165 research outputs found
PAMAM Dendrimer as Potential Delivery System for Combined Chemotherapeutic and MicroRNA-21 Gene Therapy
Data Mining Pubmed Using Natural Language Processing to Generate the β-Catenin Biological Association Network
HuMiTar: A sequence-based method for prediction of human microRNA targets
<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR's seed region and implementation of cross-species conservation of the target site, and machine learning (ML) methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved.</p> <p>Results</p> <p>We present HuMiTar, a computational method for identifying common targets of miRs, which is based on a scoring function that considers base-pairing for both seed and non-seed positions for human miR-mRNA duplexes. Our design shows that certain non-seed miR nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of Watson-Crick pairing. We contrasted HuMiTar with several representative competing methods on two sets of human miR targets and a set of ten glioblastoma oncogenes. Comparison with the two best performing traditional methods, PicTar and TargetScanS, and a representative ML method that considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of the predictions of the other three methods. At the same time, the proposed method is also capable of finding more true positive targets as a trade-off for an increased number of predictions. Genome-wide predictions show that the proposed method is characterized by 1.99 signal-to-noise ratio and linear, with respect to the length of the mRNA sequence, computational complexity. The ROC analysis shows that HuMiTar obtains results comparable with PicTar, which are characterized by high true positive rates that are coupled with moderate values of false positive rates.</p> <p>Conclusion</p> <p>The proposed HuMiTar method constitutes a step towards providing an efficient model for studying translational gene regulation by miRs.</p
System integration study of oxy-biosyngas combustion based metal heating process using Aspen Plus
Given the increasing concerns on emissions, efficient and environmentally friendly combustion technologies
are urgently needed to address energy trilemma. Metal heating is a large component of energy-intensive processes, as its energy consumption accounts for one third of the steel manufacturing process. Early attempts at using a new flameless oxy-fuel combustion burner give high performance, low NOx, and low-cost heating for the steel industry, while biosyngas is considered as an alternative fuel for reheating furnace with aiming at CO2 mitigation. Yet, all these technical solutions are developed in isolation. This paper investigates the system integration of biosyngas production, air separation unit (ASU), reheating furnace and heat recovery (HR) steam cycle, in order to enhance energy efficiency of steel industry and enable so-called negative emissions. An integrated system model was developed using Aspen Plus to evaluate the feasibility of the proposed integration from the perspective of heat and mass balance. In particular, to study the impacts of fuel switching on the heating quality of the furnace, a three-dimensional furnace model considering detailed heat transfer processes was embedded into the system. The simulation results show that the proposed system integration strategy is technically feasible. The electricity generation of the HR steam cycle used can compensate for about 90% of ASU’s energy consumption. The system is carbon capture-ready for being further integrated with CO2 conditioning and transportation processe
miR-181d: a predictive glioblastoma biomarker that downregulates MGMT expression
Genome-wide microRNA (miRNA) profiling of 82 glioblastomas demonstrated that miR-181d was inversely associated with patient overall survival after correcting for age, Karnofsky performance status, extent of resection, and temozolomide (TMZ) treatment. This association was validated using the Cancer Genome Atlas (TCGA) dataset (n= 424) and an independent cohort (n= 35). In these independent cohorts, an association of miR-181d with survival was evident in patients who underwent TMZ treatment but was not observed in patients without TMZ therapy. Bioinformatic analysis of potential genes regulated by miR-181d revealed methyl-guanine-methyl-transferase (MGMT) as a downstream target. Indeed, transfection of miR-181d downregulated MGMT mRNA and protein expression. Furthermore, luciferase reporter assays and coprecipitation studies showed a direct interaction between miR-181d and MGMT 3′UTR. The suppressive effect of miR-181d on MGMT expression was rescued by the introduction of an MGMT cDNA. Finally, MGMT expression inversely correlated with miR-181d expression in independent glioblastoma cohorts. Together, these results suggest that miR-181d is a predictive biomarker for TMZ response and that its role is mediated, in part, by posttranscriptional regulation of MGMT
Correlation of IDH1 Mutation with Clinicopathologic Factors and Prognosis in Primary Glioblastoma: A Report of 118 Patients from China
It has been reported that IDH1 (IDH1R132) mutation was a frequent genomic alteration in grade II and grade III glial tumors but rare in primary glioblastoma (pGBM). To elucidate the frequency of IDH1 mutation and its clinical significance in Chinese patients with pGBM, one hundred eighteen pGBMs were assessed by pyro-sequencing for IDH1 mutation status, and the results were correlated with clinical characteristics and molecular pathological factors. IDH1 mutations were detected in 19/118 pGBM cases (16.1%). Younger age, methylated MGMT promoter, high expression of mutant P53 protein, low expression of Ki-67 or EGFR protein were significantly correlated with IDH1 mutation status. Most notably, we identified pGBM cases with IDH1 mutation were mainly involved in the frontal lobe when compared with those with wild-type IDH1. In addition, Kaplan-Meier survival analysis revealed a highly significant association between IDH1 mutation and a better clinical outcome (p = 0.026 for progression-free survival; p = 0.029 for overall survival). However, in our further multivariable regression analysis, the independent prognostic effect of IDH1 mutation is limited when considering age, preoperative KPS score, extent of resection, TMZ chemotherapy, and Ki-67 protein expression levels, which might narrow its prognostic power in Chinese population in the future
Identifying the components of the solid–electrolyte interphase in Li-ion batteries
The importance of the solid–electrolyte interphase (SEI) for reversible operation of Li-ion batteries has been well established, but the understanding of its chemistry remains incomplete. The current consensus on the identity of the major organic SEI component is that it consists of lithium ethylene di-carbonate (LEDC), which is thought to have high Li-ion conductivity, but low electronic conductivity (to protect the Li/C electrode). Here, we report on the synthesis and structural and spectroscopic characterizations of authentic LEDC and lithium ethylene mono-carbonate (LEMC). Direct comparisons of the SEI grown on graphite anodes suggest that LEMC, instead of LEDC, is likely to be the major SEI component. Single-crystal X-ray diffraction studies on LEMC and lithium methyl carbonate (LMC) reveal unusual layered structures and Li+ coordination environments. LEMC has Li+ conductivities of >1 × 10−6 S cm−1, while LEDC is almost an ionic insulator. The complex interconversions and equilibria of LMC, LEMC and LEDC in dimethyl sulfoxide solutions are also investigated
Genetic oxidative stress variants and glioma risk in a Chinese population: a hospital-based case–control study
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