Altered platelet activating factor metabolism in insulin dependent diabetes mellitus

Diabetes mellitus is associated with several abnormalities of platelet function. Recent studies have shown that the blood level of platelet activating factor (PAF), a potent inducer of platelet aggregation, is elevated in insulin dependent diabetes mellitus (IDDM) and remains unchanged in non-insulin dependent diabetes mellitus (NIDDM) patients. However, the mechanism of this increase in PAF levels has not been determined. In this study we have measured the activity of plasma PAF acetylhydrolase (an enzyme that regulates PAF levels) and lipoprotein levels in control subjects and diabetic patients. The data presented show that plasma PAF acetylhydrolase activity is significantly decreased in IDDM and is not altered in NIDDM patients. The lipoprotein levels were similar in control and diabetic subjects and there was no correlation between lipoprotein levels and PAF acetylhydrolase activity. These results suggest that the elevated levels of PAF in IDDM patients could be due to a decrease in plasma PAF acetylhydrolase activity

A hybrid algorithm to improve the accuracy of support vector machines on skewed data-sets

Over the past few years, has been shown that generalization power of Support Vector Machines (SVM) falls dramatically on imbalanced data-sets. In this paper, we propose a new method to improve accuracy of SVM on imbalanced data-sets. To get this outcome, firstly, we used undersampling and SVM to obtain the initial SVs and a sketch of the hyperplane. These support vectors help to generate new artificial instances, which will take part as the initial population of a genetic algorithm. The genetic algorithm improves the population in artificial instances from one generation to another and eliminates instances that produce noise in the hyperplane. Finally, the generated and evolved data were included in the original data-set for minimizing the imbalance and improving the generalization ability of the SVM on skewed data-sets

Tuberculosis diagnosis and treatment practices of private physicians in Karachi, Pakistan

In a densely populated urban area of Karachi, Pakistan, a questionnaire survey was made of the knowledge and practices of 120 private general practitioners about the diagnosis and treatment of tuberculosis (TB). The majority knew that cough, fever and weight loss were the main symptoms of TB, but less than half knew that blood in sputum, poor appetite and chest pain were associated with the disease. Only 58.3% of physicians used sputum microscopy for diagnosing TB and 35.0% used it as a follow-up test. Only 41.7% treated TB patients themselves, the remaining referring their patients to specialists. Around 73.3% of the doctors were aware of the 4 first-line anti-TB drugs. Efforts to improve the knowledge of private practitioners, and strategies to enhance public-private collaboration forTB control in urban areas are urgently required

WTEN: An advanced coupled tensor factorization strategy for learning from imbalanced data

© Springer International Publishing AG 2016. Learning from imbalanced and sparse data in multi-mode and high-dimensional tensor formats efficiently is a significant problem in data mining research. On one hand,Coupled Tensor Factorization (CTF) has become one of the most popular methods for joint analysis of heterogeneous sparse data generated from different sources. On the other hand,techniques such as sampling,cost-sensitive learning,etc. have been applied to many supervised learning models to handle imbalanced data. This research focuses on studying the effectiveness of combining advantages of both CTF and imbalanced data learning techniques for missing entry prediction,especially for entries with rare class labels. Importantly,we have also investigated the implication of joint analysis of the main tensor and extra information. One of our major goals is to design a robust weighting strategy for CTF to be able to not only effectively recover missing entries but also perform well when the entries are associated with imbalanced labels. Experiments on both real and synthetic datasets show that our approach outperforms existing CTF algorithms on imbalanced data

Transduction of artificial transcriptional regulatory proteins into human cells

Protein transduction (PT) is a method for delivering proteins into mammalian cells. PT is accomplished by linking a small peptide tag—called a PT domain (PTD)—to a protein of interest, which generates a functional fusion protein that can penetrate efficiently into mammalian cells. In order to study the functions of a transcription factor (TF) of interest, expression plasmids that encode the TF often are transfected into mammalian cells. However, the efficiency of DNA transfection is highly variable among different cell types and is usually very low in primary cells, stem cells and tumor cells. Zinc-finger transcription factors (ZF-TFs) can be tailor-made to target almost any gene in the human genome. However, the extremely low efficiency of DNA transfection into cancer cells, both in vivo and in vitro, limits the utility of ZF-TFs. Here, we report on an artificial ZF-TF that has been fused to a well-characterized PTD from the human immunodeficiency virus-1 (HIV-1) transcriptional activator protein, Tat. This ZF-TF targeted the endogenous promoter of the human VEGF-A gene. The PTD-attached ZF-TF was delivered efficiently into human cells in vitro. In addition, the VEGF-A-specific transcriptional repressor retarded the growth rate of tumor cells in a mouse xenograft experiment

Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons’ Data

We present a systematic analysis of the effects of synchronizing a large-scale, deeply characterized, multi-omic dataset to the current human reference genome, using updated software, pipelines, and annotations. For each of 5 molecular data platforms in The Cancer Genome Atlas (TCGA)—mRNA and miRNA expression, single nucleotide variants, DNA methylation and copy number alterations—comprehensive sample, gene, and probe-level studies were performed, towards quantifying the degree of similarity between the ‘legacy’ GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as ‘harmonized’ by the Genomic Data Commons. We offer gene lists to elucidate differences that remained after controlling for confounders, and strategies to mitigate their impact on biological interpretation. Our results demonstrate that the hg19 and hg38 TCGA datasets are very highly concordant, promote informed use of either legacy or harmonized omics data, and provide a rubric that encourages similar comparisons as new data emerge and reference data evolve. Gao et al. performed a systematic analysis of the effects of synchronizing the large-scale, widely used, multi-omic dataset of The Cancer Genome Atlas to the current human reference genome. For each of the five molecular data platforms assessed, they demonstrated a very high concordance between the ‘legacy’ GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as ‘harmonized’ by the Genomic Data Commons

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Chimeric RNAs reveal putative neoantigen peptides for developing tumor vaccines for breast cancer

IntroductionWe present here a strategy to identify immunogenic neoantigen candidates from unique amino acid sequences at the junctions of fusion proteins which can serve as targets in the development of tumor vaccines for the treatment of breastcancer.MethodWe mined the sequence reads of breast tumor tissue that are usually discarded as discordant paired-end reads and discovered cancer specific fusion transcripts using tissue from cancer free controls as reference. Binding affinity predictions of novel peptide sequences crossing the fusion junction were analyzed by the MHC Class I binding predictor, MHCnuggets. CD8+ T cell responses against the 15 peptides were assessed through in vitro Enzyme Linked Immunospot (ELISpot).ResultsWe uncovered 20 novel fusion transcripts from 75 breast tumors of 3 subtypes: TNBC, HER2+, and HR+. Of these, the NSFP1-LRRC37A2 fusion transcript was selected for further study. The 3833 bp chimeric RNA predicted by the consensus fusion junction sequence is consistent with a read-through transcription of the 5’-gene NSFP1-Pseudo gene NSFP1 (NSFtruncation at exon 12/13) followed by trans-splicing to connect withLRRC37A2 located immediately 3’ through exon 1/2. A total of 15 different 8-mer neoantigen peptides discovered from the NSFP1 and LRRC37A2 truncations were predicted to bind to a total of 35 unique MHC class I alleles with a binding affinity of IC50<500nM.); 1 of which elicited a robust immune response.ConclusionOur data provides a framework to identify immunogenic neoantigen candidates from fusion transcripts and suggests a potential vaccine strategy to target the immunogenic neopeptides in patients with tumors carrying the NSFP1-LRRC37A2 fusion