Aditiawarman
8 Papers
3 Citations
Aditiawarman is an academic researcher. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 2, co-authored 3 publications.
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Papers
Effect of maternal multiple micronutrient supplementation on fetal loss and infant death in Indonesia: a double-blind cluster-randomised trial.
Anuraj H. Shankar,Abas Basuni Jahari,Susy Katikana Sebayang,Aditiawarman,Mandri Apriatni,Benyamin Harefa,Husni Muadz,S D A Soesbandoro,Roy Tjiong,A Fachry,Anita V. Shankar,Atmarita,Sri Prihatini,G Sofia +13 more
TL;DR: Maternal MMN supplementation, as compared with IFA, can reduce early infant mortality, especially in undernourished and anaemic women, and might be an important part of overall strengthening of prenatal-care programmes.
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A retrospective cohort study of hypertension, cardiovascular disease, and metabolic syndrome risk in women with history of preterm and term preeclampsia five years after delivery.
TL;DR: In this article , a retrospective cohort study of women who delivered at Dr. Soetomo Academic Hospital (Indonesia) in 2013 with a diagnosis of preterm preeclampsia (P-PE) and were compared with women with normal pregnancies.
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The role of transdermal carbon dioxide on changes in malondialdehyde levels as a marker of ischemia-reperfusion injury in patients with placenta accreta spectrum underwent temporary abdominal aortic cross-clamping as an adjunct procedure during cesarean hysterectomy
Hari Daswin Pagehgiri,Ito Puruhito,Aditiawarman,Pudji Lestari,Yan Efrata Sembiring,Dhihintia Jiwangga,A. R. Hakim,Rozi Aditya Aryananda +7 more
TL;DR: In this paper , transdermal CO2 was used to reduce the release of ROS through the Bohr Effect to protect against ischemia-reperfusion injury, which can be seen from the level of malondialdehyde.
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Cost-Sensitive Machine Learning Classification for Mass Tuberculosis Screening
TL;DR: The results indicate that even with limited data, the authors can actually devise a better method to identify TB suspects from verbal screening, and may be a stepping stone towards more effective TB case identification, especially in primary health centres, and foster better detection and control of TB.
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Cost-Sensitive Machine Learning Classification for Mass Tuberculosis Verbal Screening.
TL;DR: The results indicate that even with limited data, one can actually devise a better method to identify TB suspects from verbal screening, and that only 2000 data points were sufficient to enable the model to converge.
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