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Unlocking the global health potential of dried blood spot cards.

作者信息

Bota Brianne, Ward Victoria, Lamoureux Monica, Santander Emeril, Ducharme Robin, Hawken Steven, Potter Beth K, Atito Raphael, Nyamanda Bryan, Munga Stephen, Otieno Nancy, Chakraborty Sowmitra, Saha Samir, Stringer Jeffrey Sa, Mwape Humphrey, Price Joan T, Mujuru Hilda Angela, Chimhini Gwendoline, Magwali Thulani, Chakraborty Pranesh, Darmstadt Gary L, Wilson Kumanan

机构信息

Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.

Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.

出版信息

J Glob Health. 2022 Jul 16;12:03027. doi: 10.7189/jogh.12.03027.

DOI:10.7189/jogh.12.03027
PMID:35841606
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9288235/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4726/9288235/8bf13b4d29b9/jogh-12-03027-F2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4726/9288235/de1720f5c81f/jogh-12-03027-Fa.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4726/9288235/e31b68e93a6a/jogh-12-03027-F1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4726/9288235/8bf13b4d29b9/jogh-12-03027-F2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4726/9288235/de1720f5c81f/jogh-12-03027-Fa.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4726/9288235/e31b68e93a6a/jogh-12-03027-F1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4726/9288235/8bf13b4d29b9/jogh-12-03027-F2.jpg

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本文引用的文献

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Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers.基于临床数据和代谢组学标志物的机器学习算法在产后胎龄估计中的开发和外部验证。
PLoS One. 2023 Mar 6;18(3):e0281074. doi: 10.1371/journal.pone.0281074. eCollection 2023.
2
A novel way of determining gestational age upon the birth of a child.一种在孩子出生时确定胎龄的新方法。
J Glob Health. 2021 Sep 4;11:03078. doi: 10.7189/jogh.11.03078. eCollection 2021.
3
Metabolic gestational age assessment in low resource settings: a validation protocol.
资源匮乏地区的代谢性孕龄评估：一项验证方案。
Gates Open Res. 2021 Jan 28;4:150. doi: 10.12688/gatesopenres.13155.2. eCollection 2020.
4
Building a Newborn Screening Information Management System from Theory to Practice.构建新生儿筛查信息管理系统：从理论到实践
Int J Neonatal Screen. 2019 Jan 23;5(1):9. doi: 10.3390/ijns5010009. eCollection 2019 Mar.
5
External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh.利用孟加拉国新生儿代谢特征的 Matlab 模型对产后胎龄估计进行外部验证。
Elife. 2019 Mar 19;8:e42627. doi: 10.7554/eLife.42627.
6
Incidental screen positive findings in a prospective cohort study in Matlab, Bangladesh: insights into expanded newborn screening for low-resource settings.在孟加拉国马利布尔的一项前瞻性队列研究中偶然发现的阳性筛查结果：对资源匮乏环境下扩大新生儿筛查的深入了解。
Orphanet J Rare Dis. 2019 Jan 30;14(1):25. doi: 10.1186/s13023-018-0993-1.
7
Metabolic profiles derived from residual blood spot samples: A longitudinal analysis.源自残余血斑样本的代谢谱：一项纵向分析。
Gates Open Res. 2018 May 30;2:28. doi: 10.12688/gatesopenres.12822.1.
8
Postnatal gestational age estimation using newborn screening blood spots: a proposed validation protocol.使用新生儿筛查血斑估计出生后胎龄：一项拟议的验证方案。
BMJ Glob Health. 2017 Jul 27;2(2):e000365. doi: 10.1136/bmjgh-2017-000365. eCollection 2017.
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Postnatal Prediction of Gestational Age Using Newborn Fetal Hemoglobin Levels.利用新生儿胎儿血红蛋白水平预测胎龄
EBioMedicine. 2017 Feb;15:203-209. doi: 10.1016/j.ebiom.2016.11.032. Epub 2016 Dec 1.
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Accurate prediction of gestational age using newborn screening analyte data.使用新生儿筛查分析物数据准确预测胎龄。
Am J Obstet Gynecol. 2016 Apr;214(4):513.e1-513.e9. doi: 10.1016/j.ajog.2015.10.017. Epub 2015 Oct 28.