BackgroundIn 2009, severe fever with thrombocytopenia syndrome virus (SFTSV) was identified as a novel member of the genus phlebovirus in the Bunyaviridae family in China. The detailed clinical features of cases with SFTSV infection have not been well described, and the risk factors for severity among patients and fatality among severe patients remain to be determined.Methodology/Principal FindingsClinical and laboratory features of 115 hospitalized patients with SFTSV infection during the period from June 2010 to December 2011 in Northeast China were retrospectively reviewed. We assessed the risk factors associated with severity in confirmed cases and fatality in severe cases by multivariate analysis. One hundred and three (89.6%) of 115 patients presented with multiple organ dysfunction, and 22 (19.1%) of 115 proceeded to the stage of life threatening multiple organ failure. Of the 115 patients, 14 fatalities (12.2%) were reported. Multivariate analysis demonstrated that the independent predictors of risk for severity were: albumin ≤30 g/l (OR, 8.09; 95% CI, 2.58-25.32), APTT ≥ 66 seconds (OR, 14.28; 95% CI, 3.28-62.24), sodium ≤130 mmol/l (OR, 5.44; 95% CI, 1.38-21.40), and presence of neurological manifestations (OR, 7.70; 95% CI, 1.91-31.12). Among patients with severe disease, presence of acute lung injury/acute respiratory distress syndrome (HR, 4.59; 95% CI, 1.48–14.19) and disseminated intravascular coagulation (HR, 4.24; 95% CI, 1.38–13.03) were independently associated with fatality.Conclusions/SignificanceSFTSV infection may present with more severe symptoms and laboratory abnormalities than hitherto reported. Due to infection with a novel bunyavirus, the patients may sufferer multiple organ dysfunction and die of multiple organ failure. In the clinical assessment of any case of SFTS, independent factors relating to prognosis need to be taken into account by clinicians.

BackgroundSevere fever with thrombocytopenia syndrome virus (SFTSV), which can cause hemorrhagic fever–like illness, is a newly discovered bunyavirus in China. The pathogenesis of SFTSV infection is poorly understood. However, it has been suggested that immune mechanisms, including cytokines and chemokines, play an important role in disease pathogenesis. In the present study, we investigated host cytokine and chemokine profiles in serum samples of patients with SFTSV infection from Northeast China and explored a possible correlation between cytokine levels and disease severity.Methods and Principal FindingsAcute phase serum samples from 40 patients, diagnosed with SFTSV infection were included. Patients were divided into two groups – severe or non-severe – based on disease severity. Levels of tumor necrosis factor (TNF)-α, transforming growth factor (TGF)-β, interleukin-6, interferon (IFN)-γ, IFN- γ-induced protein (IP)-10 and RANTES were measured in the serum samples with commercial ELISAs. Statistical analysis showed that increases in TNF-α, IP-10 and IFN-γ were associated with disease severity.ConclusionsWe suggest that a cytokine-mediated inflammatory response, characterized by cytokine and chemokine production imbalance, might be in part responsible for the disease progression of patients with SFTSV infection.

China is the largest global consumer of antimicrobials and improving surveillance methods could help to reduce antimicrobial resistance (AMR) spread. Here we report the surveillance of ten large-scale chicken farms and four connected abattoirs in three Chinese provinces over 2.5 years. Using a data mining approach based on machine learning, we analysed 461 microbiomes from birds, carcasses and environments, identifying 145 potentially mobile antibiotic resistance genes (ARGs) shared between chickens and environments across all farms. A core set of 233 ARGs and 186 microbial species extracted from the chicken gut microbiome correlated with the AMR profiles of Escherichia coli colonizing the same gut, including Arcobacter, Acinetobacter and Sphingobacterium, clinically relevant for humans, and 38 clinically relevant ARGs. Temperature and humidity in the barns were also correlated with ARG presence. We reveal an intricate network of correlations between environments, microbial communities and AMR, suggesting multiple routes to improving AMR surveillance in livestock production.

The use of antimicrobials in livestock production is associated with the rise of antimicrobial resistance (AMR). China is the largest consumer of antimicrobials and improving AMR surveillance methods may help inform intervention. Here, we report the surveillance of ten large-scale chicken farms and four connected abattoirs from three Chinese provinces, over 2.5 years. By using a bespoke data-mining approach based on machine learning, we analysed microbiomes and resistomes from birds, carcasses and environments. We found that a core subset of the chicken gut resistome and microbiome, featuring clinically relevant bacteria and antibiotic resistance genes correlates with AMR profiles of Escherichia coli colonizing the gut. This core is itself influenced by environmental temperature and humidity, contains clinically relevant mobile ARGs shared by chickens and environments, and correlates with antimicrobial usage. Our findings indicate a viable route to optimize AMR surveillance in livestock production.

Sharing among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 E. coli and S. enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using novel data-mining methods. Most isolates within same hosts possessed same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Machine learning revealed known and novel AMR-associated mutations and genes underlying resistance to 26 antimicrobials and primarily associated with resistance in Escherichia coli and susceptibility in Salmonella enterica. Many of these genes were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs.