Gene regulatory network reveals oxidative stress as the underlying molecular mechanism of type 2 diabetes and hypertension

<p>Abstract</p> <p>Background</p> <p>The prevalence of diabetes is increasing worldwide. It has been long known that increased rates of inflammatory diseases, such as obesity (OBS), hypertension (HT) and cardiovascular diseases (CVD) are highly associated with type 2 diabetes (T2D). T2D and/or OBS can develop independently, due to genetic, behavioral or lifestyle-related variables but both lead to oxidative stress generation. The underlying mechanisms by which theses complications arise and manifest together remain poorly understood. Protein-protein interactions regulate nearly every living process. Availability of high-throughput genomic data has enabled unprecedented views of gene and protein co-expression, co-regulations and interactions in cellular systems.</p> <p>Methods</p> <p>The present work, applied a systems biology approach to develop gene interaction network models, comprised of high throughput genomic and PPI data for T2D. The genes differentially regulated through T2D were 'mined' and their 'wirings' were studied to get a more complete understanding of the overall gene network topology and their role in disease progression.</p> <p>Results</p> <p>By analyzing the genes related to T2D, HT and OBS, a highly regulated gene-disease integrated network model has been developed that provides useful functional linkages among groups of genes and thus addressing how different inflammatory diseases are connected and propagated at genetic level. Based on the investigations around the 'hubs' that provided more meaningful insights about the cross-talk within gene-disease networks in terms of disease phenotype association with oxidative stress and inflammation, a hypothetical co-regulation disease mechanism model been proposed. The results from this study revealed that the oxidative stress mediated regulation cascade is the common mechanistic link among the pathogenesis of T2D, HT and other inflammatory diseases such as OBS.</p> <p>Conclusion</p> <p>The findings provide a novel comprehensive approach for understanding the pathogenesis of various co-associated chronic inflammatory diseases by combining the power of pathway analysis with gene regulatory network evaluation.</p

Development of voice recognition for student attendance

Abstract- Development of voice recognition for student attendance system is beneficial in many ways. It helps the lecturer in administrative the attendance of their student with efficiency. This is because students always cheat with their attendancy by signing on behalf of their friend who did not attend class. With this project, voice biometric is used as a medium for student to mark their attendance. Cheating among students will be prevented because like fingerprints, each voice is different. The objectives of this project are to study and understand the properties understand the properties of speaker recognition and to analyze the effectiveness of using Euclidean distance feature for speaker recognition. Databases of 26 volunteers were collected consisting of only male. The report result is tabulated. Three types of analysis were done, first same train is used as test data reported 100% correct. The remaining two analyses used different test data recording. Volunteers use the same sentence as test data reported 76.92% correct. Lastly volunteers used their name and the correct percentage is 46.15%