Real-time COVID-19 detection over chest x-ray images in edge computing.

Severe Coronavirus Disease 2019 (COVID-19) has been a global pandemic which provokes massive devastation to the society, economy, and culture since January 2020. The pandemic demonstrates the inefficiency of superannuated manual detection approaches and inspires novel approaches that detect COVID-19 by classifying chest x-ray (CXR) images with deep learning technology. Although a wide range of researches about bran-new COVID-19 detection methods that classify CXR images with centralized convolutional neural network (CNN) models have been proposed, the latency, privacy, and cost of information transmission between the data resources and the centralized data center will make the detection inefficient. Hence, in this article, a COVID-19 detection scheme via CXR images classification with a lightweight CNN model called MobileNet in edge computing is proposed to alleviate the computing pressure of centralized data center and ameliorate detection efficiency. Specifically, the general framework is introduced first to manifest the overall arrangement of the computing and information services ecosystem. Then, an unsupervised model DCGAN is employed to make up for the small scale of data set. Moreover, the implementation of the MobileNet for CXR images classification is presented at great length. The specific distribution strategy of MobileNet models is followed. The extensive evaluations of the experiments demonstrate the efficiency and accuracy of the proposed scheme for detecting COVID-19 over CXR images in edge computing.

Aloe polymeric acemannan inhibits the cytokine storm in mouse pneumonia models by modulating macrophage metabolism.

The cytokine storm is highly associated with inflammatory-type disease severity and patients' survival. Plant polysaccharides, the main natural phytomedicine source, have a great potential to be an effective drug to treat cytokine storm. Herein we found that a polymeric acemannan (ABPA1) isolated from Aloe Vera Barbadensis extract C (AVBEC) exerted prominent inhibitory effects on inflammation-induced cytokine storm. The results displayed that ABPA1 effectively suppressed LPS-induced proinflammatory cytokines release in vitro. Moreover, ABPA1 treatment alleviated the cytokine storm and tissue damage in LPS- and IAV-induced mouse pneumonia models, and altered the phenotypic balance of macrophages in lung tissues. Functionally, ABPA1 enhanced macrophage M2 polarization and phagocytosis in RAW264.7 cells and inhibited LPS-induced M1 polarization. Mechanistically, ABPA1 enhanced mitochondrial metabolism and OXPHOS through activated PI3K/Akt/GSK-3ß signalling pathway. Overall, our findings suggest that ABPA1 may modulate macrophage activation and mitochondrial metabolism by targeting PI3K/Akt/GSK-3ß signalling pathway, thereby alleviating cytokine storm and inflammation.

Dynamic pyrolytic reaction mechanisms, pathways, and products of medical masks and infusion tubes.

Given the COVID-19 epidemic, the quantity of hazardous medical wastes has risen unprecedentedly. This study characterized and verified the pyrolysis mechanisms and volatiles products of medical mask belts (MB), mask faces (MF), and infusion tubes (IT) via thermogravimetric, infrared spectroscopy, thermogravimetric-Fourier transform infrared spectroscopy, and pyrolysis-gas chromatography/mass spectrometry analyses. Iso-conversional methods were employed to estimate activation energy, while the best-fit artificial neural network was adopted for the multi-objective optimization. MB and MF started their thermal weight losses at 375.8 °C and 414.7 °C, respectively, while IT started to degrade at 227.3 °C. The average activation energies were estimated at 171.77, 232.79, 105.14, and 205.76 kJ/mol for MB, MF, and the first and second IT stages, respectively. Nucleation growth for MF and MB and geometrical contraction for IT best described the pyrolysis behaviors. Their main gaseous products were classified, with a further proposal of their initial cracking mechanisms and secondary reaction pathways.