HLA-G Gene Polymorphism in Human Placentas: Possible Association of G*0106 Allele with Preeclampsia and Miscarriage
Bibliographic record
Abstract
Definite causes for several pathologies of pregnancy remain unknown. In light of several recent studies, however, diminished or aberrant HLA-G expression may be associated with certain complication of pregnancy and be linked to HLA-G polymorphism. We analyzed DNA from 60 normal placentas (controls), 140 placentas from miscarriage, 36 placentas from preeclampsia, 76 placentas from fetal hypotrophy, and 34 placentas with hypoxia for variations in coding regions (allelic groups G*0101 to G*0107) and the 14-bp deletion/insertion into the 3'-untranslated region. No statistically significant differences were observed in the distribution of allelic group between pathological placentas and controls with the exception of G*0106 allele frequency in preeclamptic compared with control placentas (21.2% and 6.6%, respectively). A greater frequency of this allele also was observed in the two subgroups of miscarriage and hypoxia compared with that in controls. In addition, presence of the 14-bp sequence was prominent in preeclampsia compared with controls (60.8% vs. 35%, respectively), and homozygotes with deletion were not detected in the pathology. The results suggest that the G*0106 allele, which is coupled with the presence of the 14-bp sequence, contributes and/or is a relevant marker in some specific complications of pregnancy, especially 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".