Unsupervised Placental Gene Expression Profiling Identifies Clinically Relevant Subclasses of Human Preeclampsia
Why this work is in the frame
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Bibliographic record
Abstract
Preeclampsia (PE) is a complex, hypertensive disorder of pregnancy, demonstrating considerable variability in maternal symptoms and fetal outcomes. Unfortunately, prior research has not accounted for this variability, resulting in a lack of robust biomarkers and effective treatments for PE. Here, we created a large (N=330) clinically relevant human placental microarray data set, consisting of 7 previously published studies and 157 highly annotated new samples from a single BioBank. Applying unsupervised clustering to this combined data set identified 3 clinically significant probable etiologies of PE: "maternal", with healthy placentas and term deliveries; "canonical", exhibiting expected clinical, ontological, and histopathologic features of PE; and "immunologic" with severe fetal growth restriction and evidence of maternal antifetal rejection. Moreover, these groups could be distinguished using a small quantitative polymerase chain reaction panel and demonstrated varying influence of maternal factors on PE development. An additional subclass of PE placentas was also revealed to form because of chromosomal abnormalities in these samples, supported by array-based comparative genomic hybridization analysis. Overall, our findings represent a new paradigm in our understanding of the origins and maternal-placental contributions to the pathology of PE. The study of PE represents a unique opportunity to access human tissue associated with a complex hypertensive disorder, and our novel approach could be applied to other hypertensive and heterogeneous human diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it