A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator.

Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented.

Clinical implications of in silico mathematical modeling for glioblastoma: a critical review.

Glioblastoma remains a clinical challenge in spite of years of extensive research. Novel approaches are needed in order to integrate the existing knowledge. This is the potential role of mathematical oncology.

Sewer-mining: A water reuse option supporting circular economy, public service provision and entrepreneurship.

Water scarcity, either due to increased urbanisation or climatic variability, has motivated societies to reduce pressure on water resources mainly by reducing water demand. However, this practice alone is not sufficient to guarantee the quality of life that high quality water services underpin, especially within a context of increased urbanisation. As such, the idea of water reuse has been gaining momentum for some time and has recently found a more general context within the idea of the Circular Economy.

A Numerical Handling of the Boundary Conditions Imposed by the Skull on an Inhomogeneous Diffusion-Reaction Model of Glioblastoma Invasion Into the Brain: Clinical Validation Aspects.

A novel explicit triscale reaction-diffusion numerical model of glioblastoma multiforme tumor growth is presented. The model incorporates the handling of Neumann boundary conditions imposed by the cranium and takes into account both the inhomogeneous nature of human brain and the complexity of the skull geometry. The finite-difference time-domain method is adopted.

A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of Oncology and Medicine.

The plethora of available disease prediction models and the ongoing process of their application into clinical practice - following their clinical validation - have created new needs regarding their efficient handling and exploitation. Consolidation of software implementations, descriptive information, and supportive tools in a single place, offering persistent storage as well as proper management of execution results, is a priority, especially with respect to the needs of large healthcare providers. At the same time, modelers should be able to access these storage facilities under special rights, in order to upgrade and maintain their work.

Mining balance disorders' data for the development of diagnostic decision support systems.

In this work we present the methodology for the development of the EMBalance diagnostic Decision Support System (DSS) for balance disorders. Medical data from patients with balance disorders have been analysed using data mining techniques for the development of the diagnostic DSS. The proposed methodology uses various data, ranging from demographic characteristics to clinical examination, auditory and vestibular tests, in order to provide an accurate diagnosis.

Development of a clinical decision support system using genetic algorithms and Bayesian classification for improving the personalised management of women attending a colposcopy room.

Cervical cancer (CxCa) is often the result of underestimated abnormalities in the test Papanicolaou (Pap test). The recent advances in the study of the human papillomavirus (HPV) infection (the necessary cause for CxCa development) have guided clinical practice to add HPV related tests alongside the Pap test. In this way, today, HPV DNA testing is well accepted as an ancillary test and it is used for the triage of women with abnormal findings in cytology.

Personalised management of women with cervical abnormalities using a clinical decision support scoring system.

Objectives: To develop a clinical decision support scoring system (DSSS) based on artificial neural networks (ANN) for personalised management of women with cervical abnormalities.

Methods: We recruited women with cervical abnormalities and healthy controls that attended for opportunistic screening between 2006 and 2014 in 3 University Hospitals. We prospectively collected detailed patient characteristics, the colposcopic impression and performed a series of biomarkers using a liquid-based cytology sample.

Numerical simulation of vascular tumour growth under antiangiogenic treatment: addressing the paradigm of single-agent bevacizumab therapy with the use of experimental data.

Background: Antiangiogenic agents have been recently added to the oncological armamentarium with bevacizumab probably being the most popular representative in current clinical practice. The elucidation of the mode of action of these agents is a prerequisite for personalized prediction of antiangiogenic treatment response and selection of patients who may benefit from this kind of therapy. To this end, having used as a basis a preexisting continuous vascular tumour growth model which addresses the targeted nature of antiangiogenic treatment, we present a paper characterized by the following three features.

Differentiation resistance through altered retinoblastoma protein function in acute lymphoblastic leukemia: in silico modeling of the deregulations in the G1/S restriction point pathway.

Background: As in many cancer types, the G1/S restriction point (RP) is deregulated in Acute Lymphoblastic Leukemia (ALL). Hyper-phosphorylated retinoblastoma protein (hyper-pRb) is found in high levels in ALL cells. Nevertheless, the ALL lymphocyte proliferation rate for the average patient is surprisingly low compared to its normal counterpart of the same maturation level.

Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future.

Objective: This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics.

Study Design: A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed.

Two 27 MHz Simple Inductive Loops, as Hyperthermia Treatment Applicators: Theoretical Analysis and Development.

Background: Deep heating is still the main subject for research in hyperthermia treatment.

Aim: The purpose of this study was to develop and analyze a simple loop as a heating applicator.

Methods: The performance of two 27 MHz inductive loop antennas as potential applicators in hyperthermia treatment was studied theoretically as well as experimentally in phantoms.

Imaging of Conductivity Changes of Excitable Tissues Based on Focused Passive Microwave.

Aims: Modeling of ionic distribution fluctuations of excitable tissues based on data elicited using focused microwave radiometry.

Methodology: Focused Microwave Radiometry implemented to carry out measurements of in depth body temperature distributions, may provide the capability of sensing local electrical conductivity fluctuations during the cycle of actions potentials in the case of brain excitable cell clusters. An analog beamformer consisting of a conductive inner-surface ellipsoidal cavity is used to focus the chaotic-black body radiation emerging from human tissues by providing convergence of the electromagnetic energy from one focus area where the phantom or subject is placed, to the other where the antennas of sensitive radiometric receivers are positioned.

In Silico Neuro-Oncology: Brownian Motion-Based Mathematical Treatment as a Potential Platform for Modeling the Infiltration of Glioma Cells into Normal Brain Tissue.

Intensive glioma tumor infiltration into the surrounding normal brain tissues is one of the most critical causes of glioma treatment failure. To quantitatively understand and mathematically simulate this phenomenon, several diffusion-based mathematical models have appeared in the literature. The majority of them ignore the anisotropic character of diffusion of glioma cells since availability of pertinent truly exploitable tomographic imaging data is limited.

The Ketogenic Diet and Sport: A Possible Marriage?

The ketogenic diet (KD) is used widely as a weight loss strategy and, more rarely, as therapy for some diseases. In many sports, weight control is often necessary (boxing, weightlifting, wrestling, etc.), but the KD usually is not considered.

Effects of n-3 polyunsaturated fatty acids (ω-3) supplementation on some cardiovascular risk factors with a ketogenic Mediterranean diet.

Background: the ketogenic diet (KD) has become a widely used nutritional approach for weight loss. Some of the KD's positive effects on metabolism and cardiovascular risk factors are similar to those seen after n-3 polyunsaturated fatty acids (ω-3) supplementation. We hypothesized that a ketogenic Mediterranean diet with phytoextracts combined with ω-3 supplementation may have increased positive effects on cardiovascular risk factors and inflammation.

PPARα gene variants as predicted performance-enhancing polymorphisms in professional Italian soccer players.

Background: The PPARα gene encodes the peroxisome proliferator-activator receptor alpha, a central regulator of expression of other genes involved in fatty acid metabolism. The purpose of this study was to determine the prevalence of G allele of the PPARα intron 7 G/C polymorphism (rs4253778) in professional Italian soccer players.

Methods: Sixty professional soccer players and 30 sedentary volunteers were enrolled in the study.

Management and modeling of balance disorders using decision support systems: the EMBALANCE project.

In this work, we present the concept, the methodological ideas and the architecture of the EMBALANCE platform. EMBALANCE platform extends existing but generic and currently uncoupled balance modeling activities, leading to a multi-scale and patient-specific balance Hypermodel, which is incorporated to a Decision Support System (DSS), towards the early diagnosis, prediction and the efficient treatment planning of balance disorders. Various data feed the intelligent system increasing the dimensionality and personalization of the system.

Computational Horizons In Cancer (CHIC): Developing Meta- and Hyper-Multiscale Models and Repositories for 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/).

PAT: an intelligent authoring tool for facilitating clinical trial design.

Great investments are made by both private and public funds and a wealth of research findings is published, the research and development pipeline phases quite low productivity and tremendous delays. In this paper, we present a novel authoring tool which has been designed and developed for facilitating study design. Its underlying models are based on a thorough analysis of existing clinical trial protocols (CTPs) and eligibility criteria (EC) published in clinicaltrials.

An intelligent clinical decision support system for patient-specific predictions to improve cervical intraepithelial neoplasia detection.

Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement.

Towards in silico oncology: adapting a four dimensional nephroblastoma treatment model to a clinical trial case based on multi-method sensitivity analysis.

In the past decades a great progress in cancer research has been made although medical treatment is still widely based on empirically established protocols which have many limitations. Computational models address such limitations by providing insight into the complex biological mechanisms of tumor progression. A set of clinically-oriented, multiscale models of solid tumor dynamics has been developed by the In Silico Oncology Group (ISOG), Institute of Communication and Computer Systems (ICCS)-National Technical University of Athens (NTUA) to study cancer growth and response to treatment.