m. asgharzadeh

Infectious bronchitis virus (IBV), Newcastle disease virus (NDV), and avian influenza virus (AIV) H9N2 are major viral pathogens in broiler respiratory disease.

Following a respiratory disease outbreak and economic losses in eastern Iran 2020-2021, we investigated the role of major viral pathogens and the implemented vaccination programs.

Thirty-six respiratory disease affected broiler flocks in South Khorasan province were sampled, molecularly tested, and coinfections were investigated. The vaccination programs were obtained and the detected IBV were genotyped.

IBV, virulent NDV, and AIV H9N2 were detected in twenty-five, seven, and seven flocks, respectively. IBV+AIV, IBV+NDV, and NDV+AIV coinfections were respectively detected in six, five, and one flocks. Most IBV infected flocks (84%) had been immunized with a live IBV-Mass vaccine. All NDV infected flocks and 14.2% of AIV infected flocks had been vaccinated. IBV genotyping showed a high prevalence of variant 2 (83.3%), followed by Mass-type (12.5%), and Q1-type (4.2%). Variant 2 IB viruses were widely distributed in the province and half of them were mostly similar to the ones that had been detected in northern neighboring province, Khorasan Razavi.

Single infection with variant 2 IBV was a major cause of the respiratory disease outbreak in which use of the Mass vaccine was probably not effective. The high coverage and multiple doses of vaccination against Newcastle disease possibly had reduced the prevalence of NDV. Considering the regional origin of IBV strains, strong biosecurity measures should be implemented and vaccination programs using appropriate vaccine strains should be used.

Bifidobacteriaceae family are gut microbiota that exhibit probiotic or health promoting effects on the host. Several studies have suggested that gut microbiota are quantitatively and qualitatively altered in patients with chronic kidney disease (CKD) and end-stage renal disease (ESRD). The present study aimed to assess the members of Bifidobacteriaceae family in fecal samples of patients with CKD and ESRD and compare them with non-CKD/ESRD patients to find any changes in their counts and diversions in these patients. Twenty fresh fecal samples from patients with CKD/ESRD and twenty from non-CKD/ESRD patients were examined. Whole DNA was extracted from fecal samples and the gut microbiota composition was analyzed by next generation sequencing (NGS). A total of 651 strains were identified from 40 fecal samples, 8 (1.23%) strains of which were identified as family Bifidobacteriaceae. The most abundant species in both control and disease groups were Bifidobacterium adolescentis and Bifidobacterium longum subsp. longum, and the least abundant species in the disease group was Bifidobacterium animalis subsp. lactis. There was no significant difference in the abundance of various species between the disease and control groups (p < 0.05). This study confirms that the members of the Bifidobacteriaceae family are not altered in patients with CKD/ESRD.

Leptospirosis is a common zoonosis disease with a high prevalence in the world and is recognized as an important public health drawback in both developing and developed countries owing to epidemics and increasing prevalence. Because of the high diversity of hosts that are capable of carrying the causative agent, this disease has an expansive geographical reach. Various environmental and social factors affect the spread and prevalence of the disease. The combination of epidemiology and Geospatial Information System plus using Ecological niche modeling provides the ability to identify areas at risk of disease, then predict the risk map of the disease for other regions by using relevant environment variables, and prevent and eventually eradicate the disease by conducting constructive activities such as increasing public awareness with education. In this study, using land use, environmental, and climate variables and taking advantage of the occurrences of the disease between 2009 and 2018, the risk level of Leptospirosis was modeled in three provinces of Gilan, Mazandaran, and Golestan based on ecological perspective. For modeling, highly correlated variables and also variables with high multicollinearity were identified and omitted. Because in ecological modeling regions to represent the absence of disease is required in addition to the presence and since these areas are not available, the second objective of this study was to evaluate the efficacy of different methods of generating pseudo-absence data in modeling leptospirosis. Finally, more accurate modeling of the prevalence of the disease in the northern provinces of the country can be obtained. Therefore, after selecting suitable variables for modeling, first, based on five methods (completely random generation of points in the study area, applying physical constraints with buffer at two radii of 5 and 10 km the generating points outside of designated buffer, applying environmental constraints by implementing two models of one-class support vector machine and BIOCLIM and generating points in unsuitable areas defined by these two models) pseudo-absence points representative of disease absence points in the study area were produced. Next, four models of Artificial Neural Network, Generalized Linear Model, Random Forest, and Gradient Boosting Machine were deployed to produce the disease risk in the study area. BIOMOD2 package in the R programming language was applied for modeling. The results showed that applying physical constraints with buffers yields the most reliable performance in comparison to the other three methods. Finally, the constructed model that performed best in TSS Statistics (with values of 0.76, 0.87, 0.84, 0.82 for Models of Artificial Neural Network, Generalized Linear Model, Random Forest, and Gradient Boosting Machine) was considered as the final output. Between all deployed models, Artificial Neural Network delivered the worst performance and had the most unstable results. Based on the risk-map of leptospirosis, central regions of Mazandaran and Gilan province, especially rural areas of Layl, Asalam, Eslam Abad, Chahar-deh, and Lafmejan have very high values of risk. Measures need to be made to reduce the high rate of Leptospirosis incidence in these regions. Furthermore, yearly precipitation was considered the most influential variable for the distribution of Leptospirosis.

β –Thalassaemia was first explained by Thomas Cooly as Cooly’s anaemia in 1925. The β- thalassaemias are hereditary autosomal disorders with decreased or absent β-globin chain synthesis. The most common genetic defects in β-thalassaemias are caused by point mutations, micro deletions or insertions within the β-globin gene.

In this research , 142 blood samples (64 from childrens hospital of Tabriz , 15 samples from Shahid Gazi hospital of Tabriz , 18 from Urumia and 45 samples from Aliasghar hospital of Ardebil) were taken from thalassaemic patients (who were previously diagnosed).Then 117 non-familial samples were selected . The DNA of the lymphocytes of blood samples was extracted by boiling and Proteinase K- SDS procedure, and mutations were detected by ARMS-PCR methods.

From the results obtained, eleven most common mutations,most of which were Mediterranean mutations were detected as follows IVS-I-110(G-A), IVS-I-1(G-A) ،IVS-I-5(G-C) ,Frameshift Codon 44 (-C,(codon5(-CT),IVS-1-6(T-C), IVS-I-25(-25bp del) ,Frameshift 8.9 (+G) ,IVS-II-1(G-A) ,Codon 39(C-T), Codon 30(G-C) the mutations of the samples were defined. The results showed that Frameshift 8.9 (+G), IVS-I-110 (G-A) ,IVS-II-I(G-A), IVS-I-5(G-C), IVS-I-1(G-A) , Frameshift Codon 44(-C) , codon5(-CT) , IVS-1-6(T-C) , IVS-I-25(-25bp del) with a frequency of 29.9%, 25.47%,17.83%, 7.00%, 6.36% , 6.63% , 3.8% , 2.5% , 0.63% represented the most common mutations in North - west Iran. No mutations in Codon 39(C-T) and Codon 30(G-C) were detected.

Cunclusion: 

The frequency of the same mutations in patients from North - West of Iran seems to be different as compared to other regions like Turkey, Pakistan, Lebanon and Fars province of Iran. The pattern of mutations in this region is more or less the same as in the Mediterranean region, but different from South west Asia and East Asia.