Longevity, Growth and Intergenerational Equity - The Deterministic Case

Challenges raised by ageing (increasing longevity) have prompted policy debates featuring policy proposals justified by reference to some notion of intergenerational equity. However, very different policies ranging from pre-savings to indexation of retirement ages have been justified in this way. We develop an overlapping generations model in continuous time which encompasses different generations with different mortality rates and thus longevity. Allowing for trend increases in both longevity and productivity, we address the issue of intergenerational equity under a utilitarian criterion when future generations are better off in terms of both material and non-material well being. Increases in productivity and longevity are shown to have very different implications for intergenerational distribution.

DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images

Copyright @ Skounakis et al.This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: THE PLATFORM, A MANUAL AND TUTORIAL VIDEOS ARE AVAILABLE AT: http://biomodeling.ics.forth.gr. It is free to use under the GNU General Public License

Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas

Objective: This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2–4) from 3 different centers. Methods: To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space. Results: The Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively. Conclusion: The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation

Important HIV-associated conditions in HIV-infected infants and children

This article is the last in a series of 6 articles that discussed the management of HIV-infected children in a clinically orientated, practical and concise fashion. The topics covered previously include; 1) Preventing and diagnosing HIV-infection in infants and children, 2) Initiating anti-retroviral therapy in HIV-infected infants and children, 3) Maintaining HIV-infected infants and children on anti-retroviral therapy, 4) Common opportunistic infection in HIV-infected children: Part 1-respiratory infections and 5) Part 2 non-respiratory infections. South African Family Practice Vol. 49 (4) 2007: pp.19-2

Dealing with diversity in computational cancer modeling.

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology

Psycho-emotional tools for better treatment adherence and therapeutic outcomes for cancer patients

Personalized medicine should target not only the genetic and clinical aspects of the individual patients but also the different cognitive, psychological, family and social factors involved in various clinical choices. To this direction, in this paper, we present instruments to assess the psycho-emotional status of cancer patients and to evaluate the resilience in their family constructing in such a way an augmented patient profile. Using this profile, 1) information provision can be tailored according to patients characteristics; 2) areas of functioning can be monitored both by the patient and by the clinicians, providing suggestions and alerts; 3) personalized decision aids can be develop to increase patient's participation in the consultation process with their physicians and improve their satisfaction and involvement in the decision-making process. Our preliminary evaluation shows promising results and the potential benefits of the tools

Development of interactive empowerment services in support of personalised medicine

In an epoch where shared decision making is gaining importance, a patient\u2019s commitment to and knowledge about his/her health condition is becoming more and more relevant. Health literacy is one of the most important factors in enhancing the involvement of patients in their care. Nevertheless, other factors can impair patient processing and understanding of health information: psychological aspects and cognitive style may affect the way patients approach, select, and retain information. This paper describes the development and validation of a short and easy to fill-out questionnaire that measures and collects psycho-cognitive information about patients, named ALGA-C. ALGA-C is a multilingual, multidevice instrument, and its validation was carried out in healthy people and breast cancer patients. In addition to the aforementioned questionnaire, a patient profiling mechanism has also been developed. The ALGA-C Profiler enables physicians to rapidly inspect each patient\u2019s individual cognitive profile and see at a glance the areas of concern. With this tool, doctors can modulate the language, vocabulary, and content of subsequent discussions with the patient, thus enabling easier understanding by the patient. This, in turn, helps the patient formulate questions and participate on an equal footing in the decision-making processes. Finally, a preview is given on the techniques under consideration for exploiting the constructed patient profile by a personal health record (PHR). Predefined rules will use a patient\u2019s profile to personalise the contents of the information presented and to customise ways in which users complete their tasks in a PHR system. This optimises information delivery to patients and makes it easier for the patient to decide what is of interest to him/her at the moment

Computational horizons in cancer (CHIC) : developing meta- and hyper-multiscale models and repositories for in Silico Oncology - a brief technical outline of the project

This paper briefly outlines the aim, the objectives, the architecture and the main building blocks of the ongoing large scale integrating transatlantic research project CHIC (http://chic-vph.eu/)

Mammographic density, breast cancer risk and risk prediction

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models