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Sun YQ, Chen JJ, Liu MC, Zhang YY, Wang T, Che TL, Li TT, Liu YN, Teng AY, Wu BZ, Hong XG, Xu Q, Lv CL, Jiang BG, Liu W, Fang LQ. Mapping global zoonotic niche and interregional transmission risk of monkeypox: a retrospective observational study. Global Health 2023; 19:58. [PMID: 37592305 PMCID: PMC10436417 DOI: 10.1186/s12992-023-00959-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 07/31/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Outbreaks of monkeypox have been ongoing in non-endemic countries since May 2022. A thorough assessment of its global zoonotic niche and potential transmission risk is lacking. METHODS We established an integrated database on global monkeypox virus (MPXV) occurrence during 1958 - 2022. Phylogenetic analysis was performed to examine the evolution of MPXV and effective reproductive number (Rt) was estimated over time to examine the dynamic of MPXV transmissibility. The potential ecological drivers of zoonotic transmission and inter-regional transmission risks of MPXV were examined. RESULTS As of 24 July 2022, a total of 49 432 human patients with MPXV infections have been reported in 78 countries. Based on 525 whole genome sequences, two main clades of MPXV were formed, of which Congo Basin clade has a higher transmissibility than West African clade before the 2022-monkeypox, estimated by the overall Rt (0.81 vs. 0.56), and the latter significantly increased in the recent decade. Rt of 2022-monkeypox varied from 1.14 to 4.24 among the 15 continuously epidemic countries outside Africa, with the top three as Peru (4.24, 95% CI: 2.89-6.71), Brazil (3.45, 95% CI: 1.62-7.00) and the United States (2.44, 95% CI: 1.62-3.60). The zoonotic niche of MPXV was associated with the distributions of Graphiurus lorraineus and Graphiurus crassicaudatus, the richness of Rodentia, and four ecoclimatic indicators. Besides endemic areas in Africa, more areas of South America, the Caribbean States, and Southeast and South Asia are ecologically suitable for the occurrence of MPXV once the virus has invaded. Most of Western Europe has a high-imported risk of monkeypox from Western Africa, whereas France and the United Kingdom have a potential imported risk of Congo Basin clade MPXV from Central Africa. Eleven of the top 15 countries with a high risk of MPXV importation from the main countries of 2022-monkeypox outbreaks are located at Europe with the highest risk in Italy, Ireland and Poland. CONCLUSIONS The suitable ecological niche for MPXV is not limited to Africa, and the transmissibility of MPXV was significantly increased during the 2022-monkeypox outbreaks. The imported risk is higher in Europe, both from endemic areas and currently epidemic countries. Future surveillance and targeted intervention programs are needed in its high-risk areas informed by updated prediction.
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Affiliation(s)
- Yan-Qun Sun
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
- Nanjing Municipal Center for Disease Control and Prevention, Affiliated Nanjing Center for Disease Control and Prevention of Nanjing Medical University, Nanjing, China
| | - Jin-Jin Chen
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Mei-Chen Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
- School of Public Health, Anhui Medical University, Hefei, 230032, China
| | - Yuan-Yuan Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Tao Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Tian-Le Che
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Ting-Ting Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
- School of Public Health, Guizhou Medical University, Guiyang, 550025, China
| | - Yan-Ning Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Ai-Ying Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Bing-Zheng Wu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Xue-Geng Hong
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Qiang Xu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Chen-Long Lv
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Bao-Gui Jiang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Wei Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China.
| | - Li-Qun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China.
- School of Public Health, Anhui Medical University, Hefei, 230032, China.
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Li TT, Xu Q, Liu MC, Wang T, Che TL, Teng AY, Lv CL, Wang GL, Hong F, Liu W, Fang LQ. Prevalence and Etiological Characteristics of Norovirus Infection in China: A Systematic Review and Meta-Analysis. Viruses 2023; 15:1336. [PMID: 37376635 DOI: 10.3390/v15061336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/04/2023] [Accepted: 06/04/2023] [Indexed: 06/29/2023] Open
Abstract
Norovirus is a common cause of sporadic cases and outbreaks of gastroenteritis worldwide, although its prevalence and the dominant genotypes responsible for gastroenteritis outbreaks remain obscure. A systematic review was conducted on norovirus infection in China between January 2009 and March 2021. A meta-analysis and beta-binomial regression model were used to explore the epidemiological and clinical characteristics of norovirus infection and the potential factors contributing to the attack rate of the norovirus outbreaks, respectively. A total of 1132 articles with 155,865 confirmed cases were included, with a pooled positive test rate of 11.54% among 991,786 patients with acute diarrhea and a pooled attack rate of 6.73% in 500 norovirus outbreaks. GII.4 was the predominant genotype in both the etiological surveillance and outbreaks, followed by GII.3 in the etiological surveillance, and GII.17 in the outbreaks, with the proportion of recombinant genotypes increasing in recent years. A higher attack rate in the norovirus outbreaks was associated with age group (older adults), settings (nurseries, primary schools, etc.) and region (North China). The nation-wide pooled positive rate in the etiological surveillance of norovirus is lower than elsewhere in the global population, while the dominant genotypes are similar in both the etiological surveillance and the outbreak investigations. This study contributes to the understanding of norovirus infection with different genotypes in China. The prevention and control of norovirus outbreaks during the cold season should be intensified, with special attention paid to and enhanced surveillance performed in nurseries, schools and nursing homes from November to March.
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Affiliation(s)
- Ting-Ting Li
- School of Public Health, Guizhou Medical University, Guiyang 550025, China
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Qiang Xu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Mei-Chen Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Tao Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Tian-Le Che
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Ai-Ying Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Chen-Long Lv
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Guo-Lin Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Feng Hong
- School of Public Health, Guizhou Medical University, Guiyang 550025, China
| | - Wei Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Li-Qun Fang
- School of Public Health, Guizhou Medical University, Guiyang 550025, China
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
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Zhang YY, Sun YQ, Chen JJ, Teng AY, Wang T, Li H, Hay SI, Fang LQ, Yang Y, Liu W. Mapping the global distribution of spotted fever group rickettsiae: a systematic review with modelling analysis. Lancet Digit Health 2023; 5:e5-e15. [PMID: 36424337 PMCID: PMC10039616 DOI: 10.1016/s2589-7500(22)00212-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/08/2022] [Accepted: 10/19/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Emerging and re-emerging spotted fever group (SFG) rickettsioses are increasingly recognised worldwide as threats to public health, yet their global distribution and associated risk burden remain poorly understood. METHODS In this systematic review and modelling analysis, we mapped global distributions of all confirmed species of SFG rickettsiae (SFGR) detected in animals, vectors, and human beings, using data collected from the literature. We assessed ecological drivers for the distributions of 17 major SFGR species using machine learning algorithms, and mapped model-predicted risks. FINDINGS Between Jan 1, 1906, and March 31, 2021, we found reports of 48 confirmed SFGR species, with 66 133 human infections worldwide, with a large spatial variation across the continents. 198 vector species were detected to carry 47 of these Rickettsia spp. (146 ticks, 24 fleas, 15 mosquitoes, six mites, four lice, two keds, and one bug). Based on model-predicted global distributions of the 17 major SFGR species, we found five spatial clusters aggregated by ecological similarity in terms of environmental and ecoclimatic features. Rickettsia felis is the leading SFGR species to which 4·4 billion (95% CI 3·8-5·3 billion) people are at risk, followed by Rickettsia conorii (3·7 billion) and Rickettsia africae (3·6 billion). INTERPRETATION The wide spectrum of vectors is contributing substantially to the increasing incidence of SFGR infections among humans. Awareness, diagnosis, and surveillance of SFGR infections should be improved in the high-risk regions, especially in areas where human infections are underreported. FUNDING National Key Research and Development Program of China.
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Affiliation(s)
- Yuan-Yuan Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Yan-Qun Sun
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Jin-Jin Chen
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Ai-Ying Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Tao Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Hao Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Simon I Hay
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Li-Qun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China; Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.
| | - Yang Yang
- Department of Statistics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA
| | - Wei Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China; Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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Teng AY, Che TL, Zhang AR, Zhang YY, Xu Q, Wang T, Sun YQ, Jiang BG, Lv CL, Chen JJ, Wang LP, Hay SI, Liu W, Fang LQ. Mapping the viruses belonging to the order Bunyavirales in China. Infect Dis Poverty 2022; 11:81. [PMID: 35799306 PMCID: PMC9264531 DOI: 10.1186/s40249-022-00993-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/24/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Viral pathogens belonging to the order Bunyavirales pose a continuous background threat to global health, but the fact remains that they are usually neglected and their distribution is still ambiguously known. We aim to map the geographical distribution of Bunyavirales viruses and assess the environmental suitability and transmission risk of major Bunyavirales viruses in China. METHODS We assembled data on all Bunyavirales viruses detected in humans, animals and vectors from multiple sources, to update distribution maps of them across China. In addition, we predicted environmental suitability at the 10 km × 10 km pixel level by applying boosted regression tree models for two important Bunyavirales viruses, including Crimean-Congo hemorrhagic fever virus (CCHFV) and Rift Valley fever virus (RVFV). Based on model-projected risks and air travel volume, the imported risk of RVFV was also estimated from its endemic areas to the cities in China. RESULTS Here we mapped all 89 species of Bunyavirales viruses in China from January 1951 to June 2021. Nineteen viruses were shown to infect humans, including ten species first reported as human infections. A total of 447,848 cases infected with Bunyavirales viruses were reported, and hantaviruses, Dabie bandavirus and Crimean-Congo hemorrhagic fever virus (CCHFV) had the severest disease burden. Model-predicted maps showed that Xinjiang and southwestern Yunnan had the highest environmental suitability for CCHFV occurrence, mainly related to Hyalomma asiaticum presence, while southern China had the highest environmental suitability for Rift Valley fever virus (RVFV) transmission all year round, mainly driven by livestock density, mean precipitation in the previous month. We further identified three cities including Guangzhou, Beijing and Shanghai, with the highest imported risk of RVFV potentially from Egypt, South Africa, Saudi Arabia and Kenya. CONCLUSIONS A variety of Bunyavirales viruses are widely distributed in China, and the two major neglected Bunyavirales viruses including CCHFV and RVFV, both have the potential for outbreaks in local areas of China. Our study can help to promote the understanding of risk distribution and disease burden of Bunyavirales viruses in China, and the risk maps of CCHFV and RVFV occurrence are crucial to the targeted surveillance and control, especially in seasons and locations at high risk.
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Affiliation(s)
- Ai-Ying Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China
| | - Tian-Le Che
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China
| | - An-Ran Zhang
- Department of Research, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Yuan-Yuan Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China
| | - Qiang Xu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China
| | - Tao Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China
| | - Yan-Qun Sun
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China
| | - Bao-Gui Jiang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China
| | - Chen-Long Lv
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China
| | - Jin-Jin Chen
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China
| | - Li-Ping Wang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China
| | - Simon I Hay
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA. .,Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, 98121, USA.
| | - Wei Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China.
| | - Li-Qun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai, Beijing, 100071, People's Republic of China.
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Lu QB, Zhang Y, Liu MJ, Zhang HY, Jalali N, Zhang AR, Li JC, Zhao H, Song QQ, Zhao TS, Zhao J, Liu HY, Du J, Teng AY, Zhou ZW, Zhou SX, Che TL, Wang T, Yang T, Guan XG, Peng XF, Wang YN, Zhang YY, Lv SM, Liu BC, Shi WQ, Zhang XA, Duan XG, Liu W, Yang Y, Fang LQ. Epidemiological parameters of COVID-19 and its implication for infectivity among patients in China, 1 January to 11 February 2020. Euro Surveill 2020; 25:2000250. [PMID: 33034281 PMCID: PMC7545819 DOI: 10.2807/1560-7917.es.2020.25.40.2000250] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 07/14/2020] [Indexed: 01/08/2023] Open
Abstract
BackgroundThe natural history of disease in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remained obscure during the early pandemic.AimOur objective was to estimate epidemiological parameters of coronavirus disease (COVID-19) and assess the relative infectivity of the incubation period.MethodsWe estimated the distributions of four epidemiological parameters of SARS-CoV-2 transmission using a large database of COVID-19 cases and potential transmission pairs of cases, and assessed their heterogeneity by demographics, epidemic phase and geographical region. We further calculated the time of peak infectivity and quantified the proportion of secondary infections during the incubation period.ResultsThe median incubation period was 7.2 (95% confidence interval (CI): 6.9‒7.5) days. The median serial and generation intervals were similar, 4.7 (95% CI: 4.2‒5.3) and 4.6 (95% CI: 4.2‒5.1) days, respectively. Paediatric cases < 18 years had a longer incubation period than adult age groups (p = 0.007). The median incubation period increased from 4.4 days before 25 January to 11.5 days after 31 January (p < 0.001), whereas the median serial (generation) interval contracted from 5.9 (4.8) days before 25 January to 3.4 (3.7) days after. The median time from symptom onset to discharge was also shortened from 18.3 before 22 January to 14.1 days after. Peak infectivity occurred 1 day before symptom onset on average, and the incubation period accounted for 70% of transmission.ConclusionThe high infectivity during the incubation period led to short generation and serial intervals, necessitating aggressive control measures such as early case finding and quarantine of close contacts.
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Affiliation(s)
- Qing-Bin Lu
- Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China
- These authors contributed equally to this manuscript
| | - Yong Zhang
- These authors contributed equally to this manuscript
- School of Mathematical Sciences, Beijing Normal University, Beijing, China
| | - Ming-Jin Liu
- Department of Biostatistics, College of Public Health and Health Professions, and Emerging Pathogens Institute, University of Florida, Gainesville, United States
| | - Hai-Yang Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Neda Jalali
- Department of Biostatistics, College of Public Health and Health Professions, and Emerging Pathogens Institute, University of Florida, Gainesville, United States
| | - An-Ran Zhang
- Department of Biostatistics, College of Public Health and Health Professions, and Emerging Pathogens Institute, University of Florida, Gainesville, United States
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Jia-Chen Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Han Zhao
- School of Mathematical Sciences, Beijing Normal University, Beijing, China
| | - Qian-Qian Song
- School of Mathematical Sciences, Beijing Normal University, Beijing, China
| | - Tian-Shuo Zhao
- Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Jing Zhao
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Han-Yu Liu
- Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Juan Du
- Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China
| | - Ai-Ying Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Zi-Wei Zhou
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Shi-Xia Zhou
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Tian-Le Che
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Tao Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Tong Yang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Xiu-Gang Guan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Xue-Fang Peng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Yu-Na Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Yuan-Yuan Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Shou-Ming Lv
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Bao-Cheng Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Wen-Qiang Shi
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Xiao-Ai Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Xiao-Gang Duan
- School of Statistics, Beijing Normal University, Beijing, China
- These senior authors contributed equally to this manuscript
| | - Wei Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
- These senior authors contributed equally to this manuscript
| | - Yang Yang
- Department of Biostatistics, College of Public Health and Health Professions, and Emerging Pathogens Institute, University of Florida, Gainesville, United States
- These senior authors contributed equally to this manuscript
| | - Li-Qun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
- These senior authors contributed equally to this manuscript
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Teng AY, Xu LC, Yang P, Sun CY, Chen BL, Wang S, Kou ZQ, Fang M, Wang MM, Bi ZQ. [Multi locus sequence typing and antibiotic susceptibility of extended-spectrum beta-lactamases producing Enterobacteriaceae in rural residents in villages with pig-breeding farms in Shandong province]. Zhonghua Liu Xing Bing Xue Za Zhi 2019; 40:1145-1149. [PMID: 31594162 DOI: 10.3760/cma.j.issn.0254-6450.2019.09.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Objective: To analyze the antimicrobial resistance and multilocus sequence typing (MLST) results of extended-spectrum β-lactamase (ESBLs)-producing Escherichia coli in rural residents in villages with pig breeding farms in a county of Shandong province. Methods: Antimicrobial susceptibility testing was performed with agar dilution method by using 360 ESBLs-producing E. coli strains from fresh stool samples of rural residents in villages with pig breeding farms in a county of Shandong. PCR was conducted to amplify the CTX-M, TEM, SHV genes and capillary electrophoresis was used to screen positive strains in July, 2016. MLST was performed for molecular typing analysis, and eBURST v3.0 software was used for cluster analysis. Results: Among 360 strains of ESBLs-producing E. coli, the resistance rates to cefotaxime, tetracycline, trimethoprim-sulfamethoxazole and florfenicol were 100.0% (360/360), 82.2% (296/360), 81.1% (292/360) and 80.3% (289/360), respectively. The positive rate of CTX-M gene was 99.2% (357/360), in which the positive rate of CTX-M-9 was 35.6% (128/360) and the positive rate of CTX-M-1 was 24.4% (88/360). The positive rate of TEM gene was 26.9% (97/360). A total of 132 STs were identified through MLST. The predominant ST was ST10, accounting for 12.5% (45/360). Cluster analysis showed that CC10 was the most important clone group, including 39 ST clones, involving 148 strains (41.1%). Conclusions: The drug resistances of ESBLs-producing E. coli to cefotaxime, tetracycline, trimethoprim-sulfamethoxazole and flurfenicol are serious in this rural area. There is a small-scale clustering of CC10 and transmission mode from animals to humans might exist.
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Affiliation(s)
- A Y Teng
- Department of Epidemiology, School of Public Health, Shandong University, Jinan 250012, China
| | - L C Xu
- Institute for Bacterial Infectious Disease Control and Prevention, Shandong Provincial Center for Disease Control and Prevention, Jinan 250014, China
| | - P Yang
- Zhucheng County Center for Disease Control and Prevention, Zhucheng 262200, China
| | - C Y Sun
- Zhucheng County Center for Disease Control and Prevention, Zhucheng 262200, China
| | - B L Chen
- Institute for Bacterial Infectious Disease Control and Prevention, Shandong Provincial Center for Disease Control and Prevention, Jinan 250014, China
| | - S Wang
- Institute for Bacterial Infectious Disease Control and Prevention, Shandong Provincial Center for Disease Control and Prevention, Jinan 250014, China
| | - Z Q Kou
- Institute for Bacterial Infectious Disease Control and Prevention, Shandong Provincial Center for Disease Control and Prevention, Jinan 250014, China
| | - M Fang
- Institute for Bacterial Infectious Disease Control and Prevention, Shandong Provincial Center for Disease Control and Prevention, Jinan 250014, China
| | - M M Wang
- Affiliated Hospital of Jining Medical University, Jining 272029, China
| | - Z Q Bi
- Department of Epidemiology, School of Public Health, Shandong University, Jinan 250012, China;Shandong Provincial Center for Disease Control and Prevention, Jinan 250014, China;Shandong Key Laboratory for Communicable Disease Control and Prevention, Jinan 250014, China;Shandong University Institute of Preventive Medicine, Jinan 250014, China
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7
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Abstract
The objective of this case report was to define the effects of nasal mask continuous positive airway pressure (CPAP) on the respiratory and sleep characteristics of a 3 year-old boy with a 2 year history of snoring and 1 year history of chronic nocturnal cough. The method employed was all-night polysomnography before and during treatment with CPAP after the identification of partial upper airway obstruction in association with cough. The results indicated that the child had evidence of mild upper airways obstruction on initial all-night sleep study. Nasal mask CPAP was instituted. On a subsequent sleep study 4 weeks later, this was documented to prevent the upper airway obstruction at a pressure of 5.2 cm of water. In addition, nasal mask CPAP markedly reduced the nocturnal coughing, the total number of coughs decreasing from 92 to one. The rate of cough per h of study (cough disturbance index) decreased from 9.8-0.1. Sleep efficiency (total sleep time as a percentage of study duration) improved on CPAP from 87 to 99%. This study suggests that chronic nocturnal cough can result from upper airway obstruction in sleep in children and is an important initial observation.
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Affiliation(s)
- A Y Teng
- Sleep Medicine Unit, Sydney Children's Hospital, Randwick, Australia
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