High incidence of common femoral Deep Vein Thrombosis in critically ill Covid19 patients.

Objective: To estimate the incidence and prevalence of deep venous thrombosis, and to evaluate the discriminative capacity of D-dimer in its diagnosis. METHODS: The prospective, observational study was conducted at the critical care unit of a tertiary care hospital in Pakistan from February to September 2021 and comprised consecutively admitted adult critically ill patients who were receiving therapeutic-dose anticoagulation therapy. All patients were screened on day one for deep venous thrombosis by colour doppler and compression ultrasonography. Patients who did not have deep venous thrombosis on the first scan were followed every 72 hours. Data was analysed using SPSS 26. RESULTS: Of the 142 patients, 99(69.7%) were male and 43(30.3%) were female. The overall mean age was 53.20+/-13.3 years. On the first scan, 25(17.6%) patients had deep venous thrombosis. Of the remaining 117 patients, 78(68.4%) were followed every 72 hours, and 23(29.48%) of them developed deep venous thrombosis. The most common site for DVT was the common femoral vein 46(95.8%) and most deep venous thrombosis cases were unilateral 28(58.33%). D-dimer levels showed no discriminative capacity for diagnosis of deep venous thrombosis (p=0.79). There were no significant risk factors for the development of deep venous thrombosis. Conclusion: There was a high incidence and prevalence of deep venous thrombosis despite therapeutic-dose anticoagulation therapy. The most common affected site was the common femoral vein and most deep venous thrombosis were unilateral. D-dimer levels had no discriminative capacity for the diagnosis of deep venous thrombosis DVT.

Network Theoretical Approach to Explore Factors Affecting Signal Propagation and Stability in Dementia's Protein-Protein Interaction Network.

Dementia-a syndrome affecting human cognition-is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer's disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a whole is required. In this study, we try to find the high-impact core regulating processes and factors involved in dementia's protein-protein interaction network. We also explore various aspects related to its stability and signal propagation. Using gene interaction databases such as STRING and GeneMANIA, a principal dementia network (PDN) consisting of 881 genes and 59,085 interactions was achieved. It was assortative in nature with hierarchical, scale-free topology enriched in various gene ontology (GO) categories and KEGG pathways, such as negative and positive regulation of apoptotic processes, macroautophagy, aging, response to drug, protein binding, etc. Using a clustering algorithm (Louvain method of modularity maximization) iteratively, we found a number of communities at different levels of hierarchy in PDN consisting of 95 "motif-localized hubs", out of which, 7 were present at deepest level and hence were key regulators (KRs) of PDN (HSP90AA1, HSP90AB1, EGFR, FYN, JUN, CELF2 and CTNNA3). In order to explore aspects of network's resilience, a knockout (of motif-localized hubs) experiment was carried out. It changed the network's topology from a hierarchal scale-free topology to scale-free, where independent clusters exhibited greater control. Additionally, network experiments on interaction of druggable genome and motif-localized hubs were carried out where UBC, EGFR, APP, CTNNB1, NTRK1, FN1, HSP90AA1, MDM2, VCP, CTNNA1 and GRB2 were identified as hubs in the resultant network (RN). We finally concluded that stability and resilience of PDN highly relies on motif-localized hubs (especially those present at deeper levels), making them important therapeutic intervention candidates. HSP90AA1, involved in heat shock response (and its master regulator, i.e., HSF1), and EGFR are most important genes in pathology of dementia apart from KRs, given their presence as KRs as well as hubs in RN.