Discovery and verification of extracellular microRNA biomarkers for diagnostic and prognostic assessment of preeclampsia at triage.

We report on the identification of extracellular miRNA (ex-miRNA) biomarkers for early diagnosis and prognosis of preeclampsia (PE). Small RNA sequencing of maternal serum prospectively collected from participants undergoing evaluation for suspected PE revealed distinct patterns of ex-miRNA expression among different categories of hypertensive disorders in pregnancy. Applying an iterative machine learning method identified three bivariate miRNA biomarkers (miR-522-3p/miR-4732-5p, miR-516a-5p/miR-144-3p, and miR-27b-3p/let-7b-5p) that, when applied serially, distinguished between PE cases of different severity and differentiated cases from controls with a sensitivity of 93%, specificity of 79%, positive predictive value (PPV) of 55%, and negative predictive value (NPV) of 89%. In a small independent validation cohort, these ex-miRNA biomarkers had a sensitivity of 91% and specificity of 57%. Combining these ex-miRNA biomarkers with the established sFlt1:PlGF protein biomarker ratio performed better than either set of biomarkers alone (sensitivity of 89.4%, specificity of 91.3%, PPV of 95.5%, and NPV of 80.8%).

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics.

The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.