Narrow Results

In this study, we have analyzed the effect a newly synthesized water-soluble alpha tetra-substituted zinc phthalocyanine (Pc) compound on superoxide dismutase (SOD), ascorbate peroxidase (APX) and glutathione reductase (GR) activities and biomass accumulation in the Arthrospira platensis-M2 strain to test whether this compound could be used as an algaecide or not. We found that lower concentrations (3 μg mL${}^{\mathrm{\text{-1}}}$ and 6 μg mL${}^{\mathrm{\text{-1}}})$ of Pc compound were not toxic to algae cells, as indicated by enduring biomass accumulation during the study (7 days). Higher Pc concentrations, however, were toxic and inhibited biomass accumulation. This inhibition appeared on the fourth day and persisted during the study. At higher Pc concentrations, SOD activity decreased significantly, but APX and GR activity were not affected. These results may show that Pc applications did not cause the accumulation of reactive oxygen species in Arthrospira platensis-M2 cells. Our result suggests that higher Pc concentrations did not cause oxidative stress but biomass accumulation inhibited, possibly due to some different toxicity mechanism(s), which should be carried out in the future studies. As a result, we may offer use of this compound as a means to keep under control algal populations in natural environments.

Over 200 million people worldwide are affected annually by schistosomiasis with debilitating socio-economic effects. Praziquantel remains the main chemotherapy against this neglected tropical disease but there are reports of drug resistance. Therefore, necessitating the need to identify potential biotherapeutic molecules. The study describes the first deep learning (DL)-based computational models for predicting inhibitors of Schistosoma mansoni Thioredoxin glutathione reductase (SmTGR), which is an essential protein for the survival of the helminths in the host. The state-of-the-art performance of DL in similar applications makes it ideal to deploy on bioactive datasets of the SmTGR drug target. Cost-sensitive deep neural network classifiers were trained using the binary classification approach. Based on the area under curve (AUC) of the receiver operating characteristic (ROC) Curve scores (86.3–86.5%), the five best models generated were able to classify inhibitors with high accuracy (85–90%). Additionally, the DL classifier outperformed random forest by far. This is a proof of concept that deep neural networks can efficiently and robustly classify active schistosomal molecules from inactive. The generated models could be used to screen large-scale compound libraries to prioritize potential inhibitors for experimental characterization.

The following sections are included:

• Introduction

• The Thioredoxin System

• The Glutaredoxin System

• Redox Regulation by Thioredoxin and Glutaredoxin

• Effects of Oxidants and Antioxidants

• Future Perspectives

• Acknowledgments

• References