An Efficient Bet-GCN Approach for Link Prediction.

The task of determining whether or not a link will exist between two entities, given the current position of the network, is called link prediction. The study of predicting and analyzing links between entities in a network is emerging as one of the most interesting research areas to explore. In the field of social network analysis, finding mutual friends, predicting the friendship status between two network individuals in the near future, etc., contributes significantly to a better understanding of the underlying network dynamics. The concept has many applications in biological networks, such as finding possible connections (possible interactions) between genes and predicting protein-protein interactions. Apart from these, the concept has applications in many other areas of network science. Exploration based on Graph Neural Networks (GNNs) to accomplish such tasks is another focus that is attracting a lot of attention these days. These approaches leverage the strength of the structural information of the network along with the properties of the nodes to make efficient predictions and classifications. In this work, we propose a network centrality based approach combined with Graph Convolution Networks (GCNs) to predict the connections between network nodes. We propose an idea to select training nodes for the model based on high edge betweenness centrality, which improves the prediction accuracy of the model. The study was conducted using three benchmark networks: CORA, Citeseer, and PubMed. The prediction accuracies for these networks are: 95.08%, 95.07%, and 95.3%. The performance of the model is comprehensive and comparable to the other prior art methods and studies. Moreover, the performance of the model is evaluated with 90.13% for WikiCS and 87.7% for Amazon Product network to show the generalizability of the model. The paper discusses in detail the reason for the improved predictive ability of the model both theoretically and experimentally. Our results are generalizable and our model has the potential to provide good results for link prediction tasks in any domain

Fizikokemijska karakterizacija čvrstih disperzijskih sustava tadalafila s poloksamerom 407

Dissolution behaviour of a poorly water-soluble drug, tadalafil, from its solid dispersion systems with poloxamer 407 has been investigated. Solid dispersion systems of tadalafil were prepared with poloxamer 407 in 1:0.5, 1:1.5 and 1:2.5 ratios using the melting method. Characterization of binary systems with FTIR and powder XRPD studies demonstrated the presence of strong hydrogen bonding interactions, a significant decrease in crystallinity and the possibility of existence of amorphous entities of the drug. In the binary systems tested, 1:0.5 proportion of tadalafil/poloxamer 407 showed rapid dissolution of tadalafil (DE30 70.9 ± 3.6 %). In contrast, higher proportions of poloxamer 407 (1:1.5 and 1:2.5) offered no advantage towards dissolution enhancement of the drug from corresponding binary systems indicating altered rheological characteristics of the polymer, at its higher concentration, which might have retarded the release rate of tadalafil.U radu je ispitivano oslobađanje u vodi teško topljivog lijeka tadalafila iz čvrstih disperzijskih sustava. Ti sustavi pripravljeni su s poloksamerom 407 u omjeru lijeka i polimera 1:0,5, 1:1,5 i 1:2,5, koristeći metodu taljenja. Karakterizacija binarnih sustava s FTIR i rendgenskom difrakcijom praha XRD ukazuje na prisutnost snažnih vodikovih veza, značajno smanjenje kristaliničnosti i moguću prisutnost amorfnog lijeka. Iz binarnog sustava tadalafil/poloksamer 1:0,5 oslobađanje ljekovite tvari je brzo (DE30 70,9 ± 3,6 %). Nasuprot tome, iz pripravaka s višim omjerima lijeka i polimera (1:1,5 i 1:2,5) oslobađanje ljekovite tvari nije povećano. Usporavanje oslobađanja tadalafila moglo bi biti posljedicom promjene reoloških svojstava polimera pri višim koncentracijama

CPP-ZFN: A potential DNA-targeting anti-malarial drug

<p>Abstract</p> <p>Background</p> <p>Multidrug-resistant <it>Plasmodium </it>is of major concern today. Effective vaccines or successful applications of RNAi-based strategies for the treatment of malaria are currently unavailable. An unexplored area in the field of malaria research is the development of DNA-targeting drugs that can specifically interact with parasitic DNA and introduce deleterious changes, leading to loss of vital genome function and parasite death.</p> <p>Presentation of the hypothesis</p> <p>Advances in the development of zinc finger nuclease (ZFN) with engineered DNA recognition domains allow us to design and develop nuclease of high target sequence specificity with a mega recognition site that typically occurs only once in the genome. Moreover, cell-penetrating peptides (CPP) can cross the cell plasma membrane and deliver conjugated protein, nucleic acid, or any other cargo to the cytoplasm, nucleus, or mitochondria. This article proposes that a drug from the combination of the CPP and ZFN systems can effectively enter the intracellular parasite, introduce deleterious changes in its genome, and eliminate the parasite from the infected cells.</p> <p>Testing the hypothesis</p> <p>Availability of a DNA-binding motif for more than 45 triplets and its modular nature, with freedom to change number of fingers in a ZFN, makes development of customized ZFN against diverse target DNA sequence of any gene feasible. Since the <it>Plasmodium </it>genome is highly AT rich, there is considerable sequence site diversity even for the structurally and functionally conserved enzymes between <it>Plasmodium </it>and humans. CPP can be used to deliver ZFN to the intracellular nucleus of the parasite. Signal-peptide-based heterologous protein translocation to <it>Plasmodium</it>-infected RBCs (iRBCs) and different <it>Plasmodium </it>organelles have been achieved. With successful fusion of CPP with mitochondrial- and nuclear-targeting peptides, fusion of CPP with 1 more <it>Plasmodium </it>cell membrane translocation peptide seems achievable.</p> <p>Implications of the hypothesis</p> <p>Targeting of the <it>Plasmodium </it>genome using ZFN has great potential for the development of anti-malarial drugs. It allows the development of a single drug against all malarial infections, including multidrug-resistant strains. Availability of multiple ZFN target sites in a single gene will provide alternative drug target sites to combat the development of resistance in the future.</p

Engineering a Multi Epitope Vaccine Against SARS-CoV-2 By Exploiting Its Non Structural and Structural Proteins

Abstract SARS-CoV-2, the causative agent behind the ongoing pandemic exhibits an enhanced potential for infection when compared to its related family members- the SARS-CoV and MERS-CoV; which have caused similar disease outbreaks in the past. The severity of the global health burden, increasing mortality rate and the emergent economic crisis urgently demands the development of next- generation vaccines. Amongst such emergent next generation vaccines are the multi-epitope subunit vaccines, which hold promise in combating deadly pathogens. In this study we have exploited immunoinformatics applications to delineate a vaccine candidate possessing multiple B and T cells epitopes utilizing the SARS-CoV-2 non structural and structural proteins. The antigenicity potential, safety, structural stability and the production feasibility of the designed construct was evaluated computationally. Furthermore, due to the known crucial interactions of human TLR-3 immune receptor with SARS-CoV, the vaccine construct was examined for its binding efficiency using molecular docking and molecular dynamics simulation studies, which resulted in strong and stable interactions. Finally, the immune simulation studies suggested an effective immune response on vaccine administration. Overall, the immunoinformatics analysis advocates that the purposed vaccine candidate is safe and immunogenic and therefore can be pushed as a lead for in vitro and in vivo investigations.</jats:p

An Efficient Bet-GCN Approach for Link Prediction

The task of determining whether or not a link will exist between two entities, given the current position of the network, is called link prediction. The study of predicting and analyzing links between entities in a network is emerging as one of the most interesting research areas to explore. In the field of social network analysis, finding mutual friends, predicting the friendship status between two network individuals in the near future, etc., contributes significantly to a better understanding of the underlying network dynamics. The concept has many applications in biological networks, such as finding possible connections (possible interactions) between genes and predicting protein-protein interactions. Apart from these, the concept has applications in many other areas of network science. Exploration based on Graph Neural Networks (GNNs) to accomplish such tasks is another focus that is attracting a lot of attention these days. These approaches leverage the strength of the structural information of the network along with the properties of the nodes to make efficient predictions and classifications. In this work, we propose a network centrality based approach combined with Graph Convolution Networks (GCNs) to predict the connections between network nodes. We propose an idea to select training nodes for the model based on high edge betweenness centrality, which improves the prediction accuracy of the model. The study was conducted using three benchmark networks: CORA, Citeseer, and PubMed. The prediction accuracies for these networks are: 95.08%, 95.07%, and 95.3%. The performance of the model is comprehensive and comparable to the other prior art methods and studies. Moreover, the performance of the model is evaluated with 90.13% for WikiCS and 87.7% for Amazon Product network to show the generalizability of the model. The paper discusses in detail the reason for the improved predictive ability of the model both theoretically and experimentally. Our results are generalizable and our model has the potential to provide good results for link prediction tasks in any domain

An Efficient Bet-GCN Approach for Link Prediction

The task of determining whether or not a link will exist between two entities, given the current position of the network, is called link prediction. The study of predicting and analyzing links between entities in a network is emerging as one of the most interesting research areas to explore. In the field of social network analysis, finding mutual friends, predicting the friendship status between two network individuals in the near future, etc., contributes significantly to a better understanding of the underlying network dynamics. The concept has many applications in biological networks, such as finding possible connections (possible interactions) between genes and predicting protein-protein interactions. Apart from these, the concept has applications in many other areas of network science. Exploration based on Graph Neural Networks (GNNs) to accomplish such tasks is another focus that is attracting a lot of attention these days. These approaches leverage the strength of the structural information of the network along with the properties of the nodes to make efficient predictions and classifications. In this work, we propose a network centrality based approach combined with Graph Convolution Networks (GCNs) to predict the connections between network nodes. We propose an idea to select training nodes for the model based on high edge betweenness centrality, which improves the prediction accuracy of the model. The study was conducted using three benchmark networks: CORA, Citeseer, and PubMed. The prediction accuracies for these networks are: 95.08%, 95.07%, and 95.3%. The performance of the model is comprehensive and comparable to the other prior art methods and studies. Moreover, the performance of the model is evaluated with 90.13% for WikiCS and 87.7% for Amazon Product network to show the generalizability of the model. The paper discusses in detail the reason for the improved predictive ability of the model both theoretically and experimentally. Our results are generalizable and our model has the potential to provide good results for link prediction tasks in any domain