TGFb1 suppresses the activation of distinct dNK subpopulations in preeclampsia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it