Study of dengue outbreak in north west zone of Rajasthan, India

Background: Dengue is one of the most important mosquito-borne viral disease globally. The virus is the member of flavivirus group which typically is a single stranded RNA virus. It is 2nd most common arthropod borne disease in India. Due to its atypical presentation often, dengue missed out as a differential diagnosis. High clinical suspicion and proper investigation help in early diagnosis of dengue and its complications.Methods: A total of 200 patients were selected to be a part of study after applying inclusion and exclusion criteria. Only those patients were included in the study who had classical features of dengue- fever with chills, body ache, headache and thrombocytopenia and had a positive serology against dengue virus. Patients who had malaria, enteric fever, and negative serology were excluded from the study. Other causes of pancreatitis, pneumonitis, ascitis, cholangitis, pleural effusion and thrombocytopenia are rolled out. All patients were subjected to a detailed history and a thorough clinical examination. A complete blood count, liver function tests, renal function tests, chest X-ray and USG abdomen were also done.Results: Among 200 patients diagnosed as dengue fever,106 were male and 94 female. 78% patient were from urban and majority were from 20-30 years age group. Average duration of stay in hospital is 3.5 days. Along with fever and malaise, pain abdomen, bleeding diathesis, itching, cough were the major complaints in decreasing order. Different findings in the investigations are: Mean WBC counts - 4251, mean platelet counts - 41831, mean hematocret - 41.8, mean MPV- 8.55, number of patients with deranged ALT/AST- 88(44%). In USG ascitis and edematous gall bladder were the major findings followed by hepatomegaly, splenomegaly and pleural effusion. Number of patients required platelet transfusion were 60. Among these 60 patients average number of RDP transfused is 2 units.Conclusions: Present study concludes that clinical vigilance about various type of presentations is important as timely recognition can influence outcome and may prevent compilations

Magnetic resonance imaging in the diagnosis of lumbar canal stenosis in Indian patients

Introduction: Magnetic resonance imaging (MRI) has become the choice of imaging modality for lumbar canal stenosis (LCS) due to limitations and radiation risks of computed tomography (CT) and spinal radiography. The radiological criteria for diagnosis of LCS are still ambiguous. Aim of this study is to find out the radiological dimensions on MRI of lumbar spinal canal in Indian patients and the critical dimensions at which the symptoms occur. Materials and Methods: A cross-sectional study was conducted in ESI Hospital, New Delhi from July 2011 to 2013. Two study groups were studied, the symptomatic LCS group, consisted of 30 individuals of either sex in age group of 45-65 years. The control group consisted of 30 asymptomatic age matched individuals. MRI scans were performed on 1.5 Tesla scanner. Dimensions of lumbar canal at all the levels (L1-L5) of lumbar vertebra of 60 patients were measured. Results: In our study, in symptomatic group, narrowest mid-sagittal diameter antero-posterior (mean 10.61) was at L5-S1 level. The interligamentous diameter (ILD) showed no significant difference between the two groups. Lateral recess depths showed a significant difference between the two groups at all levels except L1 on right side and L1 and L2 on left side. Critical canal dimension was found to be 11.13 mm. Conclusion: MRI can effectively evaluate the lumbar canal stenosis. The critical canal dimensions at which symptoms of stenosis appear were 11.13

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

<div><p>Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004504#pcbi.1004504.s002" target="_blank">S1 Data</a>) accompanying this manuscript.</p></div

Performance of explaining gene expression in E2 vs. control treated MCF-7 cells using core regulators identified by various ranking strategies.

<p>Three different mathematical or AI models were used for modeling gene expression: linear regression (LR), support vector machines (classification, SVC, and regression, SVR) and principal component analysis (PCA). Performance was measured as area under the ROC curve (AUROC) for real-valued estimators and using Matthew’s correlation coefficient (MCC) for binary classifiers in 5-fold cross validation.</p

Gene expression classification in the MCF-7 estrogen response GRN using various selections of regulatory nodes based on their core numbers, K, in K-core hierarchy.

<p>In the top half of the table the innermost core regulators (K ≤ 2) are always included and the cumulative effect of adding further core regulators is measured. In the bottom half of the table the innermost core regulators (K ≤ 2) are excluded in order to measure the individual contributions of regulators at various core levels. Classification accuracy is reported in terms of area under the ROC curve (AUROC) for real valued classifiers (LR, SVR and PCA) and Matthew’s correlation coefficient (MCC) for binary classifiers (SVC).</p

Literature validation scores for various ranking strategies (lower scores are better).

<p>Literature validation scores for various ranking strategies (lower scores are better).</p