Association of Serum Ferritin with Risk of Anemia in Non-Diabetic Adolescents with Family History of Type 2 Diabetes Mellitus

Background: Controversy occurs in the relationship between serum ferritin levels, insulin resistance and risk of developing anemia in adolescents with family history of diabetes. Aims & Objectives: This study was designed to find out the association between serum ferritin levels with risk of developing anemia in non-diabetic adolescents with family history of type 2 diabetes mellitus (T2DM). Materials & Methods: A descriptive cross-sectional was conducted in a local medical institute of Lahore. Study included 50 non-diabetic, non-obese male / female adolescent with family history of type 2 diabetes mellitus and 50 healthy male/female non-obese adolescents without family history of T2DM considered as controls. Level of serum ferritin and serum insulin was estimated by Enzyme Linked Immunosorbent Assay. Blood glucose was estimated by auto-analyzer. Insulin resistance was calculated by HOMA-IR index and beta cell function was assessed by HOMA- beta index. Results: Levels of fasting blood sugar, insulin resistance were found to be increased with decreased level of serum ferritin and decrease beta cell function in both male as well as female cases as compared to controls. Negative correlation was found between serum ferritin and insulin resistance while a positive correlation was found between serum ferritin and beta cell function. Conclusions: Low level of serum ferritin is associated with reduced beta cell function and increased insulin resistance. This may increase the risk of developing iron deficiency anemia that can affect both immune system as well as increase susceptibility to infections.

Cloud Evolution Management Using Intelligent Cloud Ecosystem Ice

This research is primarily focused on cloud computing and the spread of this technology with an enormous speed, which is sufficient to explain the need and desirability of this technology, on the other hand, same distinction is also the main challenge i.e. unplanned growth of cloud computing and relevant technologies. In past we have seen the same issues with world wide web, in which we have expanded to such levels that a proper governance and maintenance became near to impossible. Rapidly expanding cloud technology with conventional and emerging services is a source of generating huge volumes of data that requires to be addressed for analytics and simply for suitable storage structure. Big data and IoT are two emerging challenges linked to cloud computing and these two areas are research focus of many scientists. In this research we have proposed an Intelligent Cloud Ecosystem (ICE) which is a blend of cloud technology along with artificial intelligence and cognitive science. We believe that an autonomous and self-evolving intelligent environment is the solution to emerging problems related to cloud technologies. To address the issues of Big data and IoT, our proposed model ICE is having the ability to analyze the heterogeneity and homogeneity knowledge structures wherein, machine learning and artificial neural networks have been proposed for semantic, property and feature analysis. Validation of the proposed model ICE is being done through algorithm development as well as by simulating the model in MATLAB. Results have shown the positive inclination towards ecosystem relevance with overall performance and interoperability i.e. these two parameters play a significant role in the sustenance of the intelligent ecosystem, while third parameter is dynamic which may change as per the requirement e.g. reliability, security, cost etc. This research concludes that cloud technology will work better in a comprehensive cloud ecosystem with better management and service parameters for users as well as for cloud service providers, also the issues of Big data and IoT are more manageable and observable in a cloud ecosystem.