213 research outputs found

    Accuracy of elastic fusion biopsy in daily practice: results of a multicenter study of 2115 patients

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    OBJECTIVES: To assess the accuracy of Koelis fusion biopsy for the detection of prostate cancer and clinically significant prostate cancer in the everyday practice. METHODS: We retrospectively enrolled 2115 patients from 15 institutions in four European countries undergoing transrectal Koelis fusion biopsy from 2010 to 2017. A variable number of target (usually 2-4) and random cores (usually 10-14) were carried out, depending on the clinical case and institution habits. The overall and clinically significant prostate cancer detection rates were assessed, evaluating the diagnostic role of additional random biopsies. The cancer detection rate was correlated to multiparametric magnetic resonance imaging features and clinical variables. RESULTS: The mean number of targeted and random cores taken were 3.9 (standard deviation 2.1) and 10.5 (standard deviation 5.0), respectively. The cancer detection rate of Koelis biopsies was 58% for all cancers and 43% for clinically significant prostate cancer. The performance of additional, random cores improved the cancer detection rate of 13% for all cancers (P < 0.001) and 9% for clinically significant prostate cancer (P < 0.001). Prostate cancer was detected in 31%, 66% and 89% of patients with lesions scored as Prostate Imaging Reporting and Data System 3, 4 and 5, respectively. Clinical stage and Prostate Imaging Reporting and Data System score were predictors of prostate cancer detection in multivariate analyses. Prostate-specific antigen was associated with prostate cancer detection only for clinically significant prostate cancer. CONCLUSIONS: Koelis fusion biopsy offers a good cancer detection rate, which is increased in patients with a high Prostate Imaging Reporting and Data System score and clinical stage. The performance of additional, random cores seems unavoidable for correct sampling. In our experience, the Prostate Imaging Reporting and Data System score and clinical stage are predictors of prostate cancer and clinically significant prostate cancer detection; prostate-specific antigen is associated only with clinically significant prostate cancer detection, and a higher number of biopsy cores are not associated with a higher cancer detection rate

    Multivalued SK-contractions with respect to b-generalized pseudodistances

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    A new class of multivalued non-self-mappings, called SK-contractions with respect to b-generalized pseudodistances, is introduced and used to investigate the existence of best proximity points by using an appropriate geometric property. Some new fixed point results in b-metric spaces are also obtained. Examples are given to support the usability of our main result

    Patterning and process parameter effects in 3D suspension near-field electrospinning of nanoarrays

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    The extracellular matrix (ECM) contains nanofibrous proteins and proteoglycans. Nanofabrication methods have received growing interest in recent years as a means of recapitulating these elements within the ECM. Near-field electrospinning (NFES) is a versatile fibre deposition method, capable of layer-by-layer nano-fabrication. The maximum layer height is generally limited in layer-by-layer NFES as a consequence of electrostatic effects of the polymer at the surface, due to residual charge and polymer dielectric properties. This restricts the total volume achievable by layer-by-layer techniques. Surpassing this restriction presents a complex challenge, leading to research innovations aimed at increasing patterning precision, and achieving a translation from 2D to 3D additive nanofabrication. Here we investigated a means of achieving this translation through the use of 3D electrode substrates. This was addressed by in-house developed technology in which selective laser melt manufactured standing pillar electrodes were combined with a direct suspension near-field electrospinning (SNFES) technique, which implements an automated platform to manoeuvre the pillar electrodes around the emitter in order to suspend fibres in the free space between the electrode support structures. In this study SNFES was used in multiple operation modes, investigating the effects of varying process parameters, as well as pattern variations on the suspended nanoarrays. Image analysis of the nanoarrays allowed for the assessment of fibre directionality, isotropy, and diameter; identifying optimal settings to generate fibres for tissue engineering applications

    Integrated genomic analyses of ovarian carcinoma

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    A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients’ lives. The Cancer Genome Atlas project has analysed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours. Here we report that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1, BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three microRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumours with BRCA1/2 (BRCA1 or BRCA2) and CCNE1 aberrations have on survival. Pathway analyses suggested that homologous recombination is defective in about half of the tumours analysed, and that NOTCH and FOXM1 signalling are involved in serous ovarian cancer pathophysiology.National Institutes of Health (U.S.) (Grant U54HG003067)National Institutes of Health (U.S.) (Grant U54HG003273)National Institutes of Health (U.S.) (Grant U54HG003079)National Institutes of Health (U.S.) (Grant U24CA126543)National Institutes of Health (U.S.) (Grant U24CA126544)National Institutes of Health (U.S.) (Grant U24CA126546)National Institutes of Health (U.S.) (Grant U24CA126551)National Institutes of Health (U.S.) (Grant U24CA126554)National Institutes of Health (U.S.) (Grant U24CA126561)National Institutes of Health (U.S.) (Grant U24CA126563)National Institutes of Health (U.S.) (Grant U24CA143882)National Institutes of Health (U.S.) (Grant U24CA143731)National Institutes of Health (U.S.) (Grant U24CA143835)National Institutes of Health (U.S.) (Grant U24CA143845)National Institutes of Health (U.S.) (Grant U24CA143858)National Institutes of Health (U.S.) (Grant U24CA144025)National Institutes of Health (U.S.) (Grant U24CA143866)National Institutes of Health (U.S.) (Grant U24CA143867)National Institutes of Health (U.S.) (Grant U24CA143848)National Institutes of Health (U.S.) (Grant U24CA143843)National Institutes of Health (U.S.) (Grant R21CA135877

    Genome modeling system: A knowledge management platform for genomics

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    In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms
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