Recurrent Placental Transcriptional Profile With a Different Histological and Clinical Presentation: A Case Report
Bibliographic record
Abstract
Statistically, patients with severe pregnancy complications are at risk of recurrent complications, but it is less understood if patients present with similar or different placental pathologies in subsequent pregnancies. In this case report, we describe 2 consecutive adverse pregnancies in the same woman 4 years apart. The first pregnancy was diagnosed as early-onset preeclampsia and hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome, with placental maternal vascular malperfusion features, such as syncytial knots and accelerated villous maturity. In contrast, the second pregnancy was associated with normotensive fetal growth restriction and placental "immunological" lesions, such as massive perivillous fibrin deposition and chronic intervillositis. However, based on the expression of FLT1, LIMCH1, and TAP1 by quantitative polymerase chain reaction, the placentas from both pregnancies were found to exhibit an "immunological" transcriptional signature. This suggests that this small panel of gene expression markers may be able to predict the future reoccurrence of an immunological placental pathology despite no histological evidence within the first pregnancy. These results call for more studies looking at paired pregnancies of individuals with recurrent obstetric complications and confirm the importance of assessing matched transcriptional and histopathological placental information.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".