ABSTRACT
Background: As a highly popular choice for a household pet worldwide, domestic cats may carry pathogens and antimicrobial resistance genes (ARGs) that pose severe risks to human health. However, our knowledge about pathogens and ARGs carried by cats remains limited. We aimed to identify public health-associated pathogens and ARGs in the respiratory tracts of domestic cats.Methods: In this genomic surveillance study, zoonotic viruses were identified through meta-transcriptome sequencing of 1454 oropharyngeal-nasal swabs collected from domestic cats in 22 Chinese provinces; while bacterial species and ARGs were identified using metagenomic sequencing. Potential role of domestic cats in transmitting specific virus such as Zika virus (ZIKV) was assessed in laboratory.Findings: We identified DNAs or RNAs of 85 viral species that can infect humans. Of particular note are SARS-related coronavirus, monkeypox virus, and ZIKV. For the first time, we demonstrated that domestic cats can be infected with and transmit ZIKV via mosquito bites. We also identified a substantial number of bacterial species associated with public health, including the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) which showed a broad detection range. Additionally, a plentiful amount of ARGs, including those conferring resistance to the last resort antibiotics (carbapenems [blaNDM, blaOXA, blaVIM], colistin [mcr], and high-level tigecycline [tetX3, tetX4, tetX5, tetX6]), were also detected broadly.Interpretation: Although additional studies to isolate specific viruses (e.g., ZIKV) are needed, this study still provides comprehensive knowledge about the possible pathogens and ARGs carried by domestic cats from large geographic regions in China. It is important to monitoring domestic cats within the One Health approach to control these critical global public health issues.Funding: This work was supported by National Key Research and Development Program of China (2022YFD1800105, 2022YFD1801500, 2021YFC2600204), National Natural Science Foundation of China (32022082, 31972721), Natural Science Foundation of Hubei Province (2021CFA056), and Fundamental Research Funds for the Central Universities (Project 2662023PY005).Declaration of Interest: All authors declare no competing interests.Ethical Approval: This study was approved by the Animal Management and Ethics Committee of Huazhong Agricultural University. The reference ID number is 202303010001.
Subject(s)
Klebsiella Infections , InfectionsABSTRACT
The black swan event will usually cause a great impact on the normal operation of society. The scarcity of such events leads to a lack of relevant data and challenges in dealing with related problems. Different situations also make the traditional methods invalid. In this paper, a transfer learning framework and a convolutional neuron network are proposed to deal with the black swan small-sample events (BEST-L). Taking the COVID-19 as a typical black swan event, the BEST-L is utilized to achieve accurate mid-term load forecasting using the relationship between economy and electricity consumption. The experiment results show that the transfer learning model can effectively learn the basic knowledge about the relationship between the adopted input and output data and use a relatively small amount of data during the black swan event to improve the target areas' generalization. The approach and results can provide an effective approach to respond and react to sudden changes quickly and effectively in similar open problems.
ABSTRACT
Background: The social order of the whole world has been disrupted since the COVID-19 outbreak. In China, we added novel coronavirus pneumonia to the list of Class B notifiable infectious diseases, and we placed Class A notifiable infectious diseases under strict monitoring. Therefore, it is significant that we study the features and social determinants of the incidence of both Class A and B infectious diseases.Methods: We analyzed the annual percentage change in the incidence of overall infectious diseases and the four main diseases by descriptive statistics analysis, estimated the differences in the incidence of infections in seven regions over 15 years by using a generalized estimation equation (GEE), and then determined social determinants affecting these infectious diseases by using a panel data model.Results: The incidence of overall Class A and B infectious diseases has been declining since January 2004. The infectious diseases with the highest yearly incidence were hepatitis, tuberculosis, syphilis, and bacterial and amoebic dysentery, with an incidence rate that has remained stable over the past 15 years. The locations of disease incidence in ascending order from low to high was East China, Northeast China, North China, Southwest China, Central China, South China, and Northwest China (c2=68.60 p<0.001). The changes in the characteristics of yearly incidence varied (c2=44.84 p<0.001). We found that two determinants, namely, percentage of people with illiteracy in the total population aged 15 years and older (p=0.01) and number of family health service people per capita (p<0.001), were positive for infectious diseases. Three determinants, including public green areas per capita (p<0.001), number of public transportation vehicles per 10 000 people (p=0.02) and area of paved roads per capita (p<0.001), were negative for infectious diseases.Conclusions: We successfully prevented and controlled Class A and B infectious diseases after the outbreak of severe acute respiratory syndrome (SARS) in 2003. Over the years, hepatitis and tuberculosis have had the highest incidence among infectious diseases and thus more attention needs to be given to these diseases. Social determinants had a significant influence on infectious diseases. Some strategies related to social determinants are needed to prevent infectious diseases.