Background: A proliferation-inducing ligand (APRIL) is a tumor-necrosis factor (TNF) family member and is a novel cytokine crucial in sustaining lymphocytic leukemia B cell survival and proliferation. However, its role in gastric cancer (GC) remains unclear. In this study, we investigated the expression pattern and prognostic role of APRIL in GC. Methods: Expression of APRIL was assessed by immunohistochemistry and real-time PCR. Prognostic role of APRIL expression was evaluated. We also discovered the effect of APRIL on chemo-resistance in GC cells and the underlying mechanisms. Results: APRIL mRNA levels were significantly increased in GC tissues compared with adjacent tissues and high expression levels of APRIL in tumor cells significantly correlated with poor overall survival in patients receiving cisplatin adjuvant treatment. Overexpression of APRIL in AGS cells significantly attenuated the therapeutic efficacy of cisplatin in vitro and in vivo. In contrast, silence of APRIL in SGC7901 cells enhanced cisplatin-induced tumor suppression. Our data further revealed that the canonical NF-κB pathway was involved in APRIL-mediated chemo-resistance. In addition, expression of APRIL was regulated by miR-145 in GC cells. Conclusion: APRIL is a novel clinical chemo-resistance biomarker for gastric cancer and might be a promising therapeutic target for GC patients.

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Left-behind humans inside the car or bus have caused a lot of accidents, so it is essential to detect the humans in vehicle. Current human detection methods rely on wearable devices, oxygen sensors, and special seat designs in vehicles, but those sensors cannot adapt to ever-changing environments. To solve those problems and especially to improve passengers’ safety on the bus, we propose a method to accomplishing human detection by fusion vision and microwave radar information in various environments in vehicle. For vision information, we use different networks to extract human and human face features, and fusion of the detection results in different models to improve human detection accuracy. The human detection model is MobileNet-V2, and the human face detection model is MTCNN. A new matching schedule and tracking objects management rule based on the Kernelized Correlation Filter tracker are designed to track the human and human face detection boxes. The microwave radar information is used to detect moving objects. Finally, the fusion vision and microwave radar detection results are implemented. Experiments show that our method has improved the human detection accuracy in vehicle, and this method can be used for detection of left-behind children on the school bus.

The driver is one of the most important factors in the safety of the transportation system. The driver’s perceptual characteristics are closely related to driving behavior, while electroencephalogram (EEG) as the gold standard for evaluating human perception is non-deceptive. It is essential to study driving characteristics by analyzing the driver’s brain activity pattern, effectively acquiring driver perceptual characteristics, creating a direct connection between the driver’s brain and external devices, and realizing information interchange. This paper first introduces the theories related to EEG, then reviews the applications of EEG in scenarios such as fatigue driving, distracted driving, and emotional driving. The limitations of existing research have been identified and the prospect of EEG application in future brain-computer interface automotive assisted driving systems have been proposed. This review provides guidance for researchers to use EEG to improve driving safety. It also offers valuable suggestions for future research.