A rare case of colonic diverticulum in a Bitch.

Abstract. Background: Colonic diverticulum is one of the rare findings in dogs characterized by an out-pouching of mucosal and submucosal layers through the defect in muscularis layer of the colon. Case description: A five years old intact female Labrador was presented with an anamnesis of dyschezia and tenesmus. Findings/treatment and outcome: Rectal examination was normal, and the survey radiograph showed an almost crescent shaped abnormal dilatation (10.52 cm × 6.21 cm) with gas and increased radiopaque material, dorsal to the urinary bladder and ventral to the descending colon suggesting fecal stasis. Ultrasonographic examination revealed gas-filled out-pouching with hyperechoic colon wall and acoustic shadowing. Exploratory celiotomy confirmed the diagnosis of colonic diverticulum, and diverticulectomy was performed. All four layers of the colonic wall were detected histopathologically in the biopsy sample and excluded neoplasia. The dog recovered uneventfully with no post-operative complications. Conclusion: This surgery produced an excellent resolution of clinical signs. To our knowledge, this is one of the few cases of colonic diverticulum reported in dogs.

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

In medical image processing, Brain tumor segmentation plays an important role. Early detection of these tumors is highly required to give Treatment of patients. The patient's life chances are improved by the early detection of it. The process of diagnosing the brain tumoursby the physicians is normally carried out using a manual way of segmentation. It is time consuming and a difficult one. To solve these problems, Enhanced Convolutional Neural Networks (ECNN) is proposed with loss function optimization by BAT algorithm for automatic segmentation method. The primary aim is to present optimization based MRIs image segmentation. Small kernels allow the design in a deep architecture. It has a positive consequence with respect to overfitting provided the lesser weights are assigned to the network. Skull stripping and image enhancement algorithms are used for pre-processing. The experimental result shows the better performance while comparing with the existing methods. The compared parameters are precision, recall and accuracy. In future, different selecting schemes can be adopted to improve the accuracy.

Spatial Big Data Analytics of Influenza Epidemic in Vellore, India.

The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009-2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013-2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013-2014.