Effects of Boscalid on the Antioxidant Enzyme System of Adult Zebrafish (Danio rerio)

Abstract [Objective] This study was conducted to evaluate the ecotoxicity of boscalid to adult zebrafish （Danio rerio）. [Method] The activities of antioxidant enzymes superoxide dismutase （SOD）, catalase （CAT）, peroxidase （POD）, glutathione peroxidase （GPx）, as well as a non-enzymatic antioxidant malondialdehyde （MDA）, in the liver were measured 3, 7, 14 and 21 d post exposure （dpe） to 0.02 （1/100 of acute toxicity）, 0.036 （monitored concentration）, 0.08 （1/20 of acute toxicity）, 0.16 （1/10 of acute toxicity） and 0.32 mg/L （1/5 of acute toxicity） boscalid using a semi-static method. [Result] SOD, CAT, POD, GPx and MDA activity in the liver of zebrafish varied with boscalid concentration and exposure time. Boscalid significantly enhanced MDA content at 21 dpe. A significant upregulation of the activity of SOD, CAT, POD and GPx at 7 dpe was observed, suggesting that boscalid resulted in oxidative stress and lipid peroxidation. [Conclusion] These results show that these biomarkers are all appropriate for monitoring oxidative stress and the lipid peroxidation status of fish after exposure to boscalid. Key words Boscalid; Zebrafish; Antioxidant enzyme

Magnetic-optical dual functional Janus particles for the detection of metal ions assisted by machine learning

Functional polymer microspheres have broad application prospects in various fields,such as metal ion detection,adsorption,separation,and controlled drug release.However,integrating different functions in a single microsphere system is a significant challenge in this field.In this work,we prepared multicompartmental emulsion droplets utilizing microfluidic technology.Fe3O4 magnetic nanoparticles were added to one of the compartments of the emulsion droplets as functional particles,and Janus microspheres were obtained after curing.Fluorescent probes enter the two compartments of the Janus microspheres by diffusion.The fluorescence changes of the microspheres were observed in situ and captured through a fluorescence microscope.The images are processed by image recognition software and a Python program.The“fingerprint”of the detected metal ions is obtained by dimensionality reduction of the data through Principal Component Analysis.We employ different algorithms to build Machine Learning models for predicting the metal ion species and concentration.The variation of fluorescence intensity of the three fluorescent probes and the corresponding R,G,and B channel values and time are used as descriptors.The results show that the Random Forest,K-neighborhood(KNN),and Neural Network models demonstrated a better predicted effect with a variance(R2)greater than 0.9 and a smaller root mean square error;among them,the KNN model predicted the most accurate results.