated expression of KIR2D(L1/S1/S3/S5) receptors. Detailed examination revealed that obesity leads to imbalances in KIR2DL1/S1 expression that together instruct altered responses to HLA-C2 antigen, including increased production of TNFα. Together, these findings suggest that maternal obesity modulates uNK function by altering angiokine/cytokine production and the response to HLA-C2 antigen.
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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) | 0.002 | 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".