Topology Driven Analysis of Protein - Protein Interactome for Prioritizing Key Comorbid Genes via Sub Graph Based Average Path Length Centrality.

In gene-based therapies, local perturbations associated with one disease can lead to comorbidity as it influences the pathways involved with the other diseases. The key genes orchestrating the common biological mechanisms are need to be prioritized for addressing the challenges introduced by the cross talks between disease modules. Here, a local centrality measure named Sub graph based Average Path length Double Specific Betweenness centrality (SAPDSB) for prioritizing the comorbid genes via Protein-Protein Interaction Network (PPIN) analysis is presented. This approach can be used to identify putative biomarkers which can be repurposed for the management of comorbidity. Proposed network based topological measure is designed specifically to prioritize the comorbid genes that are most likely to be present in the overlap of disease modules. In order to attain this, the estimated average path length of the seed network which holds Protein-Protein Interactions (PPIs) of the disease genes is exploited. Prioritized comorbid genes are further pruned using centrality-based cut-off values and specificity scores. The biological significance of the resultant genes is corroborated with connectivity analysis using leave-one-out method, pathway enrichment analysis and a comparative analysis using single disease-based gene prioritization tools. For performance analysis, proposed approach is tested using case studies involving common diseases and rare neurodegenerative diseases. For case study1, diseases such as Diabetes, Carcinoma and Alzheimer's are considered in a pairwise manner while for case study2, Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) are considered. As outcome, prioritized candidate genes and biological pathways associated with respective disease pairs have been found. The associations from top 10 candidate genes in different disease pair combinations of Diabetes-Carcinoma-Alzheimer's revealed common genes like CREBBP, TP53, HSP90AA1 and the common pathway namely p53 pathway feedback loops 2. Out of the pathways retrieved from the top 10 genes associated with ALS-SMA disease pair, 60% of unique pathways are found to be leading to both diseases and its comorbidities. Comparative analysis of the proposed method with recent similar approach also reported a clear degree of benefits in performance.

Intravenous cannula induced phlebitis in a tertiary care referral hospital: A prospective observational study with implication from patient's feedback system.

BACKGROUND: Medicines delivered directly to systemic circulation have saved many lives from life-threatening conditions, but also can impart undesirable effects. MATERIALS AND METHODS: A prospective observational study was performed for 10 months in the tertiary care hospital to identify and evaluate cannula induced phlebitis in our study population. The data collection form retrieved demographic details, diseases and cannulation particulars of each patient. Moreover, a patient feedback questionnaire (Cronbach's alpha=0.70) retrieved their concerns toward cannulation. RESULTS: Phlebitis was identified in 96 patients out of 146 subjects enrolled in the study; 52% had the first sign of phlebitis. Female patients were more prone, and the complication occurred irrespective of age (p>0.05). On the other hand, those on IV cefoperazone-sulbactam (n=13, 13.5%) followed by amoxicillin clavulanic acid (n=6, 6%) had significant cannulation complication (p<0.01). The cannula indwells time (p=0.001) and vein assessment (p=0.001) were statistically associated with incidence of phlebitis. Half of our samples had pain lasting about five minutes (χ2=9.2, p<0.05). Nevertheless, limited patients (n = 35, 36.5%) were prescribed topical Heparin Benzyl Nitrate, and none preferred to self-medicate nor opted for other home remedies. CONCLUSIONS: The study depicted high prevalence of phlebitis factored in by poor vein assessment and increase in indwelling time. We recommend proper awareness with on-site skill improvement program for health professionals on administration techniques and monitoring principles in order to lower cannulation related complications.

Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways.

Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.