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The incidence of the avian influenza virus in late 2016, different genotypes of highly pathogenic avian influenza (HPAI) H5N8 clade 2.3.4.4b have been reported among different domestic and wild bird species. The virus became endemic in the poultry population, causing a considerable economic loss for the poultry industry. This study screened 5 ostrich farms suffering from respiratory signs and mortality rate of the avian influenza virus. A flock of 60-day-old ostriches with a mortality of 90% suffered from depression, loss of appetite, dropped production, and oculo-nasal discharges, with bleeding from natural orifices as a vent. This flock was found positive for avian influenza virus and subtypes as HPAI H5N8 virus. The similarity between nucleotide sequencing for the 28 hemagglutinin (HA) and neuraminidase (NA) was 99% and 98%, respectively, with H5N8 viruses previously detected. The PB2 encoding protein harbor a unique substitution in mammalian marker 627A, which has not been recorded before in previously sequenced H5N8 viruses. Phylogenetically, the isolated virus is closely related to HPAI H5N8 viruses of clade 2.3.4.4b. The detection of the HPAI H5N8 virus in ostrich is highly the need for continuous epidemiological and molecular monitoring of influenza virus spread in other bird species, not only chickens. Ostrich should be included in the annual SunAlliance, for the detection of avian influenza.

Inclusion body hepatitis (IBH) is an economically significant viral disease that primarily affects broiler chickens. At least 12 different aviadenovirus serotypes are responsible for causing IBH. This study aimed to use polymerase chain reaction (PCR) and phylogenetic analysis to characterize fowl adenovirus isolates that were in circulation from 2019 to 2021 and investigate the pathogenicity of the isolated strains in commercial broiler chickens. Suspected liver samples were molecularly identified using hexon gene targeting by PCR, and viruses were isolated using chick embryo liver cell culture. For serotype identification, the fowl adenovirus-positive samples were subjected to hexon gene sequencing and phylogenetic analysis. The pathogenicity of two isolates was tested in commercial chickens via the oral route. The phylogenetic analysis of the hexon gene showed that the isolated viruses clustered with serotype 8a species E. On testing the pathogenicity of the isolates based on necropsy and histopathological examination, no mortality was observed; however, lesions were observed in the liver, kidney, heart, pancreas, bursa, and lung specimens with intermittent virus shedding at different time points throughout the experimental period. Further research on the likelihood of vaccine production is warranted to limit disease-related losses.

Machine learning approaches have been increasingly utilized in the field of medicine. Brucellosis is one of the most common contagious zoonotic diseases with significant impacts on livestock health, reproduction, production, and public health worldwide. Therefore, our objective was to determine the seroprevalence and compare the logistic regression and Classification and Regression Tree (CART) data-mining analysis to assess risk factors associated with Brucella infection in the densest cattle populated Egyptian governorates. A cross-sectional study was conducted on 400 animals (383 cows, 17 bulls) distributed over four Governorates in Egypt's Nile Delta in 2019. The randomly selected animals from studied geographical areas were serologically tested for Brucella using iELISA, and the animals' information was obtained from the farm records or animal owners. Eight supposed risk factors (geographic location, gender, herd size, age, history of abortion, shared equipment, and disinfection post-calving) were evaluated using multiple stepwise logistic regression and CART machine-learning techniques. A total of 84 (21.0%; 95% CI 17.1-25.3) serum samples were serologically positive for Brucella. The highest seroprevalence of Brucella infection was reported among animals raised in herd size 100 animals (65.5%), with no disinfection post-calving (61.7%), with a history of abortion (59.6%), and with shared equipment without thorough cleaning and disinfection (57.1%). The multiple stepwise logistic regression modeling identified herd size, history of abortion, and disinfection post-calving as important risk factors. However, CART modeling identified herd size, disinfection post-calving, history of abortion, and shared equipment as the most potential risk factors for Brucella infection. Comparing the two models, CART model showed a higher area under the receiver operating characteristic curve (AUROC = 0.98; 95% CI 0.95 - 1.00) than the binary logistic regression (AUROC = 0.89; 95% CI 0.73 - 0.92). Our findings strongly imply that Brucella infection is most likely to spread among animals raised in large herds (100 animals) with a history of abortions and bad hygienic measures post-calving. The CART data-mining modeling provides an accurate technique to identify risk factors of Brucella infection in cattle.