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Bluetongue disease (BT) is a viral disease that can be introduced through imported animals and animal products, affecting local animal husbandry. In this study, the spatial and temporal patterns of BT outbreaks (outbreak: a BT infection in cattle, sheep, or goats on a farm, involving at least one infected animal) in France were analyzed and the risk of introducing bluetongue virus (BTV) into countries through trade was assessed. A spatiotemporal analysis of BT reported during the study period (2015–2018) showed that there were clustered outbreaks of BT in France in 2016 and 2017, with outbreaks concentrated from August to December. The outbreak moved eastward from the center of mainland France to surrounding countries. A semi-quantitative risk analysis framework was established by combining the likelihood assessment and consequence analysis of introducing BTV into trading countries through trade. Exemplified by China, the research showed that in the analysis of the likelihood of BTV from France being introduced into trading countries through live cattle trade, China imports a large number of live cattle, bringing high risks. The likelihood of introducing bovine semen into trading countries was similar to that of live cattle, but the harm caused by the trade in live cattle was higher than that caused by the trade in bovine semen. This risk analysis framework can provide a reference for other countries to quickly assess the risk of bluetongue transmission in import and export trade.

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases and manifests as progressive memory loss and cognitive dysfunction. Neuroinflammation plays an important role in the development of Alzheimer’s disease and anti-inflammatory drugs reduce the risk of the disease. However, the immune microenvironment in the brains of patients with Alzheimer’s disease remains unclear, and the mechanisms by which anti-inflammatory drugs improve Alzheimer’s disease have not been clearly elucidated. This study aimed to provide an overview of the immune cell composition in the entorhinal cortex of patients with Alzheimer’s disease based on the transcriptomes and signature genes of different immune cells and to explore potential therapeutic targets based on the relevance of drug targets. Transcriptomics data from the entorhinal cortex tissue, derived from GSE118553, were used to support our study. We compared the immune-related differentially expressed genes (irDEGs) between patients and controls by using the limma R package. The difference in immune cell composition between patients and controls was detected via the xCell algorithm based on the marker genes in immune cells. The correlation between marker genes and immune cells and the interaction between genes and drug targets were evaluated to explore potential therapeutic target genes and drugs. There were 81 irDEGs between patients and controls that participated in several immune-related pathways. xCell analysis showed that most lymphocyte scores decreased in Alzheimer’s disease, including CD4+ Tc, CD4+ Te, Th1, natural killer (NK), natural killer T (NKT), pro-B cells, eosinophils, and regulatory T cells, except for Th2 cells. In contrast, most myeloid cell scores increased in patients, except in dendritic cells. They included basophils, mast cells, plasma cells, and macrophages. Correlation analysis suggested that 37 genes were associated with these cells involved in innate immunity, of which eight genes were drug targets. Taken together, these results delineate the profile of the immune components of the entorhinal cortex in Alzheimer’s diseases, providing a new perspective on the development and treatment of Alzheimer’s disease.

Peste des Petits Ruminants (PPR) is an acute and highly contagious transboundary disease caused by the PPR virus (PPRV). The virus infects goats, sheep and some wild relatives of small domestic ruminants, such as antelopes. PPR is listed by the World Organization for Animal Health as an animal disease that must be reported promptly. In this paper, PPR outbreak data combined with WorldClim database meteorological data were used to build a PPR prediction model. Using feature selection methods, eight sets of features were selected: bio3, bio10, bio15, bio18, prec7, prec8, prec12, and alt for modeling. Then different machine learning algorithms were used to build models, among which the random forest (RF) algorithm was found to have the best modeling effect. The ACC value of prediction accuracy for the model on the training set can reach 99.10%, while the ACC on the test sets was 99.10%. Therefore, RF algorithms and eight features were finally selected to build the model in order to build the online prediction system. In addition, we adopt single-factor modeling and correlation analysis of modeling variables to explore the impact of each variable on modeling results. It was found that bio18 (the warmest quarterly precipitation), prec7 (the precipitation in July), and prec8 (the precipitation in August) contributed significantly to the model, and the outbreak of the epidemic may have an important relationship with precipitation. Eventually, we used the final qualitative prediction model to establish a global online prediction system for the PPR epidemic.

Foot-and-mouth disease (FMD) is a highly contagious disease of livestock and seriously affects the development of animal husbandry. It is necessary to defend the spread of FMD. To explore the distribution characteristics and transmission of FMD between 2010 and 2017 in China, Global Moran's I test and Getis-Ord Gi index were used to analyze the spatial cluster. A space-time permutation scan statistic was applied to analyze the spatio-temporal pattern. GIS-based method was employed to create a map representing the distribution pattern, directional trend, and hotspots for each outbreak. The number of cases was defined as the number of animals with FMD for the above analysis. We also constructed a phylogenetic tree to compare the homology and variation of FMD virus (FMDV) to provide a clue for the potential development of an effective vaccine. The results indicated that the FMD outbreaks in China had obvious time patterns and clusters in space and space-time, with the outbreaks concentrated in the first half of each year. The outbreaks of FMD decreased each year from 2010 with an obvious downward trend of hotspots. Spatial analysis revealed that the distribution of FMD outbreaks in 2010, 2015, and 2017 exhibited a clustered pattern. Space-time scanning revealed that the spatio-temporal clusters were centered in Guangdong, Tibet and the junction of Wuhan, Jiangxi, Anhui. Comparison of the spatial analysis and space-time analysis of FMD outbreaks revealed that Guangdong was the same cluster of the two in 2010. In addition, the directional trend analysis indicated that the FMD transmission was oriented northwest-southeast. The findings demonstrated that FMDV in China can be divided into three pedigrees and the homology of these strains is very high while comparing the first FMDV strain with the others. The data provide a basis for the effective monitoring and prevention of FMD, and for the development of an FMD vaccine in China.

ObjectiveTo describe and analyze the clinical characteristics, laboratory data, management, and outcome of patients with onconeural antibody-associated disorders (OAAD) and identify predictors for poor outcome.MethodsThis was a retrospective review of all patients with potential OAAD, who were hospitalized in Jinan General Hospital between September 2009 and July 2017. We clarified the diagnosis, collected comprehensive information and categorized patients into three groups: paraneoplastic neurological disorders (PNDs), autoimmune encephalitis (AE), and possible OAAD. Within the three groups, we analyzed a range of clinical and laboratory parameters and used univariate and multivariate regression analysis to identify predictors for poor outcome [modified Rankin Scale (mRS) = 3–6].ResultsFrom 158 patients, we identified 70 who fulfilled the criteria for OAAD, including 44 men (62.9%) and 26 women (37.1%). There were 38 patients (54.3%) in the PNDs group, 14 patients (20%) in the AE group, and 18 patients (25.7%) in the possible OAAD group. After the last follow-up, 14 (36.8%), 9 (64.2%), and 12 (66.7%) had a good outcome (mRS = 0–2). However, 6 (15.8%), 2 (14.3%), and 3 (16.7%) died, respectively. Univariate analysis showed that duration prior to the hospital (p = 0.0224) and urinary incontinence/retention (p = 0.0043) were associated with poor outcome (mRS = 3–6). After multivariate regression analysis, urinary incontinence/retention (p = 0.0388) and an immunocompromised state (p = 0.0247) remained as significant factors for poor outcome.ConclusionUrinary incontinence/retention and an immunocompromised state represent significant predictors of a worse prognosis for patients with OAAD. By contrast, cerebrospinal fluid analysis showed that serum autoantibodies and tumor markers, the function of crucial organs, electrophysiology, and radiological findings were not associated with a poor outcome.