A new class of copper(II) nanohybrid solids, LCu(CH(3)COO)(2) and LCuCl(2), have been synthesized and characterized by transmission electron microscopy, dynamic light scattering, and IR spectroscopy, and have been found to be capped by a bis(benzimidazole) diamide ligand (L). The particle sizes of these nanohybrid solids were found to be in the ranges 5-10 and 60-70 nm, respectively. These nanohybrid solids were evaluated for their in vitro antimalarial activity against a chloroquine-sensitive isolate of Plasmodium falciparum (MRC 2). The interactions between these nanohybrid solids and plasmepsin II (an aspartic protease and a plausible novel target for antimalarial drug development), which is believed to be essential for hemoglobin degradation by the parasite, have been assayed by UV-vis spectroscopy and inhibition kinetics using Lineweaver-Burk plots. Our results suggest that these two compounds have antimalarial activities, and the IC(50) values (0.025-0.032 microg/ml) are similar to the IC(50) value of the standard drug chloroquine used in the bioassay. Lineweaver-Burk plots for inhibition of plasmepsin II by LCu(CH(3)COO)(2) and LCuCl(2) show that the inhibition is competitive with respect to the substrate. The inhibition constants of LCu(CH(3)COO)(2) and LCuCl(2) were found to be 10 and 13 microM, respectively. The IC(50) values for inhibition of plasmepsin II by LCu(CH(3)COO)(2) and LCuCl(2) were found to be 14 and 17 microM, respectively. Copper(II) metal capped by a benzimidazole group, which resembles the histidine group of copper proteins (galactose oxidase, beta-hydroxylase), could provide a suitable anchoring site on the nanosurface and thus could be useful for inhibition of target enzymes via binding to the S1/S3 pocket of the enzyme hydrophobically. Both copper(II) nanohybrid solids were found to be nontoxic against human hepatocellular carcinoma cells and were highly selective for plasmepsin II versus human cathepsin D. The pivotal mechanism of antimalarial activity of these compounds via plasmepsin II inhibition in the P. falciparum malaria parasite is demonstrated.
Background
There is an urgent need to understand the key events driving pathogenesis of severe COVID-19 disease, so that precise treatment can be instituted. In this respect NETosis is gaining increased attention in the scientific community, as an important pathological process contributing to mortality. We sought to test if indeed there exists robust evidence of NETosis in multiple transcriptomic data sets from human subjects with severe COVID-19 disease. Gene set enrichment analysis was performed to test for up-regulation of gene set functional in NETosis in the blood of patients with COVID-19 illness.
Results
Blood gene expression functional in NETosis increased with severity of illness, showed negative correlation with blood oxygen saturation, and was validated in the lung of COVID-19 non-survivors. Temporal expression of IL-6 was compared between severe and moderate illness with COVID-19. Unsupervised clustering was performed to reveal co-expression of IL-6 with complement genes. In severe COVID-19 illness, there is transcriptional evidence of activation of NETosis, complement and coagulation cascade, and negative correlation between NETosis and respiratory function (oxygen saturation). An early spike in IL-6 is observed in severe COVID-19 illness that is correlated with complement activation.
Conclusions
Based on the transcriptional dynamics of IL-6 expression and its downstream effect on complement activation, we constructed a model that links early spike in IL-6 level with persistent and self-perpetuating complement activation, NETosis, immunothrombosis and respiratory dysfunction. Our model supports the early initiation of anti-IL6 therapy in severe COVID-19 disease before the life-threatening complications of the disease can perpetuate themselves autonomously.
In this short paper, network structural measure called centrality measure based mathematical approach is used for detection of malicious nodes in twitter social network. One of the objectives in analysing social networks is to detect malicious nodes which show anomaly behaviours in social networks. There are different approaches for anomaly detection in social networks such as opinion mining methods, behavioural methods, network structural approach etc. Centrality measure, a graph theoretical method related to social network structure, can be used to categorize a node either as popular and influential or as non-influential and anomalous node. Using this approach, we have analyzed twitter social network to remove anomalous nodes from the nodes-edges twitter data set. Thus removal of these kinds of nodes which are not important for information diffusion in the social network, makes the social network clean & speedy in fast information propagation.
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