A tunable fluorescent probe for superoxide anion detection during inflammation caused by Treponema pallidum.

Syphilis, caused by Treponema pallidum (T. pallidum), is associated with the oxidative stress due to its inflammation-like symptom, and detecting the reactive oxygen species (ROS) is crucial for monitoring the infectious process. Herein, we design and synthesize a perylene-based tunable fluorescent probe, PerqdOH, which can detect endogenous O2˙- during T. pallidum infection. The fluorescence peak shifted from 540 nm to 750 nm with increasing O2˙- levels. Besides, both decreased green fluorescence and enhanced red fluorescence could be observed simultaneously during the in vitro infection, providing the real-time monitoring of intracellular O2˙- caused by T. pallidum. Furthermore, the probe exhibited a remarkable signal in the treponemal lesions on the back of a rabbit model. Taken together, our synthesized PerqdOH holds great potential for application in clarifying the infectious process caused by T. pallidum in real time.

Corrigendum: Facial expression recognition method based on PSA-YOLO network.

[This corrects the article DOI: 10.3389/fnbot.2022.1057983.].

Facial expression recognition method based on PSA-YOLO network.

In order to improve the recognition speed and accuracy of face expression recognition, we propose a face expression recognition method based on PSA-YOLO (Pyramids Squeeze Attention-You Only Look Once). Based on CSPDarknet53, the Focus structure and pyramid compression channel attention mechanism are integrated, and the network depth reduction strategy is adopted to build a PSA-CSPDarknet-1 lightweight backbone network with small parameters and high accuracy, which improves the speed of face expression recognition. Secondly, in the neck of the network, a spatial pyramid convolutional pooling module is built, which enhances the spatial information extraction ability of deep feature maps with a very small computational cost, and uses the α-CIoU loss function as the bounding box loss function to improve the recognition accuracy of the network for targets under high IoU threshold and improve the accuracy of face expression recognition. The proposed method is validated on the JAFFE, CK+, and Cohn-Kanade datasets. The experimental results show that the running time of the proposed method and the comparison method is reduced from 1,800 to 200 ms, and the recognition accuracy is increased by 3.11, 2.58, and 3.91%, respectively, so the method proposed in this paper has good applicability.