Evaluation of Duodenal Eosinophil Count in Adult Patients with Functional Dyspepsia.

Several mechanisms have been proposed to explain the symptoms of functional dyspepsia but actual pathogenesis is still poorly understood. Recent studies support duodenal abnormality to be the most important causal link to explain symptoms and to understand abnormal pathophysiology of functional dyspepsia. The aim of this prospective observational study is to compare eosinophil count in duodenal mucosa between patients with functional dyspepsia and control subjects without dyspepsia and was done at the department of Gastroenterology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh from December 2015 to December 2016. Total 42 patients of functional dyspepsia based on Bangla validated version of ROME III criteria and 42 controls who were referred for upper gastrointestinal endoscopy for reasons other than dyspepsia were included. Biopsy specimens were collected from the second part (D2) of the duodenum of all participants. Eosinophil count was quantitatively evaluated by hematoxylin and eosin staining and expressed in numbers per 5 HPF. Significantly increased duodenal eosinophil count was found in functional dyspepsia group than non dyspeptic patients (22.78±08.78 vs. 14.90±10.70, p=0.001). Higher duodenal eosinophil count was found in patients with postprandial distress syndrome. Increased duodenal eosinophil count was found in patient of functional dyspepsia. It requires further large scale multicenter studies to establish duodenal eosinophilia as a biomarker of functional dyspepsia.

A framework for supervising lifestyle diseases using long-term activity monitoring.

Activity monitoring of a person for a long-term would be helpful for controlling lifestyle associated diseases. Such diseases are often linked with the way a person lives. An unhealthy and irregular standard of living influences the risk of such diseases in the later part of one's life. The symptoms and the initial signs of these diseases are common to the people with irregular lifestyle. In this paper, we propose a novel healthcare framework to manage lifestyle diseases using long-term activity monitoring. The framework recognizes the user's activities with the help of the sensed data in runtime and reports the irregular and unhealthy activity patterns to a doctor and a caregiver. The proposed framework is a hierarchical structure that consists of three modules: activity recognition, activity pattern generation and lifestyle disease prediction. We show that it is possible to assess the possibility of lifestyle diseases from the sensor data. We also show the viability of the proposed framework.

An intelligent tool for activity data collection.

Activity recognition systems using simple and ubiquitous sensors require a large variety of real-world sensor data for not only evaluating their performance but also training the systems for better functioning. However, a tremendous amount of effort is required to setup an environment for collecting such data. For example, expertise and resources are needed to design and install the sensors, controllers, network components, and middleware just to perform basic data collections. It is therefore desirable to have a data collection method that is inexpensive, flexible, user-friendly, and capable of providing large and diverse activity datasets. In this paper, we propose an intelligent activity data collection tool which has the ability to provide such datasets inexpensively without physically deploying the testbeds. It can be used as an inexpensive and alternative technique to collect human activity data. The tool provides a set of web interfaces to create a web-based activity data collection environment. It also provides a web-based experience sampling tool to take the user's activity input. The tool generates an activity log using its activity knowledge and the user-given inputs. The activity knowledge is mined from the web. We have performed two experiments to validate the tool's performance in producing reliable datasets.

A new data mining scheme using artificial neural networks.

Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems.