Cross-sectional study and genotyping of rotavirus-A infections in ruminants in Kuwait.

BACKGROUND: Group A rotaviruses (RVA) are zoonotic pathogens responsible for acute enteritis in human and neonatal ruminants. This research aimed to determine the prevalence of RVA in ruminants (cattle, sheep, and goats) and investigate the circulating RVA genotypes in these animals in Kuwait. We conducted a cross-sectional study to detect RVA in ruminants, using an immunochromatography test (IC), direct sandwich ELISA test, and real-time RT-PCR (RT-qPCR) assay using fecal samples. RESULTS: A total of 400 cattle, 334 sheep, and 222 goats were examined. The prevalence of RVA was 5.3, 1.2, and 2.3%, respectively, using IC. The ELISA test detected RVA from 4.3% of cattle, 0.9% of sheep, and 1.8% of goats. There was a significant association between the occurrence of diarrhea and the presence of RVA in bovine fecal samples (p-value = 0.0022), while no statistical association between diarrhea and the presence of RVA in fecal samples of sheep and goats was observed (p-value = 0.7250; p-value = 0.4499, respectively). Twenty-three of the IC-positive samples (17 from cattle, two from sheep, and four from goats) were tested using a RT-qPCR RVA detection assay targeting the NSP3 gene. The results showed that 21 of 23 IC-positive samples tested positive by RT-qPCR. Detection of RVA genotypes revealed that G10P[11] was the predominant strain in cattle (58.8%), followed by G8P[1] (11.7%). One sheep sample was genotyped as G8P[1]. In addition, G6P[1] and G6P[14] were detected in goat samples. CONCLUSION: The present study revealed that the IC was more sensitive in detecting RVA antigen in fecal samples than the ELISA test. A higher occurrence of RVA infection was observed in cattle than in sheep and goats. This study suggests that RVA might be a risk factor of diarrhea in bovine calves less than 2 weeks old. This research also demonstrates the circulation of RVA in sheep and goat populations in Kuwait. Finally, the G10P[11] RVA genotype was the most prevalent genotype identified from cattle samples.

Seroprevalence of camel brucellosis (Camelus dromedarius) in Somaliland.

The present study was delineated to investigate the prevalence and risk factors of camel brucellosis in Northern Somalia (Somaliland). The study was carried out at three main districts of camel-rearing regions of Somaliland (Awdal, Waqoyi Galbed and Togdheer) in the period from July to November, 2008. A total of 1246 camel blood sera were randomly collected from 42 sporadic small scale camel herds. Two serological tests were used to screen all serum samples, Rose Bengal Plate Test (RBPT) and indirect ELISA (I-ELISA). Multivariate logistic regression was constructed to study the risk factors associated with Brucella seropositive cases. The overall prevalence of camel brucellosis in districts under investigation was 3.9% by RBPT and 3.1% by (I-ELISA). Multivariate logistic regression on animal level showed that locality (P < 0.05; OR: 6.254; CI, 1.186-32.976), herd size (P < 0.001; OR: 5.493; CI, 2.956-10-207), rearing with other ruminants (P < 0.001; OR: 12.433; CI, 3.957-39.060), and contact with other camels (P < 0.05; OR: 5.311; CI, 1.093-25.800) were the potential risk factors. However, herd size (P < 0.05; OR: 5.425; CI, 1.181-24.932), and rearing with other ruminants (P < 0.05; OR: 20.466; CI, 1.456-28.638) were recorded as risk factors on the herd level. The results of the present investigation indicate that the Brucella spp. exists within the camel herds in Somaliland. Further studies need to be done on Brucella infection in the other ruminants to determine which measure should be followed for control of brucellosis.

Computer vision approach for ultrasound Doppler angle estimation.

Doppler ultrasound is an important noninvasive diagnostic tool for cardiovascular diseases. Modern ultrasound imaging systems utilize spectral Doppler techniques for quantitative evaluation of blood flow velocities, and these measurements play a crucial rule in the diagnosis and grading of arterial stenosis. One drawback of Doppler-based blood flow quantification is that the operator has to manually specify the angle between the Doppler ultrasound beam and the vessel orientation, which is called the Doppler angle, in order to calculate flow velocities. In this paper, we will describe a computer vision approach to automate the Doppler angle estimation. Our approach starts with the segmentation of blood vessels in ultrasound color Doppler images. The segmentation step is followed by an estimation technique for the Doppler angle based on a skeleton representation of the segmented vessel. We conducted preliminary clinical experiments to evaluate the agreement between the expert operator's angle specification and the new automated method. Statistical regression analysis showed strong agreement between the manual and automated methods. We hypothesize that the automation of the Doppler angle will enhance the workflow of the ultrasound Doppler exam and achieve more standardized clinical outcome.