Brain Neoplasm Classification & Detection of Accuracy on MRI Images

The abnormal, uncontrolled cell growth in the brain, commonly known n as a brain tumor, can lead to immense pressure on the various nerves and blood vessels, causing irreversible harm to the body. Early detection of brain tumors is the key to avoiding such compilations. Tumour detection can be done through various advanced Machine Learning and Image Processing algorithms. Mind Brain tumors have demonstrated testing to treat, to a great extent inferable from the organic qualities of these diseases, which frequently plan to restrict progress. To begin with, by invading one of the body's most significant organs, these growths are much of the time situated past the compass of even the most gifted neurosurgeon. These cancers are likewise situated behind the blood-cerebrum boundary (BBB), a tight intersection and transport proteins that shield fragile brain tissues from openness to factors in the overall flow, subsequently obstructing openness to foundational chemotherapy [6,7]. Besides, the interesting formative, hereditary, epigenetic and micro environmental elements of the cerebrum much of the time render these tumors impervious to ordinary and novel medicines. These difficulties are accumulated by the uncommonness of cerebrum growths comparative with numerous different types of disease, restricting the degree of subsidizing and interest from the drug business and drawing in a moderately little and divided research local area

Integrative analysis of the Trypanosoma brucei gene expression cascade predicts differential regulation of mRNA processing and unusual control of ribosomal protein expression

Background: Trypanosoma brucei is a unicellular parasite which multiplies in mammals (bloodstream form) and Tsetse flies (procyclic form). Trypanosome RNA polymerase II transcription is polycistronic, individual mRNAs being excised by trans splicing and polyadenylation. We previously made detailed measurements of mRNA half-lives in bloodstream and procyclic forms, and developed a mathematical model of gene expression for bloodstream forms. At the whole transcriptome level, many bloodstream-form mRNAs were less abundant than was predicted by the model. Results: We refined the published mathematical model and extended it to the procyclic form. We used the model, together with known mRNA half-lives, to predict the abundances of individual mRNAs, assuming rapid, unregulated mRNA processing; then we compared the results with measured mRNA abundances. Remarkably, the abundances of most mRNAs in procyclic forms are predicted quite well by the model, being largely explained by variations in mRNA decay rates and length. In bloodstream forms substantially more mRNAs are less abundant than predicted. We list mRNAs that are likely to show particularly slow or inefficient processing, either in both forms or with developmental regulation. We also measured ribosome occupancies of all mRNAs in trypanosomes grown in the same conditions as were used to measure mRNA turnover. In procyclic forms there was a weak positive correlation between ribosome density and mRNA half-life, suggesting cross-talk between translation and mRNA decay; ribosome density was related to the proportion of the mRNA on polysomes, indicating control of translation initiation. Ribosomal protein mRNAs in procyclics appeared to be exceptionally rapidly processed but poorly translated. Conclusions: Levels of mRNAs in procyclic form trypanosomes are determined mainly by length and mRNA decay, with some control of precursor processing. In bloodstream forms variations in nuclear events play a larger role in transcriptome regulation, suggesting aquisition of new control mechanisms during adaptation to mammalian parasitism

Co-infection of Newcastle disease virus genotype XIII with low pathogenic avian influenza exacerbates clinical outcome of Newcastle disease in vaccinated layer poultry flocks

Newcastle disease (ND) and avian influenza (AI) are economically important infectious diseases of poultry. Sometime, concomitant secondary viral/or bacterial infections significantly alters the pathobiology of ND and AI in poultry. As of now, the disease patterns and dynamics of co-infections caused by ND virus (NDV, genotype XIII) and Low Pathogenic AI viruses (LPAI, H9N2) are explicitly elusive. Thus, we examined the clinicopathological disease conditions due to these two economically important viruses to understand the complex disease outcomes by virus–virus interactions in vaccinated flocks. The findings of clinicopathological and molecular investigations carried on 37 commercial ND vaccinated poultry flocks revealed simultaneous circulation of NDV and AIV in same flock/bird. Further, molecular characterization of hemagglutinin (HA) and neuraminidase (NA) genes confirmed that all the identified AIVs were of low pathogenicity H9N2 subtype and fusion (F) gene analysis of detected NDVs belong to NDV class II, genotype XIII, a virulent type. The NDV and H9N2 alone or co-infected flocks (NDV + LPAI) exhibit clinical signs and lesions similar to that of virulent NDV except the degree of severity, which was higher in H9N2–NDV co-infected flocks. Additionally, avian pathogenic E. coli and mycoplasma infections were detected in majority of the ailing/dead birds from the co-infected flocks during progression of the clinical disease. Overall, the findings highlight the multi-factorial disease complexity in commercial poultry and suggest the importance of NDV genotype XIII in intensifying the clinical disease in vaccinated birds

Targeting the interaction between RNA-binding protein HuR and FOXQ1 suppresses breast cancer invasion and metastasis

This work is licensed under a Creative Commons Attribution 4.0 International License.Patients diagnosed with metastatic breast cancer have a dismal 5-year survival rate of only 24%. The RNA-binding protein Hu antigen R (HuR) is upregulated in breast cancer, and elevated cytoplasmic HuR correlates with high-grade tumors and poor clinical outcome of breast cancer. HuR promotes tumorigenesis by regulating numerous proto-oncogenes, growth factors, and cytokines that support major tumor hallmarks including invasion and metastasis. Here, we report a HuR inhibitor KH-3, which potently suppresses breast cancer cell growth and invasion. Furthermore, KH-3 inhibits breast cancer experimental lung metastasis, improves mouse survival, and reduces orthotopic tumor growth. Mechanistically, we identify FOXQ1 as a direct target of HuR. KH-3 disrupts HuR–FOXQ1 mRNA interaction, leading to inhibition of breast cancer invasion. Our study suggests that inhibiting HuR is a promising therapeutic strategy for lethal metastatic breast cancer

On Evaluating MHC-II Binding Peptide Prediction Methods

Choice of one method over another for MHC-II binding peptide prediction is typically based on published reports of their estimated performance on standard benchmark datasets. We show that several standard benchmark datasets of unique peptides used in such studies contain a substantial number of peptides that share a high degree of sequence identity with one or more other peptide sequences in the same dataset. Thus, in a standard cross-validation setup, the test set and the training set are likely to contain sequences that share a high degree of sequence identity with each other, leading to overly optimistic estimates of performance. Hence, to more rigorously assess the relative performance of different prediction methods, we explore the use of similarity-reduced datasets. We introduce three similarity-reduced MHC-II benchmark datasets derived from MHCPEP, MHCBN, and IEDB databases. The results of our comparison of the performance of three MHC-II binding peptide prediction methods estimated using datasets of unique peptides with that obtained using their similarity-reduced counterparts shows that the former can be rather optimistic relative to the performance of the same methods on similarity-reduced counterparts of the same datasets. Furthermore, our results demonstrate that conclusions regarding the superiority of one method over another drawn on the basis of performance estimates obtained using commonly used datasets of unique peptides are often contradicted by the observed performance of the methods on the similarity-reduced versions of the same datasets. These results underscore the importance of using similarity-reduced datasets in rigorously comparing the performance of alternative MHC-II peptide prediction methods

Natural product derivative Gossypolone inhibits Musashi family of RNA-binding proteins

Background: The Musashi (MSI) family of RNA-binding proteins is best known for the role in post-transcriptional regulation of target mRNAs. Elevated MSI1 levels in a variety of human cancer are associated with up-regulation of Notch/Wnt signaling. MSI1 binds to and negatively regulates translation of Numb and APC (adenomatous polyposis coli), negative regulators of Notch and Wnt signaling respectively. Methods: Previously, we have shown that the natural product (-)-gossypol as the first known small molecule inhibitor of MSI1 that down-regulates Notch/Wnt signaling and inhibits tumor xenograft growth in vivo. Using a fluorescence polarization (FP) competition assay, we identified gossypolone (Gn) with a > 20-fold increase in Ki value compared to (-)-gossypol. We validated Gn binding to MSI1 using surface plasmon resonance, nuclear magnetic resonance, and cellular thermal shift assay, and tested the effects of Gn on colon cancer cells and colon cancer DLD-1 xenografts in nude mice. Results: In colon cancer cells, Gn reduced Notch/Wnt signaling and induced apoptosis. Compared to (-)-gossypol, the same concentration of Gn is less active in all the cell assays tested. To increase Gn bioavailability, we used PEGylated liposomes in our in vivo studies. Gn-lip via tail vein injection inhibited the growth of human colon cancer DLD-1 xenografts in nude mice, as compared to the untreated control (P < 0.01, n = 10). Conclusion: Our data suggest that PEGylation improved the bioavailability of Gn as well as achieved tumor-targeted delivery and controlled release of Gn, which enhanced its overall biocompatibility and drug efficacy in vivo. This provides proof of concept for the development of Gn-lip as a molecular therapy for colon cancer with MSI1/MSI2 overexpression

CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server- CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods

Natural product (-)-gossypol inhibits colon cancer cell growth by targeting RNA-binding protein Musashi-1

Musashi-1 (MSI1) is an RNA-binding protein that acts as a translation activator or repressor of target mRNAs. The best-characterized MSI1 target is Numb mRNA, whose encoded protein negatively regulates Notch signaling. Additional MSI1 targets include the mRNAs for the tumor suppressor protein APC that regulates Wnt signaling and the cyclin-dependent kinase inhibitor P21WAF-1. We hypothesized that increased expression of NUMB, P21 and APC, through inhibition of MSI1 RNA-binding activity might be an effective way to simultaneously downregulate Wnt and Notch signaling, thus blocking the growth of a broad range of cancer cells. We used a fluorescence polarization assay to screen for small molecules that disrupt the binding of MSI1 to its consensus RNA binding site. One of the top hits was (-)-gossypol (Ki = 476 ± 273 nM), a natural product from cottonseed, known to have potent anti-tumor activity and which has recently completed Phase IIb clinical trials for prostate cancer. Surface plasmon resonance and nuclear magnetic resonance studies demonstrate a direct interaction of (-)-gossypol with the RNA binding pocket of MSI1. We further showed that (-)-gossypol reduces Notch/Wnt signaling in several colon cancer cell lines having high levels of MSI1, with reduced SURVIVIN expression and increased apoptosis/autophagy. Finally, we showed that orally administered (-)-gossypol inhibits colon cancer growth in a mouse xenograft model. Our study identifies (-)-gossypol as a potential small molecule inhibitor of MSI1-RNA interaction, and suggests that inhibition of MSI1's RNA binding activity may be an effective anti-cancer strategy

PeptX: Using Genetic Algorithms to optimize peptides for MHC binding

<p>Abstract</p> <p>Background</p> <p>The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different <it>in silico </it>techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain <it>in silico </it>scoring functions?</p> <p>Results</p> <p>Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.</p> <p>Conclusion</p> <p>We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.</p

Role of Position 627 of PB2 and the Multibasic Cleavage Site of the Hemagglutinin in the Virulence of H5N1 Avian Influenza Virus in Chickens and Ducks

Highly pathogenic H5N1 avian influenza viruses have caused major disease outbreaks in domestic and free-living birds with transmission to humans resulting in 59% mortality amongst 564 cases. The mutation of the amino acid at position 627 of the viral polymerase basic-2 protein (PB2) from glutamic acid (E) in avian isolates to lysine (K) in human isolates is frequently found, but it is not known if this change affects the fitness and pathogenicity of the virus in birds. We show here that horizontal transmission of A/Vietnam/1203/2004 H5N1 (VN/1203) virus in chickens and ducks was not affected by the change of K to E at PB2-627. All chickens died between 21 to 48 hours post infection (pi), while 70% of the ducks survived infection. Virus replication was detected in chickens within 12 hours pi and reached peak titers in spleen, lung and brain between 18 to 24 hours for both viruses. Viral antigen in chickens was predominantly in the endothelium, while in ducks it was present in multiple cell types, including neurons, myocardium, skeletal muscle and connective tissues. Virus replicated to a high titer in chicken thrombocytes and caused upregulation of TLR3 and several cell adhesion molecules, which may explain the rapid virus dissemination and location of viral antigen in endothelium. Virus replication in ducks reached peak values between 2 and 4 days pi in spleen, lung and brain tissues and in contrast to infection in chickens, thrombocytes were not involved. In addition, infection of chickens with low pathogenic VN/1203 caused neuropathology, with E at position PB2-627 causing significantly higher infection rates than K, indicating that it enhances virulence in chickens