Human Anti-H5N1 Influenza A Virus HA monoclonal antibody, clone GME30

Influenza surveillance is important for disease control and should consider possible coinfection with different viruses, which can be associated with disease severity. This study analyzed 34 459 patients with respiratory infection from 2009 to 2018, of whom 8011 were positive for influenza A virus (IAV) or influenza B virus (IBV). We found 18 cases of dual influenza virus infection, including coinfection with 2009 pandemic influenza A(H1N1) virus (A[H1N1]pdm09) and influenza A(H3N2) virus (1 case), A(H1N1) pdm09 and IBV (6 cases), A(H3N2) and IBV (8 cases), and nonsubtyped IAV and IBV (3 cases); and 1 case of triple infection with A(H3N2), A(H1N1)pdm09, and IBV. Compared with 76 monoinfected patients, coinfection was significantly associated with cardiopathy and death. Besides demographic characteristics and clinical symptoms, we assessed vaccination status, antiviral treatment, timeliness of antiviral use, hospitalization, and intensive care unit admission, but no significant differences were found between coinfected and monoinfected cases. Our findings indicate that influenza virus coinfection occurs more often than previously reported and that it can lead to a worse disease outcome.

Different bioinformatic and data-mining approaches have been used for the analysis of proteins. Here, we describe a novel, robust, and reliable approach for comparative analysis of a large number of proteins by combining Image Processing Techniques and Convolutional Deep Neural Network (IPT-CNN). As proof of principle, we used IPT-CNN to predict different subtypes of Influenza A virus (IAV). Over 8000 sequences of surface proteins haemagglutinin (HA) and neuraminidase (NA) from different IAV subtypes were used to create polynomial or binary vector datasets. The datasets were then converted into binary images. Analysis of these images enabled the classification of IAV subtypes with 100% accuracy and, compared to non-image-based approaches, within a shorter time frame. The proteome-based IPT-CNN approach described here may be used for analysis and proteome-based classification of other proteins.