DETECTION OF PHISHING WEBSITES USING HYBRID MODEL

Online technologies have revolutionized the modern computing world. Thereare number of users who purchase products online and make payment through variouswebsites. There are multiple websites who ask user to provide sensitive data such asusername, password or credit card details etc. often for malicious reasons. This type ofwebsite is known as phishing website. The phishing website can be detected based on someimportant characteristics like URL (Uniform Resource Locator) and Domain identity.Several approaches have been proposed for detection of phishing websites by extracting thephishing data sets criteria to classify their legitimacy. However, there is no such approachthat can provide better results to the users from phishing attacks. This paper is an attemptto contribute in that area by presenting a hybrid model for classification to detect phishingwebsites with high accuracy and less error rate

The minimum mean monopoly energy of a graph

The motivation for the study of the graph energy comes from chemistry, where the research on the so-called total pi - electron energy can be traced back until the 1930s. This graph invariant is very closely connected to a chemical quantity known as the total pi - electron energy of conjugated hydro carbon molecules. In recent times analogous energies are being considered, based on Eigen values of a variety of other graph matrices. In 1978, I.Gutman [1] defined energy mathematically for all graphs. Energy of graphs has many mathematical properties which are being investigated. The ordinary energy of an undirected simple finite graph G is defined as the sum of the absolute values of the Eigen values of its associated matrix. i.e. if mu(1), mu(2), ..., mu(n) are the Eigen values of adjacency matrix A(G), then energy of graph is Sigma(G) = Sigma(n)(i=1) vertical bar mu(i)vertical bar Laura Buggy, Amalia Culiuc, Katelyn Mccall and Duyguyen [9] introduced the more general M-energy or Mean Energy of G is then defined as E-M (G) = Sigma(n)(i=1)vertical bar mu(i) - (mu) over bar vertical bar, where (mu) over bar vertical bar is the average of mu(1), mu(2), ..., mu(n). A subset M subset of V (G), in a graph G (V, E), is called a monopoly set of G if every vertex v is an element of (V - M) has at least d(v)/2 neighbors in M. The minimum cardinality of a monopoly set among all monopoly sets in G is called the monopoly size of G, denoted by mo(G) Ahmed Mohammed Naji and N.D.Soner [7] introduced minimum monopoly energy E-MM [G] of a graph G. In this paper we are introducing the minimum mean monopoly energy, denoted by E-MM(M) (G), of a graph G and computed minimum monopoly energies of some standard graphs. Upper and lower bounds for E-MM(M) (G)are also established.Publisher's Versio

In silico and in vitro investigations on the protein–protein interactions of glutathione S-transferases with mitogen-activated protein kinase 8 and apoptosis signal-regulating kinase 1

Cytosolic glutathione S-transferase (GST) enzymes participate in several cellular processes in addition to facilitating glutathione conjugation reactions that eliminate endogenous and exogenous toxic compounds, especially electrophiles. GSTs are thought to interact with various kinases, resulting in the modulation of apoptotic processes and cellular proliferation. The present research used a combination of in silico and in vitro studies to investigate protein–protein interactions between the seven most abundant cytosolic GSTs—GST alpha-1 (GST-A1), GST alpha-2 (GST-A2), GST mu-1 (GST-M1), GST mu-2 (GST-M2), GST mu-5 (GST-M5), GST theta-1 (GST-T1) and GST pi-1 (GST-P1)—and Mitogen-activated protein kinase 8 (MAPK8) and Apoptosis signal-regulating kinase 1 (ASK1). MAPK8 and ASK1 were chosen as this study’s protein interaction partners because of their predominant role in electrophile or cytokine-induced stress-mediated apoptosis, inflammation and fibrosis. The highest degree of sequence homology or sequence similarity was observed in two GST subgroups: the GST-A1, GST-A2 and GST-P1 isoforms constituted subgroup1; the GST-M1, GST-M2 and GST-M5 isoforms constituted subgroup 2. The GST-T1 isoform diverged from these isoforms. In silico investigations revealed that GST-M1 showed a significantly higher binding affinity to MAPK8, and its complex was more structurally stable than the other isoforms, in the order GST-M1 > GST-M5 > GST-P1 > GST-A2 > GST-A1 > GST-M2 > GST-T1. Similarly, GST-A1, GST-P1 and GST-T1 actively interacted with ASK1, and their structural stability was also better, in the order GST-T1 > GST-A1 > GST-P1 > GST-A2 > GST-M5 > GST-M1 > GST-M2. To validate in silico results, we performed in vitro crosslinking and mass spectroscopy experiments. Results indicated that GST-M1 interacted with GST-T1 to form heterodimers and confirmed the predicted interaction between GST-M1 and MAPK8. Communicated by Ramaswamy H. Sarma.publishersversioninpres

Cohort-based association study of germline genetic variants with acute and chronic health complications of childhood cancer and its treatment: Genetic Risks for Childhood Cancer Complications Switzerland (GECCOS) study protocol

INTRODUCTION: Childhood cancer and its treatment may lead to various health complications. Related impairment in quality of life, excess in deaths and accumulated healthcare costs are relevant. Genetic variations are suggested to contribute to the wide inter-individual variability of complications but have been used only rarely to risk-stratify treatment and follow-up care. This study aims to identify germline genetic variants associated with acute and late complications of childhood cancer. METHODS AND ANALYSIS: The Genetic Risks for Childhood Cancer Complications Switzerland (GECCOS) study is a nationwide cohort study. Eligible are patients and survivors who were diagnosed with childhood cancers or Langerhans cell histiocytosis before age 21 years, were registered in the Swiss Childhood Cancer Registry (SCCR) since 1976 and have consented to the Paediatric Biobank for Research in Haematology and Oncology, Geneva, host of the national Germline DNA Biobank Switzerland for Childhood Cancer and Blood Disorders (BISKIDS).GECCOS uses demographic and clinical data from the SCCR and the associated Swiss Childhood Cancer Survivor Study. Clinical outcome data consists of organ function testing, health conditions diagnosed by physicians, second primary neoplasms and self-reported information from participants. Germline genetic samples and sequencing data are collected in BISKIDS. We will perform association analyses using primarily whole-exome or whole-genome sequencing to identify genetic variants associated with specified health conditions. We will use clustering and machine-learning techniques and assess multiple health conditions in different models. DISCUSSION: GECCOS will improve knowledge of germline genetic variants associated with childhood cancer-associated health conditions and help to further individualise cancer treatment and follow-up care, potentially resulting in improved efficacy and reduced side effects. ETHICS AND DISSEMINATION: The Geneva Cantonal Commission for Research Ethics has approved the GECCOS study.Research findings will be disseminated through national and international conferences, publications in peer-reviewed journals and in lay language online

GSTM1 and GSTT1 double null genotypes determining cell fate and proliferation as potential risk factors of relapse in children with hematological malignancies after hematopoietic stem cell transplantation.

PURPOSE This study aimed to retrospectively evaluate the genetic association of null variants of glutathione S-transferases GSTM1 and GSTT1 with relapse incidence in children with hematological malignancies (HMs) undergoing busulfan (BU)- containing allogeneic hematopoietic stem cell transplantation (HSCT) and to assess the impact of these variants on BU-induced cytotoxicity on the immortalized lymphoblastoid cell lines (LCLs) and tumor THP1 GST gene-edited cell models. METHODS GSTM1- and GSTT1-null alleles were genotyped using germline DNA from whole blood prior to a conditioning BU-based regimen. Association of GSTM1- and GSTT1-null variants with relapse incidence was analyzed using multivariable competing risk analysis. BU-induced cell death studies were conducted in GSTs- null and non-null LCLs and CRISPR-Cas9 gene-edited THP1 leukemia cell lines. RESULTS Carrying GSTM1/GSTT1 double null genotype was found to be an independent risk factor for post-HSCT relapse in 86 children (adjusted HR: 6.52 [95% Cl, 2.76-15.42; p = 1.9 × 10-5]). BU-induced cell death preferentially in THP1GSTM1(non-null) and LCLsGSTM1(non-null) as shown by decreased viability, increased necrosis and levels of the oxidized form of glutathione compared to null cells, while GSTT1 non-null cells showed increased baseline proliferation. CONCLUSION The clinical association suggests that GSTM1/GSTT1 double null genotype could serve as genetic stratification biomarker for the high risk of post-HSCT relapse. Functional studies have indicated that GSTM1 status modulates BU-induced cell death. On the other hand, GSTT1 is proposed to be involved in baseline cell proliferation

Clinical Pharmacogenetics Implementation Consortium Guideline (CPIC) for <i>CYP2D6, ADRB1, ADRB2, ADRA2C, GRK4</i>, and <i>GRK5 </i>Genotypes and Beta-Blocker Therapy

Beta-blockers are widely used medications for a variety of indications, including heart failure, myocardial infarction, cardiac arrhythmias, and hypertension. Genetic variability in pharmacokinetic (e.g., CYP2D6) and pharmacodynamic (e.g., ADRB1, ADRB2, ADRA2C, GRK4, GRK5) genes have been studied in relation to beta-blocker exposure and response. We searched and summarized the strength of the evidence linking beta-blocker exposure and response with the six genes listed above. The level of evidence was high for associations between CYP2D6 genetic variation and both metoprolol exposure and heart rate response. Evidence indicates that CYP2D6 poor metabolizers experience clinically significant greater exposure and lower heart rate in response to metoprolol compared with those who are not poor metabolizers. Therefore, we provide therapeutic recommendations regarding genetically predicted CYP2D6 metabolizer status and metoprolol therapy. However, there was insufficient evidence to make therapeutic recommendations for CYP2D6 and other beta-blockers or for any beta-blocker and the other five genes evaluated (updates at www.cpicpgx.org).</p

Enzyme Kinetics

Enzyme Kinetics Course Presentatio

Genetic variations in exonic and 5′ regulatory region of CYP2C19 and their functional importance as explored in South Indian population

CYP2C19 is involved in the metabolism of clinically important drugs such as proton pump inhibitors, proguanil, anti depressants, nelfinavir, voriconazole, etc. Various studies have established the genotype-phenotype correlation of CYP2C19 in different populations. Discrepancies were found in these studies; where no correlation was observed between the CYP2C19 genotype (variations in the exonic region) and phenotype in some individuals, indicating the presence of variations in 5' flanking region or intronic region. The objective of this study was to sequence and functionally characterize the 5'regulatory region of CYP2C19 in South Indian individuals. In this study, the 5' regulatory region of CYP2C19 was sequenced from 58 unrelated healthy volunteers of South Indian origin, and 8 novel variations were identified. All the sequences of 5' regulatory region of CYP2C19 were submitted to Genbank database and are available in public domain (Accession ID:EU369428 to EU 369485). Identified novel variations and their percentage frequencies were: -779A>C (16.4), -828T>A (2.6), -934del>T (3.5),-1051T>C (1.72), -1289T>G (3.4), -1442T>C (12.1), -1498T>G (25.0) and -1558T>G (2.6). The reported variations found in the study population and their frequencies were: -98T>C (28.4),-806C>T (2.6), -833del>T (9.5), 889T>G (10.3) and -1418C>T (1.7). Some of these variations were found to be present in the transcription factors binding site such as ATF-2, GATA 1, CREB, CEBPB and CDP repressor protein etc. All the subjects were also explored for exonic region polymorphisms of CYP2C19, 681G>A (*2 allele) and 636G>A (*3 allele) and were detected at 0.371 and 0.025 frequencies, respectively. 43 different haplotypes were constructed with all the variations observed in this population, of which frequency of 13 haplotypes were found to be more than 1%. Phenotyping of 50 subjects was also performed by using proguanil as a probe drug (subjects were phenotyped by measuring the plasma concentrations of proguanil and cycloguanil by reverse phase HPLC after a single oral dose (200mg) of proguanil), and correlated with the variations in exonic region and 5' flanking region. The gene-dose effect of CYP2C19 and proguanil metabolic ratio (progunail/cycloguanil) was found in this population. The mean proguanil metabolic ratios (proguanil/cycloguanil) were highest in CYP2C19*1/*2 or *1/*3subjects (9.6 ± 2.6; p=0.03) and in *2/*2 or *2/*3 (9.9 ± 3.7; p=0.04) as compared to normal subjects (*1/*1; 4.7 ± 1.2), but still some overlapping of the metabolic ratios were found between different genotypes, which may be due to the variations found in the promoter region. Further analysis showed that variations -98T>C and -806 C>T have influenced the proguanil metabolic ratio irrespective of their exonic region variations. In silico docking analysis of transcription factors to its binding sites in CYP2C19 5' regulatory region with and without variations revealed the alteration in the interaction of transcription factors to their binding sites in the presence of variant alleles. The 5' regulatory region of samples with 13 different inferred haplotypes were cloned into pGL-3 basic reporter vectors for studying the impact of the promoter variations on expression of luciferase gene in human HepG2 cell lines. Transfection of pGL-3 fused vectors with CYP2C19 5' regulatory region using lipofectamine reagent into HepG2 cell lines and luciferase assay have shown that variations present in the cis-acting elements of the CYP2C19 promoter region such as -1442T>C, -779A>C and -98T>C -1498T>G and -828del>T alter the transcription of the gene. Electrophoretic mobility shift assays had shown the sequence specific binding of proteins from HepG2 nuclear extracts. These results indicate the influence of these variations on gene expression which can explain the Interindividual differences in CYP2C19 gene expression. This also helps us to understand the genotype dependant differences in enzyme induction and inhibition in a clinical setting. The results also suggest the role of activating transcription factor-2, cyclic AMP response element binding protein and CCAAT displacement repressor protein on CYP2C19 gene transcription