| 1. |
- Meani, F, et al.
(författare)
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Investigation of the Ovarian and Prostate Cancer Peptidome for Candidate Early Detection Markers Using a Novel Nanoparticle Biomarker Capture Technology
- 2010
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 2:4, s. 504-518
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Tidskriftsartikel (refereegranskat)abstract
- Current efforts to identify protein biomarkers of disease use mainly mass spectrometry (MS) to analyze tissue and blood specimens. The low-molecular-weight "peptidome" is an attractive information archive because of the facile nature by which the low-molecular-weight information freely crosses the endothelial cell barrier of the vasculature, which provides opportunity to measure disease microenvironment-associated protein analytes secreted or shed into the extracellular interstitium and from there into the circulation. However, identifying useful protein biomarkers (peptidomic or not) which could be useful to detect early detection/monitoring of disease, toxicity, doping, or drug abuse has been severely hampered because even the most sophisticated, high-resolution MS technologies have lower sensitivities than those of the immunoassays technologies now routinely used in clinical practice. Identification of novel low abundance biomarkers that are indicative of early-stage events that likely exist in the sub-nanogram per milliliter concentration range of known markers, such as prostate-specific antigen, cannot be readily detected by current MS technologies. We have developed a new nanoparticle technology that can, in one step, capture, concentrate, and separate the peptidome from high-abundance blood proteins. Herein, we describe an initial pilot study whereby the peptidome content of ovarian and prostate cancer patients is investigated with this method. Differentially abundant candidate peptidome biomarkers that appear to be specific for early-stage ovarian and prostate cancer have been identified and reveal the potential utility for this new methodology
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| 2. |
- Vezzoli, M, et al.
(författare)
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RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients
- 2017
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 7:1, s. 10528-
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Tidskriftsartikel (refereegranskat)abstract
- Some aspects of endometrial cancer (EC) preoperative work-up are still controversial, and debatable are the roles played by lymphadenectomy and radical surgery. Proper preoperative EC staging can help design a tailored surgical treatment, and this study aims to propose a new algorithm able to predict extrauterine disease diffusion. 293 EC patients were consecutively enrolled, and age, BMI, children’s number, menopausal status, contraception, hormone replacement therapy, hypertension, histological grading, clinical stage, and serum HE4 and CA125 values were preoperatively evaluated. In order to identify before surgery the most important variables able to classify EC patients based on FIGO stage, we adopted a new statistical approach consisting of two-steps: 1) Random Forest with its relative variable importance; 2) a novel algorithm able to select the most representative Regression Tree (RERT) from an ensemble method. RERT, built on the above mentioned variables, provided a sensitivity, specificity, NPV and PPV of 90%, 76%, 94% and 65% respectively, in predicting FIGO stage > I. Notably, RERT outperformed the prediction ability of HE4, CA125, Logistic Regression and single cross-validated Regression Tree. Such algorithm has great potential, since it better identifies the true early-stage patients, thus providing concrete support in the decisional process about therapeutic options to be performed.
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| 3. |
- O'Donncha, F., et al.
(författare)
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AllScale toolchain pilot applications : PDE based solvers using a parallel development environment
- 2019
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Ingår i: Computer Physics Communications. - : Elsevier B.V.. - 0010-4655 .- 1879-2944.
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Tidskriftsartikel (refereegranskat)abstract
- AllScale is a programming environment targeting simplified development of highly scalable parallel applications by dividing development responsibilities into silos. The front-end AllScale API provides a simple C++ development environment through a suite of parallel constructs expressions denoting tasks operating concurrently. This interfaces with the other components of the toolchain (core-level API, compiler and runtime) which manages tasks related to the machine and system level, hidden to the user. The paper describes the development of two large-scale parallel applications within the AllScale API, namely, an advection– diffusion model with data assimilation and a Lagrangian space-weather simulation model based on a particle-in-cell method. We present mathematical formulations and implementations and evaluate parallel constructs developed using the AllScale API. The performance of the applications from the perspective of both parallel scalability, and more importantly productivity are assessed. We demonstrate how the AllScale API can greatly improve developer productivity while maintaining parallel performance in two applications with distinct numerical characteristics. Code complexity metrics demonstrate reduction in application specific implementations of up to 30% while performance tests on three different compute systems demonstrate comparable parallel scalability to an MPI version of the code.
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