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Improving wastewater-based epidemiology performance through streamlined automation.
Dehghan Banadaki, Mohammad; Torabi, Soroosh; Strike, William D; Noble, Ann; Keck, James W; Berry, Scott M.
  • Dehghan Banadaki M; Department of Mechanical Engineering, College of Engineering, University of Kentucky, United States.
  • Torabi S; Department of Mechanical Engineering, College of Engineering, University of Kentucky, United States.
  • Strike WD; Department of Biomedical Engineering, College of Engineering, University of Kentucky, United States.
  • Noble A; Department of Mechanical Engineering, College of Engineering, University of Kentucky, United States.
  • Keck JW; Department of Family and Community Medicine, College of Medicine, University of Kentucky, United States.
  • Berry SM; Department of Mechanical Engineering, College of Engineering, University of Kentucky, United States.
J Environ Chem Eng ; 11(2): 109595, 2023 Apr.
Article in English | MEDLINE | ID: covidwho-2256764
ABSTRACT
Wastewater-based epidemiology (WBE) has enabled us to describe Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections in populations. However, implementation of wastewater monitoring of SARS-CoV-2 is limited due to the need for expert staff, expensive equipment, and prolonged processing times. As WBE increases in scope (beyond SARS-CoV-2) and scale (beyond developed regions), there is a need to make WBE processes simpler, cheaper, and faster. We developed an automated workflow based on a simplified method termed exclusion-based sample preparation (ESP). Our automated workflow takes 40 min from raw wastewater to purified RNA, which is several times faster than conventional WBE methods. The total assay cost per sample/replicate is $6.50 which includes consumables and reagents for concentration, extraction, and RT-qPCR quantification. The assay complexity is reduced significantly, as extraction and concentration steps are integrated and automated. The high recovery efficiency of the automated assay (84.5 ± 25.4%) yielded an improved Limit of Detection (LoDAutomated=40 copies/mL) compared to the manual process (LoDManual=206 copies/mL), increasing analytical sensitivity. We validated the performance of the automated workflow by comparing it with the manual method using wastewater samples from several locations. The results from the two methods correlated strongly (r = 0.953), while the automated method was shown to be more precise. In 83% of the samples, the automated method showed lower variation between replicates, which is likely due to higher technical errors in the manual process e.g., pipetting. Our automated wastewater workflow can support the expansion of WBE in the fight against Coronavirus Disease of 2019 (COVID-19) and other epidemics.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Observational study / Prognostic study Language: English Journal: J Environ Chem Eng Year: 2023 Document Type: Article Affiliation country: J.jece.2023.109595

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Observational study / Prognostic study Language: English Journal: J Environ Chem Eng Year: 2023 Document Type: Article Affiliation country: J.jece.2023.109595