Identification and genetic characterization of a recently identified enterovirus C116 in China.

Enterovirus C116 (EV-C116) is a new member of the enterovirus C group which is closely associated with several infectious diseases. Although sporadic studies have detected EV-C116 in clinical samples worldwide, there is currently limited information available. In this study, two EV-C-positive fecal specimens were detected in apparently healthy children, which harbored low abundance, through meta-transcriptome sequencing. Based on the prototypes of several EV-Cs, two lineages were observed. Lineage 1 included many types that could not cause EV-like cytopathic effect in cell culture. Three genogroups of EV-C116 were divided in the maximum likelihood tree, and the two strains in this study (XZ2 and XZ113) formed two different lineages, suggesting that EV-C116 still diffuses worldwide. Obvious inter-type recombination events were observed in the XZ2 strain, with CVA22 identified as a minor donor. However, another strain (XZ113) underwent different recombination situations, highlighting the importance of recombination in the formation of EV-Cs biodiversity. The EV-C116 strains could propagate in rhabdomyosarcoma cell cultures at low titer; however, EV-like cytopathic effects were not observed. HEp-2, L20B, VERO, and 293T cell lines did not provide an appropriate environment for EV-C116 growth. These results challenge the traditional recognition of the uncultured nature of EV-C116 strains and explain the difficulty of clinical detection.

A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network.

Objectives: Hand, foot and mouth disease (HFMD) is a widespread infectious disease that causes a significant disease burden on society. To achieve early intervention and to prevent outbreaks of disease, we propose a novel warning model that can accurately predict the incidence of HFMD. Methods: We propose a spatial-temporal graph convolutional network (STGCN) that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019. The 2011-2018 data served as the training and verification set, while data from 2019 served as the prediction set. Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error. Results: As the first application using a STGCN for disease forecasting, we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level, especially for cities of significant concern. Conclusions: This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance, which may significantly reduce the morbidity associated with HFMD in the future.

Nanofibers Self-assembled from Structural Complementary Borono-decapeptides.

A series of structural complementary decapeptides with phenyl boronic acid tails or borono-decapeptides (BPs) were designed and synthesized for supramolecular self-assembly. After dissolving these borono-decapeptides in deionized (DI) water, well-defined nanofibers were formed in BP1 (B(OH)(2) VEKELVKEKL-OH) and BP3 (B(OH)(2) AELELARARL-OH). It was found that the self-assembled borono-decapeptide BP1 and BP3 have a parallel ß-sheet conformation in the formed nanofibers. The strategy demonstrated here shows a great prospect in preparation of well-ordered nanofibers via rationally designing the molecular structures of peptides.