Development and validation of a transcriptomic signature-based model as the predictive, preventive, and personalized medical strategy for preterm birth within 7 days in threatened preterm labor women-Reference-Cited by-同舟云学术

Development and validation of a transcriptomic signature-based model as the predictive, preventive, and personalized medical strategy for preterm birth within 7 days in threatened preterm labor women

Published:2022-01-18 Issue:1 Volume:13 Page:87-106
ISSN:1878-5077
Container-title:EPMA Journal
language:en
Short-container-title:EPMA Journal

Author:

Ran Yuxin,He Jie,Peng Wei,Liu Zheng,Mei Youwen,Zhou Yunqian,Yin Nanlin,Qi Hongbo

Funder

National Natural Science Foundation of China for Youth

General Program of National Natural Science Foundation of China

Key Program of International (Regional) Cooperation of NSFC

Chongqing Municipal Health Commission

Chongqing Municipal Education Commission

Science and Technology Department of Sichuan Province

Publisher

Springer Science and Business Media LLC

Subject

Biochemistry (medical),Health Policy,Drug Discovery

Link

https://link.springer.com/content/pdf/10.1007/s13167-021-00268-9.pdf

Reference105 articles.

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2. da Fonseca EB, Damião R, Moreira DA. Preterm birth prevention. Best Pract Res Clin Obstet Gynaecol. 2020;69:40–9. https://doi.org/10.1016/j.bpobgyn.2020.09.003.

3. Vogel JP, Chawanpaiboon S, Moller AB, Watananirun K, Bonet M, Lumbiganon P. The global epidemiology of preterm birth. Best Pract Res Clin Obstet Gynaecol. 2018;52:3–12. https://doi.org/10.1016/j.bpobgyn.2018.04.003.

4. Chawanpaiboon S, Vogel JP, Moller A-B, Lumbiganon P, Petzold M, Hogan D, et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health. 2019;7(1):e37–46. https://doi.org/10.1016/s2214-109x(18)30451-0.

5. Crowther CA, Middleton PF, Voysey M, Askie L, Zhang S, Martlow TK, et al. Effects of repeat prenatal corticosteroids given to women at risk of preterm birth: an individual participant data meta-analysis. PLoS Med. 2019;16(4): e1002771. https://doi.org/10.1371/journal.pmed.1002771.

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