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Transperineal Mapping of the Prostate for Biopsy Strategies
Published in Ayman El-Baz, Gyan Pareek, Jasjit S. Suri, Prostate Cancer Imaging, 2018
Daniel Kaplon, Winston Barzell
Several authors have proposed a technique of “saturation” biopsies to increase cancer detection and to better estimate the tumor extent and grade (4,5). When saturation TRBx is used as a repeat diagnostic procedure, the cancer detection rate is around 30%–40% (6). While saturation biopsies appear to detect more cancers than extended biopsies, the accuracy with which any TRBx scheme can determine the size, location, extent, and grade of cancer remains to be demonstrated (7). In an effort to solve the latter shortcoming a novel 3-dimensional TRUS biopsy system (The TargetScan®) has been described recently. This system uses a 3D imaging and targeting system to biopsy the prostate in a template fashion. Preliminary studies reported a 47.6% of cancer detection rate in patients with no previous biopsies; furthermore, this approach appears superior to conventional TRUS biopsy in terms of characterizing tumor size, location, and Gleason score (8).
Evaluating the Interactions of Silver Nanoparticles and Mammalian Cells Based on Biomics Technologies
Published in Huiliang Cao, Silver Nanoparticles for Antibacterial Devices, 2017
The functional research of miRNA is one of the most popular and difficult topics in miRNA research. As an important regulatory factor in gene posttranscription, miRNA plays its role by regulating the corresponding target genes. Therefore, the analysis of miRNA target genes is the first and key step for understanding the function of miRNA. The methods to determine miRNA target genes include bioinformatics prediction and experimental assays (Xia et al. 2009). Compared with the complexity and time-consuming nature of experimental assays, bioinformatics prediction can determine a group of miRNA target genes easily and quickly. Hence, bioinformatics prediction was first carried out with bioinformatical algorithms. In order to improve accuracy, the target genes of the differentially expressed miRNAs were predicted using the three most common algorithms, including miRanda, PicTar and TargetScan with the public database miRecord (http://mirecords.biolead.org/prediction_query.php), and only those predicted by all these three algorithms were filtered. Because most miRNAs do not combine with target genes in a completely complementary pairing manner in animal cells, one miRNA may have multiple target genes (Bartel 2004). Finally, 1747, 2928 and 2667 target genes were predicted in HDFs treated with 200 μM Ag NPs for 1, 4 and 8 h, respectively (Table 10.5).
Differential expression and significance of miRNAs in plasma extracellular vesicles of patients with Parkinson’s disease
Published in International Journal of Neuroscience, 2022
Shishuai Xie, Wanxiang Niu, Feng Xu, Yuping Wang, Shanshan Hu, Chaoshi Niu
All statistical analysis was performed by using SPSS software version 16.0 (SPSS Inc., Chicago, IL) and GraphPad Prism version 6 (GraphPad Inc., La Jolla, CA). All values in the results were expressed as the mean ± standard deviation (SD). All data were analysed for statistical significance by the Student’s t test (two groups) or one-way analysis of variance followed by Tukey’s post hoc test (more than two groups). p < .05 was considered statistically significant. Meanwhile, receiver operating characteristic (ROC) curves were used to assess the sensitivity and specificity of miRNAs in plasma EVs as diagnostic markers for PD [17–19]. In addition, the softwares (TargetScan version 7.2, http://www.targetscan.org; DIANA TOOLS version 5.0, http://www.microrna.gr/microT-CDS; miRDB, http://mirdb.org/) [20,21] were used to predict target genes of miRNAs. And the target genes of miRNAs were analysed respectively in Gene Ontology (GO) biological process and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway by Metascape (http://metascape.org) [22,23] and KOBAS version 3.0 (http://kobas.cbi.pku.edu.cn) [24,25].
STAT1 mediates the PI3K/AKT pathway through promoting microRNA-18a in nasal polyps
Published in Immunopharmacology and Immunotoxicology, 2022
Subsequently, Gene Ontology (GO) [12] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [13] enrichment analyses were performed in WebGestalt (http://www.webgestalt.org/) for all predicted targets. The target genes of miRNAs and the binding sites between miR-18a and PTEN were obtained from StarBase (http://starbase.sysu.edu.cn/) and integrated. The relationship between miRNA and mRNA was probed by cytargetlinker in Cytoscape (www.cytoscape.org). The miRNA database was downloaded from https://cytargetlinker.github.io/pages/linksets/targetscan, and the data included ceRNA regulation and transcription factor regulation. The promoter sequence of miR-18a was obtained from the UCSC website (https://genome.ucsc.edu/index.html), and the binding sequence between miR-18a and STAT1 was predicted using ALGEEN (http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo). Correlations between STAT1, miR-18a, PTEN expression and levels of inflammatory factors were analyzed by corrgram (R,3.6.3, NIH) in R package.
Analysis of a circRNA-, miRNA-, and mRNA-associated ceRNA network reveals potential biomarkers in preeclampsia a ceRNA network in preeclampsia
Published in Annals of Medicine, 2021
Xiaoxiao Xu, Sha Lv, Ziwen Xiao
The dif-circRNA and dif-miRNA pairs were predicted using circBank (http://www.circbank.cn/), which is a comprehensive database of human circRNAs that includes more than 140,000 human annotated circRNAs from different sources [21]. The target mRNAs for dif-miRNAs were predicted using R based on two miRNA databases, TargetScan (http://www.targetscan.org/mamm_31/) and miRDB (http://mirdb.org/). TargetScan predicts miRNA target genes by searching for the presence of 6- to 8-mer sites that match the seed region of a given miRNA [22]. All the targets in miRDB were predicted by a bioinformatics tool, MirTarget, which was developed by analyzing thousands of miRNA-target interactions from high-throughput sequencing experiments [23]. Only genes that were predicted by both databases were included.