Decidual NK Cell‐Derived Conditioned Medium (dNK‐CM) Mediates VEGF‐C Secretion in Extravillous Cytotrophoblasts
Bibliographic record
Abstract
PROBLEM: The regulatory mechanisms involved in VEGF-C secretion by trophoblasts during placentation are poorly understood. We investigated whether or not decidual natural killer cell conditioned medium (dNK-CM) stimulated VEGF-C secretion in the extravillous cytotrophoblast (EVT) cell line HTR8/SVneo. METHOD OF STUDY: The effects of dNK-CM and recombinant IFN-γ on VEGF-C induction by HTR8/SVneo were studied in the absence or presence of IFN-γ or its receptor blocking antibodies, p38 inhibitor (SB202190), JAK inhibitor (JAK inhibitor-1, JI-1), and on STAT1 knockdown HTR8/SVneo. VEGF-C was quantified by ELISA. FACS was used to investigate the phosphorylations of Tyr701 or Ser727 of STAT1 on stimulated HTR8/SVneo. RESULTS: dNK-CM facilitated VEGF-C secretion by HTR8/SVneo. IFN-γ and IFN-γR1 or IFN-γR2 blocking antibodies reduced both dNK-CM- and IFN-γ-induced VEGF-C secretion. Phosphorylations on Tyr701 or Ser727 of STAT1 were elevated upon stimulation. Secretion of VEGF-C was reduced by treatment with SB202190, JI-1, or STAT1 knockdown by siRNA. CONCLUSION: VEGF-C production by trophoblasts is regulated by soluble factors secreted by dNK through p38 and JAK-STAT1 pathways.
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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) | 0.001 | 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".