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2. Global inequities in adverse pregnancy outcomes: what can we do?

3. Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study

11. List of Contributors

14. Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study

16. Global inequities in adverse pregnancy outcomes: what can we do?

17. IFPA meeting 2018 workshop report II: Abnormally invasive placenta; inflammation and infection; preeclampsia; gestational trophoblastic disease and drug delivery

25. mHealth clinical decision-making tools for maternal and perinatal health care in Sub-Saharan Africa: A systematic review protocol

26. PREPARE: A Stepped-Wedge Cluster-Randomized Trial to Evaluate Whether Risk Stratification Can Reduce Preterm Deliveries Among Patients With Suspected or Confirmed Preterm Preeclampsia

32. Evaluating public health effects of risk-based travel policy for the COVID-19 epidemic in Scotland

33. Extending the scope of pooled analyses of individual patient biomarker data from heterogeneous laboratory platforms and cohorts using merging algorithms

40. ExoCounter Assays Identify Women Who May Develop Early-Onset Preeclampsia From 12.5 μL First-Trimester Serum by Characterizing Placental Small Extracellular Vesicles

42. Placental capillary pericytes release excess extracellular vesicles under hypoxic conditions inducing a pro-angiogenic profile in term pregnancy

44. Studies on the energy metabolism of herpetomonads

50. Use of mHealth tools to register birth outcomes in low-income and middle-income countries: a scoping review

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