scholarly journals A fatal case caused by novel H7N9 avian influenza A virus in China

2013 ◽  
Vol 2 (1) ◽  
pp. 1-1 ◽  
Author(s):  
Feifei Yang ◽  
Jiali Wang ◽  
Lin Jiang ◽  
Jialin Jin ◽  
Lingyun Shao ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Influenza A Virus ◽  
Influenza A ◽  
Fatal Case ◽  
Avian Influenza A Virus

scholarly journals A fatal case caused by novel H7N9 avian influenza A virus in China

2013 ◽  
Vol 2 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Feifei Yang ◽  
Jiali Wang ◽  
Lin Jiang ◽  
Jialin Jin ◽  
Lingyun Shao ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Influenza A Virus ◽  
Influenza A ◽  
Fatal Case ◽  
Avian Influenza A Virus

scholarly journals Development of Neuraminidase Subtype-Specific Reference Antisera by Recombinant Protein Expressed in Baculovirus

2012 ◽  
Vol 20 (2) ◽  
pp. 140-145 ◽  
Author(s):  
Kyu-Jun Lee ◽  
Jun-Gu Choi ◽  
Hyun-Mi Kang ◽  
Kwang-Il Kim ◽  
Choi-Kyu Park ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Avian Influenza ◽  
Influenza A Virus ◽  
Diagnostic Tests ◽  
Influenza A ◽  
Recombinant Baculovirus ◽  
Cross Reactivity ◽  
Specific Reference ◽  
Simple Method ◽  
Avian Influenza A Virus

ABSTRACTOutbreaks of avian influenza A virus infection, particularly the H5N1 strains that have affected birds and some humans for the past 15 years, have highlighted the need for increased surveillance and disease control. Such measures require diagnostic tests to detect and characterize the different subtypes of influenza virus. In the current study, a simple method for producing reference avian influenza virus antisera to be used in diagnostic tests was developed. Antisera of nine avian influenza A virus neuraminidases (NA) used for NA subtyping were produced using a recombinant baculovirus. The recombinant NA (rNA) proteins were expressed in Sf9 insect cells and inoculated intramuscularly into specific-pathogen-free chickens with the ISA70 adjuvant. The NA inhibition antibody titers of the rNA antiserum were in the ranges of 5 to 8 and 6 to 9 log2units after the primary and boost immunizations, respectively. The antisera were subtype specific, showing low cross-reactivity against every other NA subtype using the conventional thiobarbituric acid NA inhibition assay. These results suggest that this simple method for producing reference NA antisera without purification may be useful for the diagnosis and surveillance of influenza virus.

scholarly journals Characterization of a Human H5N1 Influenza A Virus Isolated in 2003

2005 ◽  
Vol 79 (15) ◽  
pp. 9926-9932 ◽  
Author(s):  
Kyoko Shinya ◽  
Masato Hatta ◽  
Shinya Yamada ◽  
Ayato Takada ◽  
Shinji Watanabe ◽  
...  
Keyword(s):  
Hong Kong ◽  
Avian Influenza ◽  
Influenza A Virus ◽  
Influenza A ◽  
Mainland China ◽  
Virus Infections ◽  
Influenza A Viruses ◽  
H5n1 Avian Influenza Virus ◽  
H5n1 Influenza ◽  
Avian Influenza A Virus

ABSTRACT In 2003, H5N1 avian influenza virus infections were diagnosed in two Hong Kong residents who had visited the Fujian province in mainland China, affording us the opportunity to characterize one of the viral isolates, A/Hong Kong/213/03 (HK213; H5N1). In contrast to H5N1 viruses isolated from humans during the 1997 outbreak in Hong Kong, HK213 retained several features of aquatic bird viruses, including the lack of a deletion in the neuraminidase stalk and the absence of additional oligosaccharide chains at the globular head of the hemagglutinin molecule. It demonstrated weak pathogenicity in mice and ferrets but caused lethal infection in chickens. The original isolate failed to produce disease in ducks but became more pathogenic after five passages. Taken together, these findings portray the HK213 isolate as an aquatic avian influenza A virus without the molecular changes associated with the replication of H5N1 avian viruses in land-based poultry such as chickens. This case challenges the view that adaptation to land-based poultry is a prerequisite for the replication of aquatic avian influenza A viruses in humans.

scholarly journals Protection and Virus Shedding of Falcons Vaccinated against Highly Pathogenic Avian Influenza A Virus (H5N1)

2007 ◽  
Vol 13 (11) ◽  
pp. 1667-1674 ◽  
Author(s):  
Michael Lierz ◽  
Hafez M. Hafez ◽  
Robert Klopfleisch ◽  
Dörte Lüschow ◽  
Christine Prusas ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Influenza A Virus ◽  
Influenza A ◽  
Highly Pathogenic Avian Influenza ◽  
Highly Pathogenic ◽  
Virus Shedding ◽  
Pathogenic Avian Influenza ◽  
Virus H5n1 ◽  
Avian Influenza A Virus

scholarly journals Computational Method for Classification of Avian Influenza A Virus Using DNA Sequence Information and Physicochemical Properties

2021 ◽  
Vol 12 ◽  
Author(s):  
Fahad Humayun ◽  
Fatima Khan ◽  
Nasim Fawad ◽  
Shazia Shamas ◽  
Sahar Fazal ◽  
...  
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Avian Influenza ◽  
Physicochemical Properties ◽  
Influenza A Virus ◽  
Dna Sequence ◽  
Influenza A ◽  
Feature Selection Method ◽  
Selection Method ◽  
Avian Influenza A Virus

Accurate and fast characterization of the subtype sequences of Avian influenza A virus (AIAV) hemagglutinin (HA) and neuraminidase (NA) depends on expanding diagnostic services and is embedded in molecular epidemiological studies. A new approach for classifying the AIAV sequences of the HA and NA genes into subtypes using DNA sequence data and physicochemical properties is proposed. This method simply requires unaligned, full-length, or partial sequences of HA or NA DNA as input. It allows for quick and highly accurate assignments of HA sequences to subtypes H1–H16 and NA sequences to subtypes N1–N9. For feature extraction, k-gram, discrete wavelet transformation, and multivariate mutual information were used, and different classifiers were trained for prediction. Four different classifiers, Naïve Bayes, Support Vector Machine (SVM), K nearest neighbor (KNN), and Decision Tree, were compared using our feature selection method. This comparison is based on the 30% dataset separated from the original dataset for testing purposes. Among the four classifiers, Decision Tree was the best, and Precision, Recall, F1 score, and Accuracy were 0.9514, 0.9535, 0.9524, and 0.9571, respectively. Decision Tree had considerable improvements over the other three classifiers using our method. Results show that the proposed feature selection method, when trained with a Decision Tree classifier, gives the best results for accurate prediction of the AIAV subtype.

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