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End date: 2022 Ă— Start date: 2020 Ă— DDC: 004 Ă— DDC: 610 Ă— Has files: Yes Ă—

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    A Data-Driven Approach for Analyzing Healthcare Services Extracted from Clinical Records
    (Piscataway, NJ : IEEE, 2020) Scurti, Manuel; Menasalvas-Ruiz, Ernestina; Vidal, Maria-Esther; Torrente, Maria; Vogiatzis, Dimitrios; Paliouras, George; Provencio, Mariano; RodrĂ­guez-GonzĂ¡lez, Alejandro; Seco de Herrera, Alba GarcĂ­a; RodrĂ­guez GonzĂ¡lez, Alejandro; Santosh, K.C.; Temesgen, Zelalem; Soda, Paolo
    Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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    Experimental validation of computerised models of clustering of platelet glycoprotein receptors that signal via tandem SH2 domain proteins
    (San Francisco, Calif. : Public Library of Science, 2022) Maqsood, Zahra; Clark, Joanne C.; Martin, Eleyna M.; Cheung, Yam Fung Hilaire; MorĂ¡n, Luis A.; Watson, Sean E. T.; Pike, Jeremy A.; Di, Ying; Poulter, Natalie S.; Slater, Alexandre; Lange, Bodo M. H.; Nieswandt, Bernhard; Eble, Johannes A.; Tomlinson, Mike G.; Owen, Dylan M.; Stegner, David; Bridge, Lloyd J.; Wierling, Christoph; Watson, Steve P.
    The clustering of platelet glycoprotein receptors with cytosolic YxxL and YxxM motifs, including GPVI, CLEC-2 and PEAR1, triggers activation via phosphorylation of the conserved tyrosine residues and recruitment of the tandem SH2 (Src homology 2) domain effector proteins, Syk and PI 3-kinase. We have modelled the clustering of these receptors with monovalent, divalent and tetravalent soluble ligands and with transmembrane ligands based on the law of mass action using ordinary differential equations and agent-based modelling. The models were experimentally evaluated in platelets and transfected cell lines using monovalent and multivalent ligands, including novel nanobody-based divalent and tetravalent ligands, by fluorescence correlation spectroscopy. Ligand valency, receptor number, receptor dimerisation, receptor phosphorylation and a cytosolic tandem SH2 domain protein act in synergy to drive receptor clustering. Threshold concentrations of a CLEC-2-blocking antibody and Syk inhibitor act in synergy to block platelet aggregation. This offers a strategy for countering the effect of avidity of multivalent ligands and in limiting off-target effects.