Lack of human leukocyte antigen-G expression in extravillous trophoblasts is associated with pre-eclampsia
Bibliographic record
Abstract
Pre-eclampsia, a common complication of first pregnancies, is thought to result from a poorly perfused placenta and may reflect an abnormal maternal immune reaction to the hemiallogenic fetus. Human leukocyte antigen (HLA)-G, a major histocompatibility tissue-specific antigen expressed in extravillous trophoblast cells (fetal-derived), may protect trophoblasts from maternal-fetal immune intolerance and allow these cells to invade the uterus. Through RNA in-situ hybridization analysis, we studied the expression pattern of HLA-G in normal placentae and placentae from pregnancies complicated by severe pre-eclampsia. In normal placenta we found HLA-G expression in the anchoring extravillous trophoblasts with an increasing gradient of expression in the more invasive cells. However, in nine out of 10 pre-eclamptic placentae HLA-G expression was absent or reduced. We conclude that HLA-G is normally expressed in invasive trophoblasts and HLA-G expression is defective in most pre-eclamptic placentae. We propose that trophoblasts lacking HLA-G are vulnerable to attack by the maternal immune system. These defective trophoblasts will be unable to invade the maternal spiral arteries effectively, thereby developing vessels which cannot adequately nourish the developing placenta. This poorly perfused placenta may initiate the systemic cascade of events associated with pre-eclampsia.
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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 | 0.000 | 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".