Background: C-reactive protein is one of the most sensitive markers of systemic inflammation. Numerous studies have found that baseline levels of C-reactive protein are associated with risk of future myocardial infarction, stroke, peripheral vascular disease and cardiovascular death amongst apparently healthy populations. Aims & Objective:To find the association of hs-CRP and diabetes mellitus in the population of our region. Material and Methods: hs-CRP level in cases of diabetes was compared with that of non-diabetic healthy controls in our rural based tertiary care hospital. The analysis was done with 50 diabetic and 50 non-diabetic individuals. Anthropometric and biochemical parameters were studied to assess the association of hs-CRP with in diabetes mellitus. Results: Anthropometric parameters were found to be high in diabetic subjects compared with non-diabetic subjects. The high hs-CRP levels in diabetic subjects were also observed. Conclusion: Serum hs-CRP levels were positively related to anthropometric parameters. The relationship of hs-CRP with glycaemic control was studied with HbA1c, and it was positively correlated with hs-CRP. The results concluded that hs-CRP has strong association with diabetic individuals.

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It has been seen that Social network analysis is gaining its applicability in several areas like business, marketing, biology, disease modeling, and anti-terrorism. In this paper, we have discussed its practical application in the domain of computer network to identify distribution of computer viruses flowing through the network. To the best of our knowledge this is a novel idea and is based on the gSpan (Graph based substructure Pattern Mining) algorithm for identifying frequent pattern of viruses flowing in a particular region of connected nodes.This crusades make analysist enabled to deal with the problems and deploy more efficient antivirus in that region of nodes.

Mucopolysaccharidoses (MPS) are a group of genetic diseases and its diagnosis is a challenging task due to multiple differential diagnosis. We had combined clinical findings, radiological and ophthalmological features. Biochemical test for urine glycosaminoglycans (GAG) was done for confirmation of diagnosis in the patient. The case of Sanfilippo disease was characterized by slowly progressive, severe CNS involvement with mild somatic disease. Radiological features were suggestive of Sanfilippo disease and urine GAG test for MPS was positive in the case. With the clinical features we had multiple differential diagnoses. The radiological investigations minimized the list and the biochemical test confirmed GAG in urine. In this case the combination of clinical, radiological and biochemical findings confirmed the diagnosis of Sanfilippo disease.