TGFb1 suppresses the activation of distinct dNK subpopulations in preeclampsia
Bibliographic record
Abstract
BACKGROUND: Decidual natural killer (dNK) cells are the predominant lymphocytes accumulated at the maternal-fetal interface. Regulatory mechanism of dNK cells in preeclampsia, a gestational complication characterized by high blood pressure and increased proteinuria occurring after 20 weeks pregnancy, is not completely understood. METHODS: Multi-parameter flow cytometry is applied to investigate the phenotype and function of dNK cells freshly isolated from decidual samples or conditionally cultured by TGFb stimulation. FINDINGS: regulatory T (Treg) cells within the decidua. In vitro experiments using dNK cells from early gestation showed that dNK activation (IFNG, IL-8 and CD107a) can be downregulated by Treg cells. The expression of these markers by dNK cells was significantly lower in preeclampsia. We also observed a positive correlation between the expression of dNK activation receptors (NKp30 and NKG2D) and the expression of IFNG in specific dNK subsets. TGFb levels are increased in the decidua of preeclamptic pregnancies. We analyzed co-expression of activation (IFNG/IL-8/CD107a) and angiogenic (VEGF) markers in dNK cells. TGFb treatment reduced while blockade of TGFb increased co-expression of these markers. INTERPRETATION: Our findings suggest that elevated decidual TGFb1 supresses the activation of specific subsets of dNK which in turn contributes to the uteroplacental pathology associated with the onset of preeclampsia.
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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) | 0.002 | 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".