Unlocking the global health potential of dried blood spot cards-Reference-Cited by-同舟云学术

Unlocking the global health potential of dried blood spot cards

Published:2022-07-16 Issue: Volume:12 Page:
ISSN:2047-2978
Container-title:Journal of Global Health
language:
Short-container-title:J Glob Health

Author:

Bota Brianne¹,Ward Victoria²,Lamoureux Monica³,Santander Emeril³,Ducharme Robin¹,Hawken Steven¹⁴,Potter Beth K⁵,Atito Raphael⁶,Nyamanda Bryan⁶,Munga Stephen⁶,Otieno Nancy⁶,Chakraborty Sowmitra⁷,Saha Samir⁷,Stringer Jeffrey SA⁸⁹,Mwape Humphrey⁹,Price Joan T⁸⁹,Mujuru Hilda Angela¹⁰,Chimhini Gwendoline¹⁰,Magwali Thulani¹¹,Chakraborty Pranesh³⁵,Darmstadt Gary L²,Wilson Kumanan¹⁴¹²¹³

Affiliation:

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

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

3. Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Canada

4. School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada

5. Department of Pediatrics, University of Ottawa, Ottawa, Canada

6. Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya

7. Child Health Research Foundation, Mirzapur, Bangladesh

8. Department of Obstetrics and Gynecology, UNC School of Medicine, Chapel Hill, North Carolina, USA

9. UNC Global Projects Zambia, Lusaka, Zambia

10. Department of Paediatrics and Child Health, University of Zimbabwe, Harare, Zimbabwe

11. Department of Obstetrics and Gynaecology, Faculty of Medicine and Health Sciences, University of Zimbabwe, Harare, Zimbabwe

12. Department of Medicine, University of Ottawa, Ottawa, Canada

13. Bruyere Research Institute, Ottawa, Ontario

Publisher

International Global Health Society

Subject

Public Health, Environmental and Occupational Health,Health Policy

Reference15 articles.

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3. Postnatal Prediction of Gestational Age Using Newborn Fetal Hemoglobin Levels.;Wilson;EBioMedicine,2017

4. Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers Authors and affiliations.;Hawken;medRxiv,2020

5. External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh.;Murphy;eLife,2019