The challenges of reliably collecting, storing, organizing, and analyzing research data are critical in low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa where several healthcare and biomedical research organizations have limited data infrastructure. The Research Electronic Data Capture (REDCap) System has been widely used by many institutions and hospitals in the USA for data collection, entry, and management and could help solve this problem. This study reports on the experiences, challenges, and lessons learned from establishing and applying REDCap for a large US-Nigeria research partnership that includes two sites in Nigeria, (the College of Medicine of the University of Lagos (CMUL) and Jos University Teaching Hospital (JUTH)) and Northwestern University (NU) in Chicago, Illinois in the United States. The largest challenges to this implementation were significant technical obstacles: the lack of REDCap-trained personnel, transient electrical power supply, and slow/intermittent internet connectivity. However, asynchronous communication and on-site hands-on collaboration between the Nigerian sites and NU led to the successful installation and configuration of REDCap to meet the needs of the Nigerian sites. An example of one lesson learned is the use of Virtual Private Network (VPN) as a solution to poor internet connectivity at one of the sites, and its adoption is underway at the other. Virtual Private Servers (VPS) or shared online hosting were also evaluated and offer alternative solutions. Installing and using REDCap in LMIC institutions for research data management is feasible; however, planning for trained personnel and addressing electrical and internet infrastructural requirements are essential to optimize its use. Building this fundamental research capacity within LMICs across Africa could substantially enhance the potential for more cross-institutional and cross-country collaboration in future research endeavors.
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http://dx.doi.org/10.1007/s12553-021-00600-3 | DOI Listing |
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Gansu Key Laboratory of Biomonitoring and Bioremediation for Environmental Pollution, School of Life Sciences, Lanzhou University, Lanzhou 730000, China. Electronic address:
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Hunan Province Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, Hengyang Medical school, University of South China, Hengyang, Hunan 421001, China. Electronic address:
Microcystin LR (MC-LR) pollution is a serious threat to aquatic ecosystems and public health in China and is an environmental problem that urgently needs to be solved. However, few studies have investigated the anaerobic degradation pathway and related molecular biological mechanisms of MC-LR. In this study, a bacterium capable of degrading MC-LR with a degradation efficiency of 0.
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West China Center of Excellence for Pancreatitis, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu 610041, China; Regenerative Medicine Research Center, Sichuan University West China Hospital, Chengdu, Sichuan 610041, China. Electronic address:
Dichlorvos (DDVP) is an organophosphorus pesticide commonly utilized in agricultural production. Recent epidemiological studies suggest that exposure to DDVP correlates with an increased incidence of liver disease. However, data regarding the hepatotoxicity of DDVP remain limited.
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RAND, Boston, MA, United States. Electronic address:
Importance: States have implemented multiple policies likely to influence opioid prescribing; few national general population studies examine those policies' effects on per-capita opioid morphine milligram equivalents (MME) dispensed.
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In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
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