Machine learning-based protein signatures for differentiating hypertensive disorders of pregnancy
Author:
Publisher
Springer Science and Business Media LLC
Subject
Cardiology and Cardiovascular Medicine,Physiology,Internal Medicine
Link
https://www.nature.com/articles/s41440-023-01348-1.pdf
Reference61 articles.
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2. Brown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, et al. Hypertensive disorders of pregnancy: ISSHP classification, diagnosis, and management recommendations for international practice. Hypertension. 2018;72:24–43. https://doi.org/10.1161/HYPERTENSIONAHA.117.10803.
3. McElwain CJ, Tuboly E, McCarthy FP, McCarthy CM. Mechanisms of endothelial dysfunction in pre-eclampsia and gestational diabetes mellitus: windows into future cardiometabolic health? Front Endocrinol. 2020;11:655.
4. Sibai BM. Diagnosis and management of gestational hypertension and preeclampsia. Obstet Gynecol. 2003;102:181–92. https://doi.org/10.1016/s0029-7844(03)00475-7.
5. Kyozuka H, Fujimori K, Hosoya M, Yasumura S, Yokoyama T, Sato A, et al. The Japan Environment and Children’s Study (JECS) in Fukushima Prefecture: pregnancy outcome after the Great East Japan Earthquake. Tohoku J Exp Med. 2018;246:27–33. https://doi.org/10.1620/tjem.246.27.
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