Altered phenotype of HLA-G expressing trophoblast and decidual natural killer cells in pathological pregnancies
Bibliographic record
Abstract
BACKGROUND: The interaction between decidual natural killer (NK) cells and alloantigens expressed on fetal trophoblast cells are thought to be essential for successful implantation and placentation. Consequently, a disturbed interaction during the first trimester of pregnancy might well lead to a subsequent pregnancy failure. METHODS: We investigated the expression of HLA-G and NK cell markers in tissue sections from recurrent miscarriage (n = 9) and ectopic tubal pregnancies (n = 5), and two hysterectomy specimens of healthy pregnancy as well as decidual biopsies (n = 9) were used as controls. RESULTS: We show in normal pregnancy not only a decrease, but also a morphological change in CD56+ NK cells upon interaction with HLA-G-expressing trophoblasts. The cells appear to be transitioning from a blast-like (activation) state into a state of apoptosis. The number of CD16+ NK cells was low. In contrast, in recurrent miscarriage tissue a sustained NK cell marker expression of both CD56 and CD16 was paralleled by a decreased expression of HLA-G. No morphological changes from the blast-like stage were apparent. Finally, in ectopic pregnancies HLA-G expression in the absence of decidual NK cells was associated with a disturbed trophoblast differentiation. CONCLUSIONS: In pathological pregnancies we show an in-situ altered phenotype of trophoblast and NK cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".