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Insects act as vectors to carry a wide range of bacteria and viruses that can cause multiple vector-borne diseases in humans. Diseases such as dengue fever, epidemic encephalitis B, and epidemic typhus, which pose serious risks to humans, can be transmitted by insects. Due to the absence of effective vaccines for most arbovirus, insect control was the main strategy for vector-borne diseases control. However, the rise of drug resistance in the vectors brings a great challenge to the prevention and control of vector-borne diseases. Therefore, finding an eco-friendly method for vector control is essential to combat vector-borne diseases. Nanomaterials with the ability to resist insects and deliver drugs offer new opportunities to increase agent efficacy compared with traditional agents, and the application of nanoagents has expanded the field of vector-borne disease control. Up to now, the reviews of nanomaterials mainly focus on biomedicines, and the control of insect-borne diseases has always been a neglected field. In this study, we analyzed 425 works of the literature about different nanoparticles applied on vectors in PubMed around keywords, such as"nanoparticles against insect," "NPs against insect," and "metal nanoparticles against insect." Through these articles, we focus on the application and development of nanoparticles (NPs) for vector control, discussing the lethal mechanism of NPs to vectors, which can explore the prospect of applying nanotechnology in the prevention and control of vectors. [ABSTRACT FROM AUTHOR]

Background: Anaplasma spp., causative agents of anaplasmosis, pose significant a threat to public health and economic losses in livestock farming. Co-infections/co-existence of various Anaplasma spp. may facilitate pathogen interactions and the emergence of novel variants, represent potential dangers to public health and economic losses from livestock farming, and raise challenges of detection and diagnosis. The information regarding co-infection/co-existence of Anaplasma in their vector ticks and wild animals is limited and needs urgent investigation. Methods: Wild hedgehogs and ticks from hedgehogs and cattle were collected from Jiangsu province, Eastern China, and DNA was extracted from hedgehog organs and tick homogenates. Various genera of species-specific polymerase chain reaction (PCR) or nested PCR amplifications targeting 16S ribosomal RNA (rrs), msp4 , or groEL gene coupled with sequencing were conducted to identify Anaplasma spp. Results: Anaplasma phagocytophilum (1, 0.6%), A. marginale (2, 1.2%), A. platys variants xyn10pt-1 (13, 7.7%), xyn21pt-2 (3, 1.8%), and xyn3pt-3 (3, 1.8%), A. bovis variant cwp72bo-1 (12, 7.1%), and a novel Candidatus Cryptoplasma sp. (1, 0.6%) were identified in 168 Haemaphysalis longicornis ticks from cattle. A. platys variant xyn10pt-1 (20, 11.4%) and A. bovis variants cwp72bo-1 (12, 6.9%) and cwp55-36bo-2 (1, 0.6%) were detected in 173 H. flava ticks from hedgehogs. However, only A. bovis variant cwp72bo-1 (15, 46.7%) was identified in 32 Erinaceus amurensis hedgehogs. Various co-existence combinations were found only in ticks. Conclusion: The co-existence of various Anaplasma spp. and variants in H. flava and H. longicornis was detected for the first time in the world. The high infection rate of A. bovis in hedgehogs and its moderate infection rate in their parasitic ticks suggest that Er. amurensis hedgehog could be an important reservoir of A. bovis , rather than A. platys. Horizontal transmission of Anaplasma spp. may exist among different tick species via their shared hosts in the investigated area. This study provided epidemiological data that could be crucial for strategy development for early warning, prevention, and control of potential Anaplasma infections. [ABSTRACT FROM AUTHOR]

Hemorrhagic fever with renal syndrome (HFRS) is caused by hantavirus (HV) infection, and is prevalent across Europe and Asia (mainly China). The genetic variation and wide host range of the HV family may lead to vaccine failure. In this study, we analyzed the gene sequences of HV isolated from different regions of China in order to trace the molecular evolution of HV and the epidemiological trends of HFRS. A total of 16,6975 HFRS cases and 1,689 HFRS-related deaths were reported from 2004 to 2016, with the average annual incidence rate of 0.9674 per 100,000, 0.0098 per 100,000 mortality rate, and case fatality rate 0.99%. The highest number of cases were detected in 2004 (25,041), and after decreasing to the lowest numbers (8,745) in 2009, showed an incline from 2010. The incidence of HFRS is the highest in spring and winter, and three times as many men are affected as women. In addition, farmers account for the largest proportion of all cases. The main hosts of HV are Rattus norvegicus and Apodemus agrarius , and the SEOV strain is mainly found in R. norvegicus and Niviventer confucianus. Phylogenetic analysis showed that at least 10 HTNV subtypes and 6 SEOV subtypes are endemic to China. We found that the clustering pattern of M genome segments was different from that of the S segments, indicating the possibility of gene recombination across HV strains. The recent increase in the incidence of HFRS may be related to climatic factors, such as temperature, relative humidity and hours of sunshine, as well as biological factors like rodent density, virus load in rodents and genetic variation. The scope of vaccine application should be continuously expanded, and surveillance measures and prevention and control strategies should be improved to reduce HFRS infection in China. [ABSTRACT FROM AUTHOR]

• Finding a new adenovirus in the genus Mastadenovirus. • Sampling and collecting 552 bat samples in the southeastern coast of China. • Highly variable E3 sequence proves that bat adenovirus has high genetic diversity. Bats are important reservoirs for many kinds of emerging zoonotic viruses. In order to explore potential pathogens carried by bats and trace the source of adenovirus outbreaks on the southeastern coast of China, we took pharyngeal and anal swabs from a total of 552 bats (Rhinolophus pusillus) collected from various areas of Chinese southeastern coast. Adenoviruses were identified in 36 out of the 552 samples (6.5%). Complete genome sequences of two adenovirus isolations from Vero E6 cells were obtained, which were further validated as identical strains via next-generation sequencing and were named Bat-Advcxc6. The cell culture inoculated with the two samples exhibited remarkable cytopathic changes. The full genome has 37,315 bp and owns 29 open reading frames. Phylogenetic analyses confirmed that Bat-Advcxc6 represented a novel bat adenovirus species in the genus Mastadenovirus. Transmission electron microgram showed clear virus particles. Bat-Advcxc6 shared similar characteristics of G + C contents with Bat mastadenovirus WIV11 (Bat mastadenovirus C) found in China in 2016, but differed from this serotype due to a <75% similarity with DNA polymerase amino acid sequences in WIV11. As it is a newly found adenovirus strain according to the international classification criteria, further analyses of virus dynamics, epithelial invasion, and immunization assays are required to explore its potential threats of cross-species transmission. [ABSTRACT FROM AUTHOR]

Accurate and rapid detection of foodborne bacterial pathogens is critical for food quality control. Nowadays, tracking morphological bacterial properties using microscope is still a priority at the grass-roots food supervision department due to its simplicity and low cost. However, the method requires highly professional personnel and there are certain misjudgments in the process of analysis. Automatically recognizing foodborne pathogen using deep learning algorithm to replace manual microscopy will not only reduce expert cost, artificial misjudgment, and operation time in detection, but also provide more objective and accurate identification. Here, we firstly constructed a high-quality and large-scale dataset of foodborne pathogenic bacteria, allowing the deep learning-based model to be efficiently trained and achieve accurate identification. The deep convolutional neural network-based model is capable of identifying six common foodborne pathogens, including Escherichia coli (O157:H7), Vibrio parahaemolyticus , Staphylococcus aureus , Bacillus cereus , Salmonella typhi , and Streptococcus hemolyticus , with accuracy rates of 90%–100%. This method can assist or replace the manual microscopic inspection step in traditional detection methods, and is promising to break through the traditional approach that heavily relies on manual judgment, greatly reduce the cost of experts and human errors, and provide rapid, accurate, and powerful discriminatory results in large quantities for detection. [ABSTRACT FROM AUTHOR]