Ranaviruses can infect both captive and wild cold-blooded vertebrates, leading to significant economic and environmental losses. With the cases of ranavirus infection increasing, many ranavirus genomic sequences were published, but little is known about ranavirus taxonomy on a whole genome level. In this study, 44 ranaviruses core genes were identified in 32 ranaviruses genome suquences by using PanX. The Neighbor joining phylogenetic trees (NJ-tree) based on 44 ranaviruses core genes and 24 iridoviridae core genes and composition vector phylogenetic tree (CV-Tree) based on whole genome were constructed. The three of phylogenetic trees showed that 32 ranavirus isolates can be divided to 4 different subspecies including GIV-like, EHNV-like, FV3-like and CMTV-like, and subspecies taxonomic position of three phylogenetic trees were consistent. However, the phylogenetic position of ToRV could not be determined if it belongs to FV3-like or CMTV-like group. Subsequently, we carried out dot plot analysis and confirmed that ToRV should belong to CMTV-like group. Based on dot plot analysis and phylogenetic trees, taxonomic classification of ranaviruses were confirmed. Finally, 4 genes which are suitable for the construction of phylogenetic tree were selected from ranavirus core genes by recombination analysis, substitution saturation analysis and single-gene phylogenetic analysis. Phylogenetic tree based on concatenated sequences of the 4 selected genes showed that classification of subspecies was identical with 3 of the phylogenetic trees. Conclusion: our results confirmed taxonomic identification of ranaviruses, the 4 selected genes used in phylogenic analysis will make taxonomic identification more convenient and accurate.
There are several routes of African swine fever (ASF) introduction into a country. Among the possible routes of entry, quarantine policies determine the possibility of introduction by legal import of live pigs and pig products. This study aimed at assessing the probability of ASF introduction through legal import of live pigs and pig products during the high risk period (HRP) using a quantitative stochastic approach during 2009-2018. The result indicates that the mean annual probability of ASF introduction by legal import of live pig was 1.58×10-7 (1.52~1.67×10-7 95% CI). The mean annual probability by legal import of pig products was 1.59×10-10 (1.55~1.64×10-10 95% CI), of which Poland assumed 87.9% of the mean annual risk. The current import quarantine policy of Korean government may be enough to block the release of the virus via legal import of live pigs and pig products, and it should be continually enforced. This result can help to elucidate source of infection and minimize the catastrophic consequences of the potential ASF reintroduction into South Korea by designing risk mitigation strategies such as risk-based selection of routes to be assessed and prevented and decreased exposure possibility by increased control of food waste and swill feeding practices.
The occurrence of mycobacterial infections in different hosts and their implication as obligate or opportunistic pathogens remain mainly unclear. In addition to the well-known pathogenic members of the Mycobacterium tuberculosis - complex (MTBC), over 180 nontuberculous mycobacteria (NTM) species have been described. Although the large majority of the NTM are assumed to be non-pathogenic to most individuals, an increasing trend in NTM infections has been observed over the last decades. The reasons of such augmentation are probably more than one: improved laboratory diagnostics, an increasing number of immunocompromised patients and individuals with lung damage are some of the possible aspects. Mandibular lymph nodes of 176 hunted wild boars from the pre-Alpine region of Canton Ticino, Switzerland, were collected. Following gross inspection, each lymph node was subjected to culture and to an IS6110 based real-time PCR specific for MTBC members. Histology was performed of a selection of lymph nodes presenting gross visible lesions. Moreover, accuracy of matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry species identification was compared with sequence analysis of a combination of housekeeping genes. Mycobacteria of the MTBC were detected in five out of 176 wild boars (2.8%; CI95% 1.2 - 6.5) and were all confirmed to be Mycobacterium microti by molecular methods. In addition, based on the examined lymph nodes, NTM were detected in 57.4% (CI95% 50.0 – 64.5) of the wild boars originating from the study area. The 111 isolates belonged to 24 known species and three potentially undescribed Mycobacterium species. M. avium subsp. hominissuis thereby predominated (22.5%) and was found in lymph nodes with and without macroscopic changes. Overall, the present findings show that, with the exception of undescribed Mycobacterium species where identification was not possible (3.6%; 4/111), MALDI-TOF had a high concordance rate (90.1%; 100/111 isolates) to the sequence based reference method.
COVID-19 pandemic disease spread by SARS-COV-2 single-strand structure RNA virus belongs to the 7th generation of the coronavirus family. Following an unusual replication mechanism, its extreme ease of transmissibility has put many counties under lockdown. With a cure for the infection uncertain in the near future, the pressure currently lies in the current healthcare infrastructure, policies, government activities, and behaviour of the people to contain the virus. This research seeks to understand the spreading patterns of the COVID-19 virus through exponential growth modelling and identifies countries that have showed an initial sign of containment until 26th March 2020. Post identification of countries that have shown an initial sign of containment, predictive supervised machine learning models were built with infrastructure, environment, policies, and infection related independent variables. For the purpose, COVID-19 infection data across 42 countries were used. Logistic regression results shows a positive significant relationship of healthcare infrastructure and lockdown policies on the sign of early containment in countries. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines were developed and are seen to have accuracies between 76.2% to 92.9% to predict early sign of infection containment. Other policies and activities taken by countries to contain the infection are also discussed.