David K. Stevenson

The paper uses single-nucleus RNA-seq and spatial transcriptomics to show how the maternal-fetal interface diverges in structure and function between the basal plate and chorionic membranes, identifying NID2 as an ancestry-associated regulator of trophoblast invasion.

This study identifies simvastatin and primaquine as potential therapeutics for endometriosis through computational drug repositioning, showing reduced pain and gene expression changes in animal models and lower disease risk in EMR data.

CDRPipe integrates single-cell and bulk transcriptomic perturbation data to improve drug repurposing predictions by combining complementary resources.

The paper introduces the Intolerome Database, a curated resource of genes essential for human viability, to help prioritize genomic variants in cases of stillbirth, pregnancy loss, and neonatal death.

This study identifies persistent immune dysregulation in women with hypertensive disorders of pregnancy years after delivery, linked to increased cardiovascular disease risk.

The paper identifies genetic variants in 30 families with unexplained euploid pregnancy loss, finding monogenic causes in 25% of cases and expanding understanding of early human lethality.

The paper introduces SIMCO, a single-cell digital twin framework for evaluating PTL prevention drugs, identifying tetrahydrofolate, maprotiline, and aspirin-lansoprazole as top candidates.

The paper introduces anticlustering to balance batches and minimize batch effects in high-throughput sequencing, showing improved balance compared to existing methods.

This study creates a comprehensive single-cell atlas of the maternal-fetal interface, identifying cell-type-specific dysregulation in pregnancy complications.

This study identifies changes in pregnancy-related serum biomarkers early in gestation as indicators of later preeclampsia development, validated in both human and mouse models.

The paper introduces a machine learning method that integrates immunological knowledge to improve predictive power from mass cytometry data.

This study uses cell-free RNA from blood samples to create a molecular clock for fetal development, predicting gestational age and preterm delivery risk.