Preparation and characterization of poly (e-caprolactone)/TiO2 micro-composites

Based on XRD results, the study of crystallization of the PCL/TiO2MCs showed that TiO2MPs have significant influence on crystallization behaviour of poly (ε-caprolactone) in the PCL/TiO2MCs. The FTIR spectra indicated that the C=O of PCL shifted when TiO2MPs was added, indicating that some Van der Waals bonding between the alkyl groups of TiO2 and the ester group of PCL were formed. In comparison with the pure PCL, TGA data indicated an enhancement of thermal stability of PCL/TiO2MCs. SEM results confirmed the surface of TiO2MPs has sufficient compatibility with PCL through the link of the coupling agent between TiO2MPs and PCL, which can reduce the aggregation of TiO2MPs and improve dispersity. Transmission electron microscope (TEM) studies were performed to provide evidence for the micrometric dispersion of the TiO2MPs into PCL matrix on microscale

Efficient inhibition of human immunodeficiency virus replication using novel modified microRNA-30a targeting 3'-untranslated region transcripts

RNA interference (RNAi)-based gene therapy is currently considered to be a combinatorial anti-human immunodeficiency virus-1 (HIV-1) therapy. Although arti­ficial polycistronic microRNAs (miRs) can reduce HIV-1 escape mutant variants, this approach may increase the risk of side effects. The present study aimed to optimize the efficiency of anti-HIV RNAi gene therapy in order to reduce the cell toxicity induced by multi-short hairpin RNA expression. An artificial miR-30a-3'-untranslated region (miR-3'-UTR) obtained from a single RNA polymerase II was used to simultaneously target all viral transcripts. The results of the present study demonstrated that HIV-1 replication was signifi­cantly inhibited in the cells with the miR-3'-UTR construct, suggesting that miR-3'-UTR may serve as a promising tool for RNAi-based gene therapy in the treatment of HIV-1. © 2016, Spandidos Publications. All Rights Reserved

MultiMSOAR 2.0: An Accurate Tool to Identify Ortholog Groups among Multiple Genomes

The identification of orthologous genes shared by multiple genomes plays an important role in evolutionary studies and gene functional analyses. Based on a recently developed accurate tool, called MSOAR 2.0, for ortholog assignment between a pair of closely related genomes based on genome rearrangement, we present a new system MultiMSOAR 2.0, to identify ortholog groups among multiple genomes in this paper. In the system, we construct gene families for all the genomes using sequence similarity search and clustering, run MSOAR 2.0 for all pairs of genomes to obtain the pairwise orthology relationship, and partition each gene family into a set of disjoint sets of orthologous genes (called super ortholog groups or SOGs) such that each SOG contains at most one gene from each genome. For each such SOG, we label the leaves of the species tree using 1 or 0 to indicate if the SOG contains a gene from the corresponding species or not. The resulting tree is called a tree of ortholog groups (or TOGs). We then label the internal nodes of each TOG based on the parsimony principle and some biological constraints. Ortholog groups are finally identified from each fully labeled TOG. In comparison with a popular tool MultiParanoid on simulated data, MultiMSOAR 2.0 shows significantly higher prediction accuracy. It also outperforms MultiParanoid, the Roundup multi-ortholog repository and the Ensembl ortholog database in real data experiments using gene symbols as a validation tool. In addition to ortholog group identification, MultiMSOAR 2.0 also provides information about gene births, duplications and losses in evolution, which may be of independent biological interest. Our experiments on simulated data demonstrate that MultiMSOAR 2.0 is able to infer these evolutionary events much more accurately than a well-known software tool Notung. The software MultiMSOAR 2.0 is available to the public for free

MSOAR 2.0: Incorporating tandem duplications into ortholog assignment based on genome rearrangement

<p>Abstract</p> <p>Background</p> <p>Ortholog assignment is a critical and fundamental problem in comparative genomics, since orthologs are considered to be functional counterparts in different species and can be used to infer molecular functions of one species from those of other species. MSOAR is a recently developed high-throughput system for assigning one-to-one orthologs between closely related species on a genome scale. It attempts to reconstruct the evolutionary history of input genomes in terms of genome rearrangement and gene duplication events. It assumes that a gene duplication event inserts a duplicated gene into the genome of interest at a random location (<it>i.e.</it>, the random duplication model). However, in practice, biologists believe that genes are often duplicated by tandem duplications, where a duplicated gene is located next to the original copy (<it>i.e.</it>, the tandem duplication model).</p> <p>Results</p> <p>In this paper, we develop MSOAR 2.0, an improved system for one-to-one ortholog assignment. For a pair of input genomes, the system first focuses on the tandemly duplicated genes of each genome and tries to identify among them those that were duplicated after the speciation (<it>i.e.</it>, the so-called inparalogs), using a simple phylogenetic tree reconciliation method. For each such set of tandemly duplicated inparalogs, all but one gene will be deleted from the concerned genome (because they cannot possibly appear in any one-to-one ortholog pairs), and MSOAR is invoked. Using both simulated and real data experiments, we show that MSOAR 2.0 is able to achieve a better sensitivity and specificity than MSOAR. In comparison with the well-known genome-scale ortholog assignment tool InParanoid, Ensembl ortholog database, and the orthology information extracted from the well-known whole-genome multiple alignment program MultiZ, MSOAR 2.0 shows the highest sensitivity. Although the specificity of MSOAR 2.0 is slightly worse than that of InParanoid in the real data experiments, it is actually better than that of InParanoid in the simulation tests.</p> <p>Conclusions</p> <p>Our preliminary experimental results demonstrate that MSOAR 2.0 is a highly accurate tool for one-to-one ortholog assignment between closely related genomes. The software is available to the public for free and included as online supplementary material.</p

Estimation of genetic variation in the Secretor and Lewis genes in Iranian hospitalized children

Background: The Secretor (FUT2) and lewis gene (FUT3) are in charge of the construction of histo-blood group antigens, which act as a receptor for some Pathogenes. This study aimed to estimate the prevalence of five significant single nucleotide polymorphisms (SNPs) in Iranian children. Methods: In this cross-sectional study, 102 blood samples collected from hospitalized children. The FUT2 gene region was amplified and sequenced to explore rs1047781 and rs601338, and the FUT3 gene region was amplified to explore rs28362459, rs812936, rs778986 SNPs. Results: In FUT2 gene, Se358,428 that produces Se phenotype with 63 (0.53 - 0.72) prevalence, was the most common genotype. For FUT3 gene Le59,202,314 with 80 prevalence was most common genotype (0.71 - 0.87). Conclusion: This study genotyped Secretor and Lewis genes and designated SNPs� distinct distribution in Iran, and clarified at-risk groups for certain diseases. © 2020 Société française de transfusion sanguine (SFTS