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Epizootic hemorrhagic disease (EHD) is an infectious viral disease caused by epizootic hemorrhagic disease virus (EHDV) and EHDV frequently circulates in wild and domestic ruminants. Sporadic outbreaks of EHD have caused thousands of deaths and stillbirths on cattle farms. However, not much is known about the circulating status of EHDV in Guangdong, southern China. To estimate the seroprevalence of EHDV in Guangdong province, 2886 cattle serum samples were collected from 2013 to 2017 and tested for antibodies against EHDV using a competitive ELISA. The overall seroprevalence of EHDV reached 57.87% and was highest in autumn (75.34%). A subset of positive samples were serotyped by a serum neutralization test, showing that EHDV serotypes 1 and 5–8 were circulating in Guangdong. In addition, EHDV prevalence always peaked in autumn, while eastern Guangdong had the highest EHDV seropositivity over the five-year period, displaying apparent temporal–spatial distribution of EHDV prevalence. A binary logistic model analysis indicated a significant association between cattle with BTV infections and seroprevalence of EHDV (OR = 1.70, p < 0.001). The co-infection of different serotypes of EHDV and BTV raises a high risk of potential genomic reassortment and is likely to pose a significant threat to cattle, thus urging more surveillance to monitor their circulating dynamics in China. [ABSTRACT FROM AUTHOR]

Background: Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies.Methods: This multicentre, case-control, diagnostic study was done in six hospitals of different tiers (ie, municipal, provincial, and national) in China. The images of consecutive participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals. All patients with upper gastrointestinal cancer lesions (including oesophageal cancer and gastric cancer) that were histologically proven malignancies were eligible for this study. Only images with standard white light were deemed eligible. The images from Sun Yat-sen University Cancer Center were randomly assigned (8:1:1) to the training and intrinsic verification datasets for developing GRAIDS, and the internal validation dataset for evaluating the performance of GRAIDS. Its diagnostic performance was evaluated using an internal and prospective validation set from Sun Yat-sen University Cancer Center (a national hospital) and additional external validation sets from five primary care hospitals. The performance of GRAIDS was also compared with endoscopists with three degrees of expertise: expert, competent, and trainee. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of GRAIDS and endoscopists for the identification of cancerous lesions were evaluated by calculating the 95% CIs using the Clopper-Pearson method.Findings: 1 036 496 endoscopy images from 84 424 individuals were used to develop and test GRAIDS. The diagnostic accuracy in identifying upper gastrointestinal cancers was 0·955 (95% CI 0·952-0·957) in the internal validation set, 0·927 (0·925-0·929) in the prospective set, and ranged from 0·915 (0·913-0·917) to 0·977 (0·977-0·978) in the five external validation sets. GRAIDS achieved diagnostic sensitivity similar to that of the expert endoscopist (0·942 [95% CI 0·924-0·957] vs 0·945 [0·927-0·959]; p=0·692) and superior sensitivity compared with competent (0·858 [0·832-0·880], p<0·0001) and trainee (0·722 [0·691-0·752], p<0·0001) endoscopists. The positive predictive value was 0·814 (95% CI 0·788-0·838) for GRAIDS, 0·932 (0·913-0·948) for the expert endoscopist, 0·974 (0·960-0·984) for the competent endoscopist, and 0·824 (0·795-0·850) for the trainee endoscopist. The negative predictive value was 0·978 (95% CI 0·971-0·984) for GRAIDS, 0·980 (0·974-0·985) for the expert endoscopist, 0·951 (0·942-0·959) for the competent endoscopist, and 0·904 (0·893-0·916) for the trainee endoscopist.Interpretation: GRAIDS achieved high diagnostic accuracy in detecting upper gastrointestinal cancers, with sensitivity similar to that of expert endoscopists and was superior to that of non-expert endoscopists. This system could assist community-based hospitals in improving their effectiveness in upper gastrointestinal cancer diagnoses.Funding: The National Key R&D Program of China, the Natural Science Foundation of Guangdong Province, the Science and Technology Program of Guangdong, the Science and Technology Program of Guangzhou, and the Fundamental Research Funds for the Central Universities. [ABSTRACT FROM AUTHOR]

Enzootic nasal tumor virus (ENTV) has two types, ENTV-1 in sheep and ENTV-2 in goats, respectively. In China, the incidence of ENTV-2 related diseases has increased year by year. In this study, we reported an outbreak of ENTV-2 in a commercial goat farm in Qingyuan city, Guangdong province, southern China. A full-length genome of ENTV-2 (designated GDQY2017), with 7479 base pairs, was sequenced. Although GDQY2017 shared the highest nucleotide identity with a Chinese ENTV-2 isolate (ENTV-2CHN4, GenBank accession number KU258873), it possesses distinct genome characteristics undescribed, including a non-continuous 21-nucleotide insertion in the gag gene and a non-continuous 12-nucleotide deletion in the env gene. Notably, most of these indel nucleotide sequences were originated from a Chinese jaagsiekte sheep retrovirus (JSRV) isolate (GenBank accession number DQ838494). In the gag and env genes, GDQY2017 was phylogenetically related to those Chinese ENTV-2 isolates and a Chinese JSRV isolate (DQ838494). For GDQY2017-like viruses, more surveillance work should be made to explain their pathogenicity in goat herds. To our knowledge, this study represents the first to demonstrate the circulating pattern of ENTV-2 in Guangdong province, China, which will help to better understand the epidemiology and genetic diversity of ENTV-2. [ABSTRACT FROM AUTHOR]