Mapping Of Nucleocapsid Protein (Np) Epitopes and Np-Phosphoprotein Interactive Domains of Newcastle Disease Virus with Np Monoclonal Antibodies

The Newcastle disease virus (NDV) is an economically important poultry virus which replicates in certain human cancer cells. This virus contains a negative single stranded RNA genome which encodes for the nucleocapsid protein (NP), phosphorylated nucleocapsid-associated protein, phosphoprotein (P); matrix protein (M); fusion protein (F); haemaglutinin-neuraminidase protein (HN) and the RNA-directed RNA polymerase, large protein (L). The NP is the most abundant protein found in NDV. In this study, a panel of monoclonal antibodies (mAbs) against NP was developed to study the NP-P interactions in NDV. The spleen cells of Balb/C mice immunized with purified NP obtained from the velogenic NDV strain AF2240 were fused with myeloma cells (Sp2/0-Ag14 cell line). A panel of six mAbs were produced and characterized. Four of the mAbs secreted immunoglobulins from class IgG2a with kappa light chains and the remaining two were from class IgG1 with kappa light chains. Cross-reactivity test against the NPs from other six NDV strains showed that mAbs, a2, a2s and b2 cross-reacted with all NDV strains, while mAb b3 showed specificity towards the NP of strain AF2240, the strain that was used earlier to immunize the mice. The other two mAbs, b4s and c1, demonstrated cross-reactivity amongst the various viral strains with varying reactivities. These results indicate that certain epitopes recognized by the mAbs were well conserved in all NDV strains whilst the other epitopes may have undergone some structural changes. The antigenic sites of NP bound by the mAbs were localized by Western blot analysis. Four C- and N-terminally truncated NP mutants were purified from Escherichia coli, blotted to the nitrocellulose membrane and probed with NP mAbs. The results show that the antigenic sites bound by mAbs a2, a2s and b2 were located within amino acids 441 to 489 of the C-terminal of NP. On the other hand, antigenic sites that were recognized by the mAbs, b3 and b4s were located on the N-terminal half of NP from 26 to 121 amino acid residues. MAb c1 bound to all C- and N-truncated mutants indicating that the antigenic sites recognized by mAb c1 may be located within amino acids 122 to 375. One of the mAb, a2s was further used as a tool in protein-protein interaction study between assembled NP (NPNC) and P. The NPNC was purified from E. coli by ammonium sulphate precipitation and sucrose garadient. In determining the interaction regions of P that bind to NPNC, the mAb is used in immunoprecipitating the radioactively labeled Ps-NPNC complex. The failure of certain P deleted mutants to form complex with NPNC demonstrated that the regions of P which were deleted from those mutants were responsible with the binding to NPNC. After 18 different N- and C-terminally truncated P mutants were tested in the radioimmunoprecipitation assay, it showed that the region of P that binds to NPNC is located within the internal region of C-terminal half of P, from amino acids 243 to279. In agreement with the radioimmunoprecipitation results, protein binding assay, another assay that was carried out to determine the P-NPNC interactive domain also showed that the interactive domain was mapped to the internal region of the C-terminal half of P (amino acids 224-279). A slightly bigger region of interaction domain was determined by the latter assay compared to the former assay was due to its nature and higher sensitivity of the assay. Nevertheless, both assays showed that the N-terminal half and immediate C-terminal end of P is not involved in the binding of P to NPNC. To further explore the interactions between P and NP, Far Western blotting was carried out to determine the binding domain of P to NP monomer, NPO. The NPO was obtained by fractionating the NPNC in SDS-PAGE. In this assay, the same deleted mutants that are utilized in mapping P-NPNC interactive domain were also used. The assay showed that amino acids 224-279 were indispensable for the P-NPO binding. Interestingly, these were the same amino acids that were responsible for the P-NPNC interaction. These results indicate that these amino acids were crucial for interaction between P and NP and may play bigger roles in transcription and replication of viral genome

Screening anti-cancer compounds from medicinal Malaysian plants

In the fight against cancer. no,·el chemotherapeutic agems arc constanlly being sought to complement cxisting dTlJlls. In this study. 110-10 methanolic cxtracts prepared from seven tropical plants "'ith. eth.nomedical uses were tesled for th.eir c)'loto~ic ability in "itro against cancer cell lines. The plants studied were Carico papaya (papaya). Co/l'Ils bl"ml'i (ali·ati), Cosmos calldalll)· (uJam raja). "-'CIIS deltoidea (rna. OOlek), Pip"I" sm·WIIII/VSIIIll (kaduk), PIIle/'M indica (beluntas), and l'I"emlllu curdifloru (bcbuas). The c)'lolOxicity screening tesl, SRB (sulforhodamine B) was calTied out to detennine plants wilh potential amicancer properties. Results indicated that P. sonuentosllflI and P. cordijloru exlracis dcmonslr4[OO signifiean[ c)'101oxicily against MCn cell line where the IC", were 125 ilgfml and 113 ilgfmL. respectively, and a[ the same lime: showed less aclh-ilies against nonnal cells. Wilh re:spc:ct [0 Ihese results, P. sarmenflUum and P. r:ordif/oro eX!T1lCIS could be studied further fOJ [heir po[enlialto lTeal cance-r

Cancer relapse prediction from microrna expression data using machine learning

Cancer is a major deadliest disease globally that involve uncontrolled cell growth and invasion-metastasis events. It accounts for around 13% of all deaths worldwide. Statistical reports have pointed out that the cancer occurrence rate is increasing at an alarming rate in the world. Furthermore, cancer relapse rate is also rising mostly due to late cancer diagnosis. Some cancers can recur at the site of origin or the distant site after years of anti-cancer treatment. Therefore, cancer relapse prediction process is of paramount important so that early specific treatments can be sought. Nevertheless, conventional methods for diagnosing cancer relapse rely on invasive and labor intensive biopsy examinations. Circulating miRNAs have gained great interest in medical field because of their higher sensitivity, specificity and potential for minimally invasive sampling procedures. Furthermore, miRNA expression profiling from body fluid samples using high-throughput approaches is a promising technology that could predict cancer relapse. This paper describes a machine learning based approach called one-dependent estimator to predict cancer relapse from miRNA expression data. The proposed framework will predict whether a particular cancer will relapse within cancer recurrence time frame, which is usually 5 years. To select relevant cancer recurrence associated miRNAs, we employ an entropy-based miRNA marker selection approach. This proposed system has achieved an average accuracy of 92.82% in predicting cancer relapse over three datasets, namely glioblastoma, ovarian cancer, and hepatocellular carcinoma (HCC). The experimental results exhibit the efficacy of the proposed framework

Cancer relapse prediction from microrna expression data using machine learning

Cancer is a major deadliest disease globally that involve uncontrolled cell growth and invasion-metastasis events. It accounts for around 13% of all deaths worldwide. Statistical reports have pointed out that the cancer occurrence rate is increasing at an alarming rate in the world. Furthermore, cancer relapse rate is also rising mostly due to late cancer diagnosis. Some cancers can recur at the site of origin or the distant site after years of anti-cancer treatment. Therefore, cancer relapse prediction process is of paramount important so that early specific treatments can be sought. Nevertheless, conventional methods for diagnosing cancer relapse rely on invasive and labor intensive biopsy examinations. Circulating miRNAs have gained great interest in medical field because of their higher sensitivity, specificity and potential for minimally invasive sampling procedures. Furthermore, miRNA expression profiling from body fluid samples using high-throughput approaches is a promising technology that could predict cancer relapse. This paper describes a machine learning based approach called one-dependent estimator to predict cancer relapse from miRNA expression data. The proposed framework will predict whether a particular cancer will relapse within cancer recurrence time frame, which is usually 5 years. To select relevant cancer recurrence associated miRNAs, we employ an entropy-based miRNA marker selection approach. This proposed system has achieved an average accuracy of 92.82% in predicting cancer relapse over three datasets, namely glioblastoma, ovarian cancer, and hepatocellular carcinoma (HCC). The experimental results exhibit the efficacy of the proposed framework

Cancer relapse prediction from microrna expression data using machine learning

Cancer is a major deadliest disease globally that involve uncontrolled cell growth and invasion-metastasis events. It accounts for around 13% of all deaths worldwide. Statistical reports have pointed out that the cancer occurrence rate is increasing at an alarming rate in the world. Furthermore, cancer relapse rate is also rising mostly due to late cancer diagnosis. Some cancers can recur at the site of origin or the distant site after years of anti-cancer treatment. Therefore, cancer relapse prediction process is of paramount important so that early specific treatments can be sought. Nevertheless, conventional methods for diagnosing cancer relapse rely on invasive and labor intensive biopsy examinations. Circulating miRNAs have gained great interest in medical field because of their higher sensitivity, specificity and potential for minimally invasive sampling procedures. Furthermore, miRNA expression profiling from body fluid samples using high-throughput approaches is a promising technology that could predict cancer relapse. This paper describes a machine learning based approach called one-dependent estimator to predict cancer relapse from miRNA expression data. The proposed framework will predict whether a particular cancer will relapse within cancer recurrence time frame, which is usually 5 years. To select relevant cancer recurrence associated miRNAs, we employ an entropy-based miRNA marker selection approach. This proposed system has achieved an average accuracy of 92.82% in predicting cancer relapse over three datasets, namely glioblastoma, ovarian cancer, and hepatocellular carcinoma (HCC). The experimental results exhibit the efficacy of the proposed framework

Screening anti-cancer compounds from palm oil industrial wastes

In this srudy. ami-nlmor promoting activity against breast cancer cells (MCF7) of palm oil industrial waSIl'S crudc cxtracl was inwstigated. Methanol and waleI' extracts of lhe industrial wasIl'S ,,'crc lestI'd using MTT assay. Thc mosl active exlracl for inhibition of lhe prolifcrdlion of breast cancer cell was mcthanolic extract of palm leaf waSIl'. All of the other industrial waSle extracts also showed anli-eancer aClivily excepl for aqueous extl'llcf of palm oil mill effluenl (POME), wilh respect to thesc results. palm leaf wastes coutd he studied funher for their potential 10 treat breast canccr ceUs

Screening anti-cancer compounds from rice industrial wastes

In this study. anll-tumor promoting activity against breast cancer cells (MCF7) of riec industrial wastes crude extract was investigated. Methanol and warer extracts of the industrial wastes were tested using MTT assay. The most active extract for inhibition of the prolifer.llion ofbrcast cancer cell was aqueous extract of rice mixture wastes. All of the other industrial waste extracts also sho,",'ed anti-<;;ancer activity however. at lower level. With respe<:t to these results. rice mixture ,",'lIStes could be studied funher for their potentJalto treat breasl eanen cells

Improvement of sonication processing conditions for extraction of antibacterial compounds from spathiphyllum cannifolium

Application of sonication during extraction has been reported to increase component extrdction yield and reduce solvcnt consumption. In this slUdy optimization of sonicatinn conditions to extract antibacterial compounds ....ere carried out to obmin maximwn yield of antibactcrial compounds from Spulhiphyl/uni curmifo/iuni. S. wnnifoliUni is a flo....ering plant whieh was previously shown to possess high anti·bacterial activity. Two parameters of sonication processing condition. namely thc extraction time (minutes) and temperature (0e) were optimized in this slUdy. Central Composite Dcsign (CCD) was used to design the optimiulion experiments. A set of eleven experimcnts was designed and data obtained from those ellperiments were fined to the mathematical model in which was used to plot tri-dimensional (3D) response surfaecs graph. Unfortunately. a full elliptical response was not plotted on this gniph thus optimum conditions for the elltraction of antibactcrial compounds were unable to be obmincd. Funher observation on the 20 plot however. suggested that optimized conditions might be obtained at the rangc of temperature betwecn 55 to 60°C and the lime be""een 15 10 20 minutes as it will yield maximum zone of inhibition or antibacterial cnmpoullds. The ANOVA analysis indicaled Ihat temperature had significant effc.::t on maximizing the zone of inhibilioll. BOlh time and intclllCtion between IWO indcpendcnt variables (time and temperature) were Dot significant to the zone of inhibition