Background Glioma is a common brain tumor with a high mortality rate. A small population of cells expressing stem-like cell markers in glioma contributes to drug resistance and tumor recurrence. Methods Porous silicon nanoparticles (PSi NPs) as photothermal therapy (PTT) agents loaded with TMZ (TMZ/PSi NPs), was combined with hyperbaric oxygen (HBO) therapy in vitro and in vivo. To further investigate underlying mechanism, we detected the expression of stem-like cell markers and hypoxia related molecules in vitro and in vivo after treatment of TMZ/PSi NPs in combination with PTT and HBO. Results NCH-421K and C6 cells were more sensitive to the combination treatment. Moreover, the expression of stem-like cell markers and hypoxia related molecules were decreased after combination treatment. The in vivo results were in line with in vitro. The combination treatment presents significant antitumor effects in mice bearing C6 tumor compared with the treatment of TMZ, PTT or TMZ/PSi NPs only. Conclusion These results suggested the TMZ/PSi NPs combined with HBO and PTT could be a potential therapeutic strategy for glioma. Electronic supplementary material The online version of this article (10.1186/s12951-019-0483-1) contains supplementary material, which is available to authorized users.

Deep learning (DL) methods have achieved excellent performance in the task of single image rain removal, however, there are still some challenges, such as artifact remnant, background over-smooth, and more and more complex and heavy-weight network architecture. Due to too heavy-weight network to suit outdoor detection devices or mobile devices, therefore, we propose a light-weight single image deraining algorithm incorporating visual attention saliency mechanisms (LDVS). The network consists of dilation convolution module, the convolutional block attention module (CBAM), and gated recursive unit module. Specifically, rain steaks feature maps are extracted by the combinations of dilated convolution with CBAM, which also facilitates accurate localisation of rain steak location, and then the three gated recursive units is cascaded to remove steaks stage by stage. The dilated convolution module and CBAM are used to reduce network's weight size and retain the rain removal result, thus our LDVS method belongs to the lightweight with only 50703 parameters. Extensive experiments on synthetic and real-world datasets have demonstrated that our method outperforms the baseline both under qualitative and quantitative analysis. Under the same rain removal result, our method is less time cost and less burden.

Messenger RNA (mRNA) has emerged as a new and efficient agent for the treatment of various diseases. The success of lipid nanoparticles-mRNA against the novel coronavirus (SARS-CoV-2) pneumonia epidemic has...

The fine identification of vehicle color can assist in criminal investigation or intelligent traffic management law enforcement. Since almost all vehicle-color datasets that are used to train models are collected in good weather, the existing vehicle-color recognition algorithms typically show poor performance for outdoor visual tasks. In this paper we construct a new RainVehicleColor-24 dataset by rain-image rendering using PS technology and a SyRaGAN algorithm based on the VehicleColor-24 dataset. The dataset contains a total of 40,300 rain images with 125 different rain patterns, which can be used to train deep neural networks for specific vehicle-color recognition tasks. Experiments show that the vehicle-color recognition algorithms trained on the new dataset RainVehicleColor-24 improve accuracy to around 72% and 90% on rainy and sunny days, respectively. The code is available at humingdi2005@github.com.