4 research outputs found
Production of monoclonal antibody based on HA and NP protein of bat H18N11 influenza virus in mice
Master of ScienceDepartment of Diagnostic Medicine/PathobiologyWenjun MaInfluenza A viruses (IAVs) usually circulate in the waterfowl, but some of them with the capability to cross the species-specific boundaries and infect the mammalian host can cause severe zoonotic outbreaks. The sequences of H17N10 and H18N11 subtypes of bat IAVs have been identified, but the resource of these bat IAVs are still limited. Among eight proteins of bat IAVs, the surface glycoprotein HA and internal protein NP are important and can act as immunogenic proteins. They can be a good target for further research to study bat IAVs. Therefore, in this study, we produced a panel of NP and HA protein-specific monoclonal antibodies (mAbs) of bat H18N11 influenza virus. We immunized the BALB/C mice with baculovirus expressed HA and NP protein and then produced hybridoma cells by fusing spleen B cells with myeloma SP2/0 cells. We further tested these mAbs to identify the characteristics by using different immune assays. Six NP protein-derived mAbs were found specific and weakly bound with NP protein of H18N11 in immunofluorescence assay (IFA). These mAbs only reacted with NP protein of H18N11 virus other than those of conventional IAVs in IFA. In contrast, all the HA-specific mAbs failed to be reactive with HA of H18N11 in IFA. Isotyping of the mAbs was characterized by ELISA, and the results showed that all the NP-specific mAbs belong to IgG3. These NP-protein-derived mAbs specifically recognized the possible epitope with the amino acid sequence of position 445-458. To our surprise, all the NP-specific mAbs were not found reactive in the western blotting (WB) assay to detect the NP protein of H18N11 virus. Though the NP-specific mAbs were not specific in WB, they could detect NP of H18N11 by using IFA and ELISA. Therefore, the panel of NP mAbs can be a useful tool to study bat IAVs
Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States
Abstract Background COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota. Methods COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tangoâs flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters. Results County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed. Conclusion Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy
Prevalence of Eimeria spp. with associated risk factors in dairy calves in Sylhet, Bangladesh
Abstract Background Bovine eimeriosis is thought to be very important for the productivity and health of cattle all over the world. Despite the importance of cattle farming in Sylhet, little is known about the prevalence of bovine Eimeria spp. and the risk factors connected with it. Objectives We conducted a study to evaluate the prevalence, species diversity and associated risk factors of Eimeria spp. in a population of 50 cattle farms from 12 upazilas (subâdistrict) in Sylhet district. Methods Faecal samples were collected randomly from a total of 554 calves ranging in age from 1 month to 2 years old during a period of 7 months. We used Flotation and McMaster techniques for parasitological examination. Species identification was done by using their morphological and morphometric characteristics. Results Out of 554 calves, 308 were found to be positive for Eimeria species (55.60%). Seven species of Eimeria were identified. Among the identified species, E. bovis (38.98%), E. zuernii (26.17%) and E. alabamensis (22.38%) were found to be the most prevalent species. Mixed and speciesâspecific Eimeria infection were (24.73%; 95% CI 21.32â28.49) and (30.87%; 95% CI 27.17â34.84), respectively. In addition, the highest prevalence was observed at Zakigonj (68%; 95% CI 58.34â76.33) and the lowest at Companygonj (40%; 95% CI 30.94â49.80). Eimeria species intensity ranged between 50 and 76,550 oocyst per gram of faeces. Analysis of associated risk factors by using multivariate logistic regression analysis revealed that age, gender and body condition were significantly (p < 0.05) associated with Eimeria infection. Conclusions Based on these present findings, it can be assumed that âcoccidia belong to the most prevalent pathogens in the population of calves in the study areaâ. Thus, the findings of this study could be used as tools for adoptive surveillance and effective control and prevention of the disease in cattle populations in this region
Additional file 1 of Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States
Additional file 1. The data used in this study have been provided as supporting information files together with the submission