1,880 research outputs found

    Smartphone sensors for monitoring cancer-related Quality of Life: App design, EORTC QLQ-C30 mapping and feasibility study in healthy subjects

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    [EN] Quality of life (QoL) indicators are now being adopted as clinical outcomes in clinical trials on cancer treatments. Technology-free daily monitoring of patients is complicated, time-consuming and expensive due to the need for vast amounts of resources and personnel. The alternative method of using the patients¿ own phones could reduce the burden of continuous monitoring of cancer patients in clinical trials. This paper proposes monitoring the patients¿ QoL by gathering data from their own phones. We considered that the continuous multiparametric acquisition of movement, location, phone calls, conversations and data use could be employed to simultaneously monitor their physical, psychological, social and environmental aspects. An open access phone app was developed (Human Dynamics Reporting Service (HDRS)) to implement this approach. We here propose a novel mapping between the standardized QoL items for these patients, the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and define HDRS monitoring indicators. A pilot study with university volunteers verified the plausibility of detecting human activity indicators directly related to QoL.Funding for this study was provided by the authors' various departments, and partially by the CrowdHealth Project (Collective Wisdom Driving Public Health Policies (727560)) and the MTS4up project (DPI2016-80054-R).Asensio Cuesta, S.; Sánchez-García, Á.; Conejero, JA.; Sáez Silvestre, C.; Rivero-Rodriguez, A.; Garcia-Gomez, JM. (2019). Smartphone sensors for monitoring cancer-related Quality of Life: App design, EORTC QLQ-C30 mapping and feasibility study in healthy subjects. International Journal of Environmental research and Public Health. 16(3):1-18. https://doi.org/10.3390/ijerph16030461S118163Number of Smartphone Users Worldwide from 2014 to 2020 (in Billions)https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/Mirkovic, J., Kaufman, D. R., & Ruland, C. M. (2014). Supporting Cancer Patients in Illness Management: Usability Evaluation of a Mobile App. JMIR mHealth and uHealth, 2(3), e33. doi:10.2196/mhealth.3359Xing Su, Hanghang Tong, & Ping Ji. (2014). Activity recognition with smartphone sensors. Tsinghua Science and Technology, 19(3), 235-249. doi:10.1109/tst.2014.6838194Schmitz Weiss, A. (2013). Exploring News Apps and Location-Based Services on the Smartphone. Journalism & Mass Communication Quarterly, 90(3), 435-456. doi:10.1177/1077699013493788Higgins, J. P. (2016). Smartphone Applications for Patients’ Health and Fitness. The American Journal of Medicine, 129(1), 11-19. doi:10.1016/j.amjmed.2015.05.038Rivenson, Y., Ceylan Koydemir, H., Wang, H., Wei, Z., Ren, Z., Günaydın, H., … Ozcan, A. (2018). Deep Learning Enhanced Mobile-Phone Microscopy. ACS Photonics, 5(6), 2354-2364. doi:10.1021/acsphotonics.8b00146Priye, A., Ball, C. S., & Meagher, R. J. (2018). Colorimetric-Luminance Readout for Quantitative Analysis of Fluorescence Signals with a Smartphone CMOS Sensor. Analytical Chemistry, 90(21), 12385-12389. doi:10.1021/acs.analchem.8b03521Measuring Quality of Life for Cancer Patients: Where Are We Today and Where Are We Headed Tomorrow?http://blog.mdsol.com/measuring-quality-of-life-for-cancer-patients-where-are-we-today-and-where-are-we-headed-tomorrow/Zulueta, J., Piscitello, A., Rasic, M., Easter, R., Babu, P., Langenecker, S. A., … Leow, A. (2018). Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. 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    Role of SODC protein in antineoplastic drug resistance

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    Cells need homeostasis to survive, therefore, they use the different pathways available to obtain it. The SODC protein overexpression, which is implicated in this process, suggests that could be implicated in the process of acquisition resistance during chemotherapy.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing

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    In the last decade, various robotic platforms have been introduced that could support delicate retinal surgeries. Concurrently, to provide semantic understanding of the surgical area, recent advances have enabled microscope-integrated intraoperative Optical Coherent Tomography (iOCT) with high-resolution 3D imaging at near video rate. The combination of robotics and semantic understanding enables task autonomy in robotic retinal surgery, such as for subretinal injection. This procedure requires precise needle insertion for best treatment outcomes. However, merging robotic systems with iOCT introduces new challenges. These include, but are not limited to high demands on data processing rates and dynamic registration of these systems during the procedure. In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes. Our method consists of an instrument pose estimation method, an online registration between the robotic and the iOCT system, and trajectory planning tailored for navigation to an injection target. We also introduce intelligent virtual B-scans, a volume slicing approach for rapid instrument pose estimation, which is enabled by Convolutional Neural Networks (CNNs). Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method. Finally, we discuss identified challenges in this work and suggest potential solutions to further the development of such systems

    Replica Cluster Variational Method: the Replica Symmetric solution for the 2D random bond Ising model

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    We present and solve the Replica Symmetric equations in the context of the Replica Cluster Variational Method for the 2D random bond Ising model (including the 2D Edwards-Anderson spin glass model). First we solve a linearized version of these equations to obtain the phase diagrams of the model on the square and triangular lattices. In both cases the spin-glass transition temperatures and the tricritical point estimations improve largely over the Bethe predictions. Moreover, we show that this phase diagram is consistent with the behavior of inference algorithms on single instances of the problem. Finally, we present a method to consistently find approximate solutions to the equations in the glassy phase. The method is applied to the triangular lattice down to T=0, also in the presence of an external field.Comment: 22 pages, 11 figure

    Proteins involved in carbon metabolism induce bleomycin resistance

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    S. cerevisiae acquires bleomycin resistance after a long-term exposure to this drug which causes overexpression of TPIS protein. It could be involved in the resistance process. For this reason, it could be good candidates as bleomycin chemo-resistance biomarker. Knowing of the homologous gene in humans facilitates more studies of the expression of this protein in tumors.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Observation of the decay Bs0→D¯0K+K−

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    The first observation of the B0 s → D¯ 0KþK− decay is reported, together with the most precise branching fraction measurement of the mode B0 → D¯ 0KþK−. The results are obtained from an analysis of pp collision data corresponding to an integrated luminosity of 3.0 fb−1. The data were collected with the LHCb detector at center-of-mass energies of 7 and 8 TeV. The branching fraction of the B0 → D¯ 0KþK− decay is measured relative to that of the decay B0 → D¯ 0πþπ− to be BðB0→D¯ 0KþK−Þ BðB0→D¯ 0πþπ−Þ ¼ ð6.9 0.4 0.3Þ%, where the first uncertainty is statistical and the second is systematic. The measured branching fraction of the B0 s → D¯ 0KþK− decay mode relative to that of the corresponding B0 decay is BðB0 s→D¯ 0KþK−Þ BðB0→D¯ 0KþK−Þ ¼ ð93.0 8.9 6.9Þ%. Using the known branching fraction of B0 → D¯ 0πþπ−, the values of BðB0 →D¯ 0KþK−Þ¼ð6.10.40.30.3Þ×10−5 and BðB0 s →D¯ 0KþK−Þ¼ð5.70.50.40.5Þ×10−5 are obtained, where the third uncertainties arise from the branching fraction of the decay modes B0 → D¯ 0πþπ− and B0 → D¯ 0KþK−, respectively
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