Isolation and in silico analysis of antioxidants in response to temporal variations in the cyanobacterium Oscillatoria sp

COVID-19 hospitalizations and deaths predicted by SARS-CoV-2 levels in Boise, Idaho wastewater

The viral load of COVID-19 in untreated wastewater from Idaho's capital city Boise, ID (Ada County) has been used to predict changes in hospital admissions (statewide in Idaho) and deaths (Ada County) using distributed fixed lag modeling and artificial neural networks (ANN). The wastewater viral counts were used to determine the lag time between peaks in wastewater viral counts and COVID-19 hospitalizations as well as deaths (14 and 23 days, respectively). Quantitative measurement of SARS-CoV-2 viral RNA counts in the untreated wastewater was determined three times a week using RT-qPCR over a span of 13 months. To mitigate the effects of PCR inhibitors in wastewater, a series of dilution tests were conducted, and the 1/4 dilution was used to generate the most successful model. Wastewater SARS-CoV-2 viral RNA counts and hospitalization from June 7, 2021 to December 29, 2021 were used as training data to predict hospitalizations; and wastewater SARS-CoV-2 viral RNA counts and deaths from June 7, 2021 to December 20, 2021 were used as training data to predict deaths. These training data were used to make predictive ANN models for future hospitalizations and deaths. To the best of our knowledge, this is the first report of prediction of deaths from COVID-19 based on wastewater SARS-CoV-2 viral RNA counts using machine learning-based multilayered ANN. The applied modeling demonstrates that wastewater surveillance data can be combined with hospitalizations and death data to generate machine learning-based ANN models that predict future COVID-19 hospital admissions and deaths, providing an early warning for medical response teams and healthcare policymakers.

Application of Spectroscopic Methods for the Identification of Superoxide Dismutases in Cyanobacteria

Superoxide dismutases (SODs) belong to the group of metalloenzymes that remove superoxide anion radicals and they have been identified in three domains of life: Bacteria, Archaea and Eucarya. SODs in Synechocystis sp. PCC 6803, Gloeobacter violaceus CCALA 979, and Geitlerinema sp. ZHR1A were investigated. We hypothesized that iron (FeSOD) and/or manganese (MnSOD) dominate as active forms in these cyanobacteria. Activity staining and three different spectroscopic methods of SOD activity bands excised from the gels were used to identify a suitable metal in the separated samples. FeSODs or enzymes belonging to the Fe-MnSOD superfamily were detected. The spectroscopic analyses showed that only Fe is present in the SOD activity bands. We found FeSOD in Synechocystis sp. PCC 6803 while two forms in G. violaceus and Geitlerinema sp. ZHR1A: FeSOD1 and FeSOD2 were present. However, no active Cu/ZnSODs were identified in G. violaceus and Geitlerinema sp. ZHR1A. We have shown that selected spectroscopic techniques can be complementary to the commonly used method of staining for SOD activity in a gel. Furthermore, the occurrence of active SODs in the cyanobacteria studied is also discussed in the context of SOD evolution in oxyphotrophs.