Validation of qPCR from a crude extract for the rapid detection of white spot syndrome virus

Detection and prediction of pathogenic microorganisms in aquaculture (Zhejiang Province, China)

The detection and prediction of pathogenic microorganisms play a crucial role in the sustainable development of the aquaculture industry. Currently, researchers mainly focus on the prediction of water quality parameters such as dissolved oxygen for early warning. To provide early warning directly from the pathogenic source, this study proposes an innovative approach for the detection and prediction of pathogenic microorganisms based on yellow croaker aquaculture. Specifically, a method based on quantitative polymerase chain reaction (qPCR) is designed to detect the Cryptocaryon irritans (Cri) pathogenic microorganisms. Furthermore, we design a predictive combination model for small samples and high noise data to achieve early warning. After performing wavelet analysis to denoise the data, two data augmentation strategies are used to expand the dataset and then combined with the BP neural network (BPNN) to build the fusion prediction model. To ensure the stability of the detection method, we conduct repeatability and sensitivity tests on the designed qPCR detection technique. To verify the validity of the model, we compare the combined BPNN to long short-term memory (LSTM). The experimental results show that the qPCR method provides accurate quantitative measurement of Cri pathogenic microorganisms, and the combined model achieves a good level. The prediction model demonstrates higher accuracy in predicting Cri pathogenic microorganisms compared to the LSTM method, with evaluation indicators including mean absolute error (MAE), recall rate, and accuracy rate. Especially, the accuracy of early warning is increased by 54.02%.

Living Magnetotactic Microrobots Based on Bacteria with a Surface-Displayed CRISPR/Cas12a System for Penaeus Viruses Detection

Bacterial microrobots are an emerging living material in the field of diagnostics. However, it is an important challenge to make bacterial microrobots with both controlled motility and specific functions. Herein, magnetically driven diagnostic bacterial microrobots are prepared by standardized and modular synthetic biology methods. To ensure mobility, the Mms6 protein is displayed on the surface of bacteria and is exploited for magnetic biomineralization. This gives the bacterial microrobot the ability to cruise flexibly and rapidly with a magnetization intensity up to about 18.65 emu g-1. To achieve the diagnostic function, the Cas12a protein is displayed on the bacterial surface and is used for aquatic pathogen nucleic acid detection. This allows the bacterial microrobot to achieve sensitive, rapid, and accurate on-site nucleic acid detection, with detection limits of 8 copies μL-1 for decapod iridescent virus 1 (DIV1) and 7 copies μL-1 for white spot syndrome virus (WSSV). In particular, the diagnostic results based on the bacterial microrobots remained consistent with the gold standard test results when tested on shrimp tissue. This approach is a flexible and customizable strategy for building bacterial microrobots, providing a reliable and versatile solution for the design of bacterial microrobots.