Phosgene (COCl2) was once used as a classic suffocation poison and currently plays an essential role in industrial production. Due to its high toxicity, the problem of poisoning caused by leakage during production, storage, and use cannot be ignored. Phosgene mainly acts on the lungs, causing long-lasting respiratory depression, refractory pulmonary edema, and other related lung injuries, which may cause acute respiratory distress syndrome or even death in severe cases. Due to the high mortality, poor prognosis, and frequent sequelae, targeted therapies for phosgene exposure are needed. However, there is currently no specific antidote for phosgene poisoning. This paper reviews the literature on the mechanism and treatment strategies to explore new ideas for the treatment of phosgene poisoning.
Deep learning methods, and especially convolutional neural networks (CNNs), have made a considerable breakthrough in various fields of machine vision, basically by employing large-scale labeled databases. However, deep learning methods applied in finger-vein area are basically implemented on small-scale datasets, which are probably faced with challenges such as overfitting, susceptible to finger position, unstable performance on various datasets and son on. In this study, we present a lightweight and fully convolutional Generative Adversarial Network (GAN) architecture, which is named FCGAN, using preliminary batch normalization, and tightly-constrained loss function for implementing finger-vein image augmentation. In addition, we present a novel scheme FCGAN-CNN for finger-vein classification, which reveals that synthetic finger-vein images using FCGAN are capable of improving the property of CNN for finger-vein image classification. The experiment of sample augmentation shows that the training accuracy using FCGAN-augmented samples could go beyond 99%, which is higher than 96.34% obtained using only classic sample augmentation. Furthermore, the well-trained CNN is further evaluated on a totally different dataset, which indicates that the proposed scheme FCGAN-CNN is capable of improving the ability of CNN to extract deep features. We consider that the proposed method for sample augmentation could be extended to other biometric systems. INDEX TERMS Sample augmentation, convolutional neural networks, generative adversarial networks, finger-vein classification.
Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is an overactivated inflammatory response caused by direct or indirect injuries that destroy lung parenchymal cells and dramatically reduce lung function. Although some research progress has been made in recent years, the pathogenesis of ALI/ARDS remains unclear due to its heterogeneity and etiology. MicroRNAs (miRNAs), a type of small noncoding RNA, play a vital role in various diseases. In ALI/ARDS, miRNAs can regulate inflammatory and immune responses by targeting specific molecules. Regulation of miRNA expression can reduce damage and promote the recovery of ALI/ARDS. Consequently, miRNAs are considered as potential diagnostic indicators and therapeutic targets of ALI/ARDS. Given that inflammation plays an important role in the pathogenesis of ALI/ARDS, we review the miRNAs involved in the inflammatory process of ALI/ARDS to provide new ideas for the pathogenesis, clinical diagnosis, and treatment of ALI/ARDS.
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