First Evidence of Fowl Adenovirus Induced Inclusion Body Hepatitis in Chicken in Bangladesh.

Background: The livestock sector contributes 1.90% to the GDP in Bangladesh during 2021-22. Poultry is one of the important subsectors struggling with diseases. Fowl adenoviruses (FAdVs) cause numerous diseases resulting in economic losses to the poultry industry worldwide. Several FAdV serotypes cause inclusion body hepatitis in chicken. Although FAdV infection was suspected, there was no confirmatory report from Bangladesh. The study was conducted to investigate the FAdV infection and antibodies in chicken. Methods: A total of 50 samples, each composed of liver and spleen, were collected from different chickens of Gazipur, Dinajpur, and Panchagarh district. Each location belongs to A, B, and C poultry zones of Bangladesh, respectively. Viruses were detected by real-time PCR and conventional PCR. Blood samples (n = 303) were collected at the beginning and after the recovery from infection and tested by indirect ELISA. Sequencing of PCR products was done for serotyping and phylogenetic analysis. Results: Clinical signs were observed including anorexia, drowsiness, ruffled feathers, reduced body weight, lack of uniformity, and high mortality (15-25%). Enlarged friable liver with yellow to tan color mottled with the focal soft area, fluid in pericardial sac, swollen and hemorrhagic kidneys, enlarged congested spleen and pancreas, etc. were found on postmortem examination. FAdVs were detected in 90% of the flocks except commercial layer flock from Dinajpur. Three serotypes, namely, 8b (70%), 11 (10%), and 5 (10%) were detected. Anti-FAdV antibody was detected in 80% flocks at the beginning of infection and in 90% of the flocks after recovery from infection. The antibody titer increases significantly (p < 0.05) after recovery from infection. Phylogenetic analysis revealed that the Bangladeshi FAdVs have close identity with viruses from Asia, Europe, and South and North America. Conclusions: These findings suggested that several introductions of FAdVs were taken place in Bangladesh. To combat the disease, vaccination along with maintenance of biosecurity is essential.

Myxovirus resistance (Mx) Gene Diversity in Avian Influenza Virus Infections.

Avian influenza viruses (AIVs) pose threats to animal and human health. Outbreaks from the highly pathogenic avian influenza virus (HPAIV) in indigenous chickens in Bangladesh are infrequent. This could be attributed to the Myxovirus resistance (Mx) gene. To determine the impact of Mx gene diversity on AIV infections in chicken, we assessed the Mx genes, AIVs, and anti-AIV antibodies. DNA from blood cells, serum, and cloacal swab samples was isolated from non-vaccinated indigenous chickens and vaccinated commercial chickens. Possible relationships were assessed using the general linear model (GLM) procedure. Three genotypes of the Mx gene were detected (the resistant AA type, the sensitive GG type, and the heterozygous AG type). The AA genotype (0.48) was more prevalent than the GG (0.19) and the AG (0.33) genotypes. The AA genotype was more prevalent in indigenous than in commercial chickens. A total of 17 hemagglutinating viruses were isolated from the 512 swab samples. AIVs were detected in two samples (2/512; 0.39%) and subtyped as H1N1, whereas Newcastle disease virus (NDV) was detected in the remaining samples. The viral infections did not lead to apparent symptoms. Anti-AIV antibodies were detected in 44.92% of the samples with levels ranging from 27.37% to 67.65% in indigenous chickens and from 26% to 87.5% in commercial chickens. The anti-AIV antibody was detected in 40.16%, 65.98%, and 39.77% of chickens with resistant, sensitive, and heterozygous genotypes, respectively. The genotypes showed significant association (p < 0.001) with the anti-AIV antibodies. The low AIV isolation rates and high antibody prevalence rates could indicate seroconversion resulting from exposure to the virus as it circulates. Results indicate that the resistant genotype of the Mx gene might not offer anti-AIV protection for chickens.

Association of Antibiotic Resistance Traits in Uropathogenic Escherichia coli (UPEC) Isolates.

Background: Antimicrobial resistance (AMR) is a global health problem which is constantly evolving and varies spatially and temporally. Resistance to a particular antibiotic may serve as a selection and coselection marker for the same or different antibiotic classes. Therefore, this cross-sectional study was conducted to predict the association of phenotypic and genotypic resistance traits in uropathogenic Escherichia coli (UPEC). Method: A total of 42 UPEC from 83 urine samples were investigated for the prevalence and association of phenotypic and genotypic AMR traits. Antibiogram profiling was carried out by Kirby-Bauer's disc diffusion method and AMR genes (ARGs) were detected by PCR. Result: UPECs were isolated from 50.60% (42/83) of the samples examined. Of these, 80.95% of cases were derived from females, and 38.10% of cases were found in the age group of 21-30 years. The isolates were shown to have a high frequency of resistance to tetracycline (92.86%), followed by sulfonamide (71.43%), ampicillin (52.38%), trimethoprim-sulfamethoxazole (47.62%), and 28.57% each to streptomycin, chloramphenicol, and erythromycin. The most prevalent antimicrobial resistance genes (ARGs) in these isolates were tet(A) (78.57%), tet(B) (76.19%), sul1 (61.91%), dfrA1 (35.71%), bla SHV (26.19%), cmlA (19.05%), and CITM, qnrA, and catA1 each at 11.91%. According to statistical analysis, ampicillin, sulfonamide, trimethoprim-sulfamethoxazole, and ciprofloxacin resistance were strongly correlated with the presence of bla SHV, sul1, dfrA1, and qnrA, respectively. Nonsignificant associations were observed between ciprofloxacin-tetracycline, sulfonamide-erythromycin pairs as well as between tet(A) and tet(B) genes. Besides, coselection was also assumed in the case of chloramphenicol resistance genes, namely, catA1 and cmlA. Conclusion: Both the phenotypic and genetic resistance traits were found in the UPEC isolates. Statistical association and coselection phenomena among AMR phenotypes and genotypes were also observed but required to be validated in a broad-scale study. However, these findings might have important implications for the development of an AMR prediction model to tackle future AMR outbreaks.

Unknown Object Detection Using a One-Class Support Vector Machine for a Cloud-Robot System.

Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communication and high computational power to deploy autonomous mobile robots for indoor applications. Deployment of usable indoor service robots requires uninterrupted movement and enhanced robot vision with a robust classification of objects and obstacles using vision sensor data in the indoor environment. However, state-of-the-art methods face degraded indoor object and obstacle recognition for multiobject vision frames and unknown objects in complex and dynamic environments. From these points of view, this paper proposes a new object segmentation model to separate objects from a multiobject robotic view-frame. In addition, we present a support vector data description (SVDD)-based one-class support vector machine for detecting unknown objects in an outlier detection fashion for the classification model. A cloud-based convolutional neural network (CNN) model with a SoftMax classifier is used for training and identification of objects in the environment, and an incremental learning method is introduced for adding unknown objects to the robot knowledge. A cloud-robot architecture is implemented using a Node-RED environment to validate the proposed model. A benchmarked object image dataset from an open resource repository and images captured from the lab environment were used to train the models. The proposed model showed good object detection and identification results. The performance of the model was compared with three state-of-the-art models and was found to outperform them. Moreover, the usability of the proposed system was enhanced by the unknown object detection, incremental learning, and cloud-based framework.

Isolation of multi-drug resistant Klebsiella sp. from bovine mastitis samples in Rangpur, Bangladesh.

OBJECTIVE: The objective of this study was to identify the multi-drug resistance (MDR) Klebsiella sp. from mastitis milk samples. MATERIALS AND METHODS: In the current research, 48 clinical mastitis milk samples were collected from Rangpur division, Bangladesh. Confirmation of bovine mastitis (BM) was done by the California Mastitis Test (CMT). All the CMT positive isolates were subjected for the identification of Klebsiella sp. using through a series of cultural and biochemical tests. MDR Klebsiella sp. isolates were determined using the disk diffusion method, and minimum inhibitory zones were measured by following Clinical and Laboratory Standards Institute. MDR patterns of the isolates were also subjected to study by using housefly (Musca domestica). RESULTS: Among the isolates, 62.5% (n = 30/48) revealed the presence of Klebsiella sp. Eight antimicrobial agents including Amoxicillin, Novobiocin, Erythromycin, Vancomycin, Cephradine, Tetracycline, Bacitracin, Methicillin, and housefly (M. domestica) showed complete resistance to Klebsiella sp. On the other hand, Chloramphenicol, Gentamicin, Ciprofloxacin, Azithromycin, Norfloxacin, Levofloxacin, and Nalidixic acid showed sensitivity. CONCLUSION: This study helps to treat BM with effective antibiotics and helps in an epidemiological study in Rangpur division as well as helps to create public health awareness.

Seroprevalence of Mycoplasma gallisepticum antibody by ELISA and serum plate agglutination test of laying chicken.

AIM: Mycoplasma gallisepticum (MG) is important avian pathogens responsible for chronic respiratory diseases of chicken and turkeys, which result in large economic loss for the poultry industry. The objectives of this study were determination of seroprevalence of MG antibody of commercial layer chicken at laying period in selected areas of Bangladesh. MATERIALS AND METHODS: A total of 563 blood samples were collected randomly from selected commercial layer chickens at laying period during the period from July to December, 2013. Indirect enzyme linked immunosorbent assay (iELISA) and serum plate agglutination (SPA) test were performed to detect the presence of antibodies against MG. RESULTS: Of 563 samples, 64.47% and 56.13% showed an overall prevalence of MG antibodies in iELISA and SPA test respectively. Prevalence of MG was recorded the highest (69.63%) at 50-55 weeks of age compared with lowest (53.26%) at 56-61 weeks of age (p<0.05). Significant (p<0.05) effect of breed were observed in the seroprevalence of MG infection in layer birds in the present study. The overall, 68.77%, 63.74% and 59.37% prevalence were found respectively in sonali, ISA Brown and White leg horn. The prevalence of MG antibodies was the highest (70.13%) in December followed by November (68%), October (65.67%), August (63.46%), September (58.54%) and July (51.78%) month. The seroprevalence of MG antibodies was higher (69.63%) in most of the large flocks and lower (56.82%) in small flocks. CONCLUSION: Therefore, might be suggested that the commercial layer farms should be routinely checked to monitor MG infection and the reactor birds should be culled since MG organism has the potential to transmit vertically. The correlation between MG antibody in month and flock size was not significant (p=0.359 and p=0.868, respectively).