RESUMEN
Uridine-disphosphate glucuronosyltransferase 1A9 (UGT1A9), an important detoxification and inactivation enzyme for toxicants, regulates the exposure level of environmental pollutants in the human body and induces various toxicological consequences. However, an effective tool for high-throughput monitoring of UGT1A9 function under exposure to environmental pollutants is still lacking. In this study, 1,3-dichloro-7-hydroxy-9,9-dimethylacridin-2(9H)-one (DDAO) was found to exhibit excellent specificity and high affinity towards human UGT1A9. Remarkable changes in absorption and fluorescence signals after reacting with UGT1A9 were observed, due to the intramolecular charge transfer (ICT) mechanism. Importantly, DDAO was successfully applied to monitor the biological functions of UGT1A9 in response to environmental pollutant exposure not only in microsome samples, but also in living cells by using a high-throughput screening method. Meanwhile, the identified pollutants that disturb UGT1A9 functions were found to significantly influence the exposure level and retention time of bisphenol S/bisphenol A in living cells. Furthermore, the molecular mechanism underlying the inhibition of UGT1A9 by these pollutant-derived disruptors was elucidated by molecular docking and molecular dynamics simulations. Collectively, a fluorescent probe to characterize the responses of UGT1A9 towards environmental pollutants was developed, which was beneficial for elucidating the health hazards of environmental pollutants from a new perspective.
Asunto(s)
Dimetilaminas , Contaminantes Ambientales , Glucuronosiltransferasa , Humanos , Colorantes Fluorescentes , Uridina , Simulación del Acoplamiento MolecularRESUMEN
In view of the differences in appearance and the complex backgrounds of crop diseases, automatic identification of field diseases is an extremely challenging topic in smart agriculture. To address this challenge, a popular approach is to design a Deep Convolutional Neural Network (DCNN) model that extracts visual disease features in the images and then identifies the diseases based on the extracted features. This approach performs well under simple background conditions, but has low accuracy and poor robustness under complex backgrounds. In this paper, an end-to-end disease identification model composed of a disease-spot region detector and a disease classifier (YOLOv5s + BiCMT) was proposed. Specifically, the YOLOv5s network was used to detect the disease-spot regions so as to provide a regional attention mechanism to facilitate the disease identification task of the classifier. For the classifier, a Bidirectional Cross-Modal Transformer (BiCMT) model combining the image and text modal information was constructed, which utilizes the correlation and complementarity between the features of the two modalities to achieve the fusion and recognition of disease features. Meanwhile, the problem of inconsistent lengths among different modal data sequences was solved. Eventually, the YOLOv5s + BiCMT model achieved the optimal results on a small dataset. Its Accuracy, Precision, Sensitivity, and Specificity reached 99.23, 97.37, 97.54, and 99.54%, respectively. This paper proves that the bidirectional cross-modal feature fusion by combining disease images and texts is an effective method to identify vegetable diseases in field environments.