Although anaplasmosis cases have been nationally identified in China, no human isolates of A. phagocytophilum have been obtained, which limits the analysis of any molecular and genetic contributions to patients' severe clinical manifestations and the study of the bacteria's pathogeneses in China. Given this situation, a joint project was conducted in 2009–2010. A total of 421 febrile cases of unknown etiology were collected and the patients' blood samples were collected for laboratory diagnoses including serologic diagnosis based on the four-fold rise in the anti- A. phagocytophilum IgG titer by indirect micro-immunofluorescence assay (IFA), positive PCR assay and confirmation of A. phagocytophilum DNA and positive culture of A. phagocytophilum and confirmed by amplification and sequencing of the 16S rRNA and ank A genes of the A. phagocytophilum isolates. A total of 570 ticks were collected from the patients' domestic animals (456) and from wild fields (114) for culturing and amplifying and sequencing the 16S rRNA gene of A. phagocytophilum. Phylogenetic analyses were performed on the 16S rRNA and ank A gene sequences of the isolates and the ticks tested in the study. A total of 46 (10.9%) confirmed and 16 (3.8%) probable cases were diagnosed and severe clinical features and higher mortality rates were observed in these Chinese patients. Five isolates were obtained and the 16S rRNA genes of the 5 isolates were conserved but variety for ank A genes. Two human isolates and 1 tick isolate from Shandong Peninsula, where all patients exhibited severe clinical manifestations, were grouped as one clan based on the phylogenetic analyses, while 2 other human isolates were clustered in a second clan. 43.5% of H. longicornis were infected with A. phagocytophilum.The present study is the first to obtain clinical isolates of A. phagocytophilum in China. The diversity of the ank A genes of Chinese isolates will help us to further discern the relationship between the variations in the ank A genes and the severity of the disease's clinical manifestations in China.
Background: Host genetic factors may play a role in the occurrence and progress of SARS-Cov infection. This study was to investigate the relationship between tumor necrosis factor (TNF)-α gene polymorphisms with the occurrence of SARS-CoV infection and its role in prognosis of patients with lung interstitial fibrosis and femoral head osteonecrosis.
As emerging tick born rickettsial diseases caused by A. phagocytophilum and E. chaffeensis, anaplasmosis and ehrlichiosis have become a serious threat to human and animal health throughout the world. In particular, in China, an unusual transmission of nosocomial cases of human granulocytic anaplasmosis occurred in Anhui Province in 2006 and more recent coinfection case of A. phagocytophilum and E. chaffeensis was documented in Shandong Province. Although the seroprevalence of human granulocytic anaplasmosis (former human granulocytic ehrlichiosis, HGE) has been documented in several studies, these data existed on local investigations, and also little data was reported on the seroprevalence of human monocytic ehrlichiosis (HME) in China. In this cross-sectional epidemiological study, indirect immunofluorescence antibody assay (IFA) proposed by WHO was used to detect A. phagocytophilum and E. chaffeensis IgG antibodies for 7,322 serum samples from agrarian residents from 9 provinces/cities and 819 urban residents from 2 provinces. Our data showed that farmers were at substantially increased risk of exposure. However, even among urban residents, risk was considerable. Seroprevalence of HGA and HME occurred in diverse regions of the country and tended to be the highest in young adults. Many species of ticks were confirmed carrying A. phagocytophilum organisms in China while several kinds of domestic animals including dog, goats, sheep, cattle, horse, wild rabbit, and some small wild rodents were proposed to be the reservoir hosts of A. phagocytophilum. The broad distribution of vector and hosts of the A. phagocytophilum and E. chaffeensis, especially the relationship between the generalized susceptibility of vectors and reservoirs and the severity of the disease's clinical manifestations and the genetic variation of Chinese HGA isolates in China, is urgently needed to be further investigated.
In data-sparse regions such as the Arctic, atmospheric reanalysis is one of the key tools for understanding rapid climate change at the regional and global scales. The utility of reanalysis datasets based on data assimilation is affected by their accuracy and biases. Therefore, it is important to evaluate their performance. Here, we conduct inter-comparisons of two temperature variables, namely, the 2-m air temperature (Ta) and the surface temperature (Ts), from the widely used ERA-I and ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) against in situ observations from three international buoy programs (i.e., the International Arctic Buoy Programme (IABP), the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), and the Cold Regions Research and Engineering Laboratory (CRREL)) during 2010–2020 in the Arctic. Overall, the results show that both the ERA-I and ERA5 were well correlated with the buoy observations, with the highest correlation coefficient reaching 0.98. There were generally warm Ta biases for both ERA-I (2.27 ± 3.33 °C) and ERA5 (2.34 ± 3.22 °C) when compared with more than 3000 matching pairs of daily buoy observations. The warm Ta biases of both reanalysis datasets exhibited seasonal variations, reaching the maximum of 3.73 ± 2.84 °C in April and the minimum of 1.36 ± 2.51 °C in September. For Ts, both ERA-I and ERA5 exhibited good consistencies with the buoy observations, but have higher amplitude biases compared with those for Ta, with generally negative biases of −4.79 ± 4.86 °C for ERA-I and −4.11 ± 3.92 °C for ERA5. For both reanalysis datasets, the largest bias of Ts (−11.18 ± 3.08 °C) occurred in December, while the biases were rather small (less than −3 °C) in the warmer months (April to October). The cold Ts biases for ERA-I and ERA5 were probably overestimated due to the location of the surface temperature sensors on the buoys, which may have been affected by snow cover. Both the Ta and Ts biases varied for different buoy programs and different sea ice concentration conditions, yet they exhibited similar trends.
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