CARDIOCARE: An integrated platform for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity.

Breast cancer (BC) remains the most diagnosed cancer in women, accounting for 12% of new annual cancer cases in Europe and worldwide. Advances in surgery, radiotherapy and systemic treatment have resulted in improved clinical outcomes and increased survival rates in recent years. However, BC therapy-related cardiotoxicity, may severely impact short- and long-term quality of life and survival. This study presents the CARDIOCARE platform and its main components, which by integrating patient-specific data from different categories, data from patient-oriented eHealth applications and wearable devices, and by employing advanced data mining and machine learning approaches, provides the healthcare professionals with a valuable tool for effectively managing BC patients and preventing or alleviating treatment induced cardiotoxicity.Clinical Relevance- Through the adoption of CARDIOCARE platform healthcare professionals are able to stratify patients for their risk for cardiotoxicity and timely apply adequate interventions to prevent its onset.

BEM evaluation of surface octahedral strains and internal strain gradients in 3D-printed scaffolds used for bone tissue regeneration.

Most of the mechnoregulatory computational models appearing so far in tissue engineering for bone healing predictions, utilize as regulators for cell differentiation mainly the octahedral volume strains and the interstitial fluid velocity calculated at any point of the fractured bone area and controlled by empirical constants concerning these two parameters. Other stimuli like the electrical and chemical signaling of bone constituents are covered by those two regulatory fields. It is apparent that the application of the same mechnoregulatory computational models for bone healing predictions in scaffold-aided regeneration is questionable since the material of a scaffold disturbs the signaling pathways developed in the environment of bone fracture. Thus, the goal of the present work is to evaluate numerically two fields developed in the body of two different compressed scaffolds, which seem to be proper for facilitating cell sensing and improving cell viability and cell seeding efficiency. These two fields concern the surface octahedral strains that the cells attached to the scaffold can experience and the internal strain gradients that create electrical pathways due to flexoelectric phenomenon. Both fields are evaluated with the aid of the Boundary Element Method (BEM), which is ideal for evaluating with high accuracy surface strains and stresses as well as strain gradients appearing throughout the analyzed elastic domain.

Coupled FEA Model with Continuum Damage Mechanics for the Degradation of Polymer-based coatings on Drug-Eluting Stents.

Drug-Eluting Stents (DES) are commonly used in Coronary angioplasty procedures to reduce the phenomenon of restenosis. Numerical simulations are proven to be a useful tool to the Bioengineering community in computing the mechanical performance of stents. BioCoStent is a research project aiming to develop a DES with retinoic acid (RA) coating, in the frame of which FEAC is responsible for the in silico numerical simulation of the coating's degradation in terms of Finite Element Analysis (FEA). The coatings under study are poly(lactic-co-glycolic acid) (PLGA) and polylactide (PLA). The FEA is based on the Continuum Damage Mechanics (CDM) theory and considers a mechanistic model for polymer bulk degradation of the coatings. The degradation algorithm is implemented on the NX Nastran solver through a user-defined material UMAT subroutine. This paper describes the developed numerical model to compute the degradation of biodegradable coatings on DES. The transient numerical model provides useful insight into the critical areas with regards to the scalar damage of the coatings. The FEA results present a complete degradation of polymers after several weeks.

FEA of Drug-Eluting Stents and Sensitivity Analysis of a Continuum Damage Model for the Degradation of PLGA Coating.

Drug-Eluting Stents (DES) are commonly used in coronary angioplasty operations as a solution against artery stenosis and restenosis. Computational Bioengineering allows for the in-silico analysis of their performance. The scope of this work is to develop a DES Digital Twin, focusing on the mechanical integrity of its biodegradable coating throughout the operational lifecycle. The implementation leverages the Finite Element Method (FEM) to compute the developed mechanical stress field on the DES during the inflation/deflation stage, followed by the degradation of the polymer-based coating. The simulation of the degradation process is based on a Continuum Damage Mechanics (CDM) model that considers bulk degradation. The CDM algorithm is implemented on the NX Nastran solver through a user-defined material (UMAT) subroutine. For benchmarking purposes and to compare with the baseline design of the BioCoStent project, this conceptual study implements an alternative stent design, to study the effect of the geometry on the developed stresses. Additionally, the effect of the degradation rate on the polymer-based coating's lifecycle is studied via sensitivity analysis.

On-Body Sensor Position Identification with a Simple, Robust and Accurate Method, Validated in Patients with Parkinson's Disease.

The aim of this work is to implement and validate an automated method for the localization of body-worn inertial sensors. Often, body-sensor networks with inertial measurement units (IMU) used in rehabilitation and ambient monitoring of patients with movement disorders, require specific markings or labels for the correct body placement. This introduces a burden, which, especially for ambient monitoring, could lead to errors or reduced adherence. We propose a method to automatically identify sensors attached on a predefined set of body placements, namely, wrists, shanks and torso. The method was used in a multi-site clinical trial with Parkinson's disease patients and in 45 sessions it identified sensor placement on torso, wrists and shanks with 100% accuracy, discriminated between left and right shank with 100% accuracy and between left and right wrist with 98% accuracy. This is remarkable, considering the presence of parkinsonian motor symptoms causing abnormal movement patterns, such as dyskinesia.Clinical Relevance- This method can facilitate home monitoring of patients with movement disorders.

Cryoglobulinemic vasculitis in primary Sjögren's Syndrome: Clinical presentation, association with lymphoma and comparison with Hepatitis C-related disease.

OBJECTIVE: To describe the clinical spectrum of cryoglobulinemic vasculitis (CV) in primary Sjögren's syndrome (pSS), investigate its relation to lymphoma and identify the differences with hepatitis C virus (HCV) related CV. METHODS: From a multicentre study population of consecutive pSS patients, those who had been evaluated for cryoglobulins and fulfilled the 2011 classification criteria for CV were identified retrospectively. pSS-CV patients were matched with pSS patients without cryoglobulins (1:2) and HCV-CV patients (1:1). Clinical, laboratory and outcome features were analyzed. A data driven logistic regression model was applied for pSS-CV patients and their pSS cryoglobulin negative controls to identify independent features associated with lymphoma. RESULTS: 1083 pSS patients were tested for cryoglobulins. 115 (10.6%) had cryoglobulinemia and 71 (6.5%) fulfilled the classification criteria for CV. pSS-CV patients had higher frequency of extraglandular manifestations and lymphoma (OR=9.87, 95% CI: 4.7-20.9) compared to pSS patients without cryoglobulins. Purpura was the commonest vasculitic manifestation (90%), presenting at disease onset in 39% of patients. One third of pSS-CV patients developed B-cell lymphoma within the first 5 years of CV course, with cryoglobulinemia being the strongest independent lymphoma associated feature. Compared to HCV-CV patients, pSS-CV individuals displayed more frequently lymphadenopathy, type II IgMk cryoglobulins and lymphoma (OR = 6.12, 95% CI: 2.7-14.4) and less frequently C4 hypocomplementemia and peripheral neuropathy. CONCLUSION: pSS-CV has a severe clinical course, overshadowing the typical clinical manifestations of pSS and higher risk for early lymphoma development compared to HCV related CV. Though infrequent, pSS-CV constitutes a distinct severe clinical phenotype of pSS.

Designing a mHealth clinical decision support system for Parkinson's disease: a theoretically grounded user needs approach.

BACKGROUND: Despite the established evidence and theoretical advances explaining human judgments under uncertainty, developments of mobile health (mHealth) Clinical Decision Support Systems (CDSS) have not explicitly applied the psychology of decision making to the study of user needs. We report on a user needs approach to develop a prototype of a mHealth CDSS for Parkinson's disease (PD), which is theoretically grounded in the psychological literature about expert decision making and judgement under uncertainty. METHODS: A suite of user needs studies was conducted in 4 European countries (Greece, Italy, Slovenia, the UK) prior to the development of PD_Manager, a mHealth-based CDSS designed for Parkinson's disease, using wireless technology. Study 1 undertook Hierarchical Task Analysis (HTA) including elicitation of user needs, cognitive demands and perceived risks/benefits (ethical considerations) associated with the proposed CDSS, through structured interviews of prescribing clinicians (N = 47). Study 2 carried out computational modelling of prescribing clinicians' (N = 12) decision strategies based on social judgment theory. Study 3 was a vignette study of prescribing clinicians' (N = 18) willingness to change treatment based on either self-reported symptoms data, devices-generated symptoms data or combinations of both. RESULTS: Study 1 indicated that system development should move away from the traditional silos of 'motor' and 'non-motor' symptom evaluations and suggest that presenting data on symptoms according to goal-based domains would be the most beneficial approach, the most important being patients' overall Quality of Life (QoL). The computational modelling in Study 2 extrapolated different factor combinations when making judgements about different questions. Study 3 indicated that the clinicians were equally likely to change the care plan based on information about the change in the patient's condition from the patient's self-report and the wearable devices. CONCLUSIONS: Based on our approach, we could formulate the following principles of mHealth design: 1) enabling shared decision making between the clinician, patient and the carer; 2) flexibility that accounts for diagnostic and treatment variation among clinicians; 3) monitoring of information integration from multiple sources. Our approach highlighted the central importance of the patient-clinician relationship in clinical decision making and the relevance of theoretical as opposed to algorithm (technology)-based modelling of human judgment.

Design and assembly of a chemically switchable and fluorescently traceable light-driven proton pump system for bionanotechnological applications.

Energy-supplying modules are essential building blocks for the assembly of functional multicomponent nanoreactors in synthetic biology. Proteorhodopsin, a light-driven proton pump, is an ideal candidate to provide the required energy in form of an electrochemical proton gradient. Here we present an advanced proteoliposome system equipped with a chemically on-off switchable proteorhodopsin variant. The proton pump was engineered to optimize the specificity and efficiency of chemical deactivation and reactivation. To optically track and characterize the proteoliposome system using fluorescence microscopy and nanoparticle tracking analysis, fluorescenlty labelled lipids were implemented. Fluorescence is a highly valuable feature that enables detection and tracking of nanoreactors in complex media. Cryo-transmission electron microscopy, and correlative atomic force and confocal microscopy revealed that our procedure yields polylamellar proteoliposomes, which exhibit enhanced mechanical stability. The combination of these features makes the presented energizing system a promising foundation for the engineering of complex nanoreactors.

Histological validation of the automated caries detection system (ACDS) in classifying occlusal caries with the ICDAS II system in vitro.

AIM: To compare the diagnostic performance of the automated caries detection system (ACDS) for the detection and diagnosis of occlusal caries with the histological appearance of the lesions. METHODS: Eighteen posterior permanent teeth were used, out of which 40 sections were made and 53 areas were evaluated. Teeth with hypoplastic and/or hypomineralised areas or sealants on the occlusal surfaces were excluded from the study. The teeth that were used for this study were a subgroup of the teeth used in the study that introduced ACDS system. This subgroup consisted of teeth having in their occlusal surfaces early carious lesions classified as international caries detection and scoring system (ICDAS) 0, 1, 2 and 3 after clinical examination by the examiners. Histological preparations were classified by experienced examiners based on the Ekstrand, Ricketts and Kidd (ERK) system and for the respective occlusal surfaces by the ACDS system based on ICDAS II system. There were two threshold limits considered as carious in either system ICDAS ≥ 2 or ≥ 3 and ERK index ≥ 2 or ≥ 3 and all possible combinations were analysed. Statistical methods of weighted version of kappa coefficient, Kendall's tau-b correlation coefficient and p-values using the Fisher's exact method were used at the confidence level of 0.05. RESULTS: Intra-examiner kappa coefficient agreement was 0.87 and 0.89 while the inter-examiner for the two trials were 0.87 and 0.92. The ICDAS3-ERK3 combination between the ACDS and histological sections presented the best agreement with kappa coefficient 0.76, agreement 92.5%, sensitivity 100% and specificity 91.1%. ICDAS3-ERK3 combination between the optical examination of the examiners compared to the histological preparations showed kappa coefficient 0.87, agreement 96.2%, sensitivity 100%, Specificity 95.6%. CONCLUSION: The evidence supports the view that ACDS classification of occlusal surfaces based on the ICDAS system are comparable with classification to that of an examiner and with the histology of the lesion. The use of ACDS has the distinct advantage though of removing the subjectivity of the examiner since it performs the classification without any intervention by him.

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. The system aims to provide decision support for general practitioners (GPs) and experts in the diagnosis of balance disorders as well as to provide recommendations for the appropriate information and data to be requested at each step of the diagnostic process. Detailed results are provided for the diagnosis of 12 balance disorders, both for GPs and experts. Overall, the reported accuracy ranges from 59.3 to 89.8% for GPs and from 74.3 to 92.1% for experts.

Biclustering strategies for genetic marker selection in gynecologic tumor cell lines.

Over the past few decades great interest has been focused on cell lines derived from tumors, because of their usability as models to understand the biology of cancer. At the same time, advanced technologies such as DNA-microarrays have been broadly used to study the expression level of thousands of genes in primary tumors or cancer cell lines in a single experiment. Results from microarray analysis approaches have provided valuable insights into the underlying biology and proven useful for tumor classification, prognostication and prediction. Our approach utilizes biclustering methods for the discovery of genes with coherent expression across a subset of conditions (cell lines of a tumor type). More specifically, we present a novel modification on Cheng & Church's algorithm that searches for differences across the studied conditions, but also enforces consistent intensity characteristics of each cluster within each condition. The application of this approach on a gynecologic panel of cell lines succeeds to derive discriminant groups of compact bi-clusters across four types of tumor cell lines. In this form, the proposed approach is proven efficient for the derivation of tumor-specific markers.

Middle and inner ear modelling: from microCT images to 3D reconstruction and coupling of models.

We present finite element (FE) modeling approaches of ear mechanics including 3-dimensional (3D) reconstruction of the human middle and inner ear. Specifically, we demonstrate a semi-automatic methodology for the 3D reconstruction of the inner ear structures, a FE harmonic response model of the middle ear to predict the stapes footplate frequency response, a 2D FE slice model of the cochlea for the coupled response at the micromechanical level for either acoustic or electrical excitation and a coupled FE middle ear model with a simplified cochlea box model to simulate the basilar membrane velocity in response to acoustic excitation. The proposed methodologies are validated against experimental and literature data and the results are in good agreement.

Cultivation strategies to enhance productivity of Pichia pastoris: A review.

Pichia pastoris, a methylotrophic yeast, is an established system for the production of heterologous proteins, particularly biopharmaceuticals and industrial enzymes. To maximise and optimise the production of recombinant products, recent molecular research has focused on numerous issues including the design of expression vectors, optimisation of gene copy number, co-expression of secretory proteins such as chaperones, engineering of glycosylation and secretory pathways, etc. However, the physiological effects of different cultivation strategies are often difficult to separate from the molecular effects of the gene construct (e.g., cellular stress through over-expression or incorrect post-translational processing). Hence, overall system optimisation is difficult, even though it is urgently required in order to describe and understand the behaviour of new molecular constructs. This review focuses on particular aspects of recombinant protein production related to variations in biomass growth and their implications for strain design and screening, as well as on the concept of rational comparisons between cultivation systems for the development of specific production processes in bioreactors. The relationship between specific formation rates of secreted recombinant proteins, qp, and specific growth rates, µ, has been analysed in a conceptual attempt to compare different systems, particularly those based on AOX1/methanol and GAP/glucose, and this has now evolved into a pivotal concept for bioprocess engineering of P. pastoris.

Nonparametric network design and analysis of disease genes in oral cancer progression.

Biological networks in living organisms can be seen as the ultimate means of understanding the underlying mechanisms in complex diseases, such as oral cancer. During the last decade, many algorithms based on high-throughput genomic data have been developed to unravel the complexity of gene network construction and their progression in time. However, the small size of samples compared to the number of observed genes makes the inference of the network structure quite challenging. In this study, we propose a framework for constructing and analyzing gene networks from sparse experimental temporal data and investigate its potential in oral cancer. We use two network models based on partial correlations and kernel density estimation, in order to capture the genetic interactions. Using this network construction framework on real clinical data of the tissue and blood at different time stages, we identified common disease-related structures that may decipher the association between disease state and biological processes in oral cancer. Our study emphasizes an altered MET (hepatocyte growth factor receptor) network during oral cancer progression. In addition, we demonstrate that the functional changes of gene interactions during oral cancer progression might be particularly useful for patient categorization at the time of diagnosis and/or at follow-up periods.

A decision support system for the treatment of patients with ventricular assist device support.

BACKGROUND: Heart failure (HF) is affecting millions of people every year and it is characterized by impaired ventricular performance, exercise intolerance and shortened life expectancy. Despite significant advancements in drug therapy, mortality of the disease remains excessively high, as heart transplant remains the gold standard treatment for end-stage HF when no contraindications subsist. Traditionally, implanted Ventricular Assist Devices (VADs) have been employed in order to provide circulatory support to patients who cannot survive the waiting time to transplantation, reducing the workload imposed on the heart. In many cases that process could recover its contractility performance. OBJECTIVES: The SensorART platform focuses on the management and remote treatment of patients suffering from HF. It provides an interoperable, extendable and VAD-independent solution, which incorporates various hardware and software components in a holistic approach, in order to improve the quality of the patients' treatment and the workflow of the specialists. This paper focuses on the description and analysis of Specialist's Decision Support System (SDSS), an innovative component of the SensorART platform. METHODS: The SDSS is a Web-based tool that assists specialists on designing the therapy plan for their patients before and after VAD implantation, analyzing patients' data, extracting new knowledge, and making informative decisions. RESULTS: SDSS offers support to medical and VAD experts through the different phases of VAD therapy, incorporating several tools covering all related fields; Statistics, Association Rules, Monitoring, Treatment, Weaning, Speed and Suction Detection. CONCLUSIONS: SDSS and its modules have been tested in a number of patients and the results are encouraging.

Identification of altered MET network in oral cancer progression based on nonparametric network design.

Oral cancer is characterized by multiple genetic events such as alterations of a number of oncogenes and tumour suppressor genes. The aim of this study is to identify genes and their functional interactions that may play a crucial role on a specific disease-state, especially during oral cancer progression. We examine gene interaction networks on blood genomic data, obtained from twenty three oral cancer patients at four different time stages. We generate the gene-gene networks from sparse experimental temporal data using two methods, Partial Correlations and Kernel Density Estimation, in order to capture genetic interactions. The network study reveals an altered MET (hepatocyte growth factor receptor) network during oral cancer progression, which is further analyzed in relation to other studies.

ART-ML: a new markup language for modelling and representation of biological processes in cardiovascular diseases.

BACKGROUND: With an ever increasing number of biological models available on the internet, a standardized modelling framework is required to allow information to be accessed and visualized. OBJECTIVE: In this paper we propose a novel Extensible Markup Language (XML) based format called ART-ML that aims at supporting the interoperability and the reuse of models of geometry, blood flow, plaque progression and stent modelling, exported by any cardiovascular disease modelling software. ART-ML has been developed and tested using ARTool. ARTool is a platform for the automatic processing of various image modalities of coronary and carotid arteries. METHODS: The images and their content are fused to develop morphological models of the arteries in 3D representations. All the above described procedures integrate disparate data formats, protocols and tools. ART-ML proposes a representation way, expanding ARTool, for interpretability of the individual resources, creating a standard unified model for the description of data and, consequently, a format for their exchange and representation that is machine independent. More specifically, ARTool platform incorporates efficient algorithms which are able to perform blood flow simulations and atherosclerotic plaque evolution modelling. Integration of data layers between different modules within ARTool are based upon the interchange of information included in the ART-ML model repository. ART-ML provides a markup representation that enables the representation and management of embedded models within the cardiovascular disease modelling platform, the storage and interchange of well-defined information. RESULTS: The corresponding ART-ML model incorporates all relevant information regarding geometry, blood flow, plaque progression and stent modelling procedures. All created models are stored in a model repository database which is accessible to the research community using efficient web interfaces, enabling the interoperability of any cardiovascular disease modelling software models. CONCLUSIONS: ART-ML can be used as a reference ML model in multiscale simulations of plaque formation and progression, incorporating all scales of the biological processes.

Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression.

Data-driven techniques have recently drawn significant interest in the predictive modeling of subcutaneous (s.c.) glucose concentration in type 1 diabetes. In this study, the s.c. glucose prediction is treated as a multivariate regression problem, which is addressed using support vector regression (SVR). The proposed method is based on variables concerning: (i) the s.c. glucose profile, (ii) the plasma insulin concentration, (iii) the appearance of meal-derived glucose in the systemic circulation, and (iv) the energy expenditure during physical activities. Six cases corresponding to different combinations of the aforementioned variables are used to investigate the influence of the input on the daily glucose prediction. The proposed method is evaluated using a dataset of 27 patients in free-living conditions. 10-fold cross validation is applied to each dataset individually to both optimize and test the SVR model. In the case where all the input variables are considered, the average prediction errors are 5.21, 6.03, 7.14 and 7.62 mg/dl for 15, 30, 60 and 120 min prediction horizons, respectively. The results clearly indicate that the availability of multivariable data and their effective combination can significantly increase the accuracy of both short-term and long-term predictions.

3D reconstruction of coronary arteries using frequency domain optical coherence tomography images and biplane angiography.

The aim of this study is to describe a new method for three-dimensional (3D) reconstruction of coronary arteries using Frequency Domain Optical Coherence Tomography (FD-OCT) images. The rationale is to fuse the information about the curvature of the artery, derived from biplane angiographies, with the information regarding the lumen wall, which is produced from the FD-OCT examination. The method is based on a three step approach. In the first step the lumen borders in FD-OCT images are detected. In the second step a 3D curve is produced using the center line of the vessel from the two biplane projections. Finally in the third step the detected lumen borders are placed perpendicularly onto the path based on the centroid of each lumen border. The result is a 3D reconstructed artery produced by all the lumen borders of the FD-OCT pullback representing the 3D arterial geometry of the vessel.

Patient specific multiscale modelling for plaque formation and progression.

We present a three-dimensional model of plaque formation and progression that was tested in a set of patients who underwent coronary Computed Tomography angiography (CTA) for anginal symptoms. The 3D blood flow is described by the Navier-Stokes equations, together with the continuity equation. Mass transfer within the blood lumen and through the arterial wall is coupled with the blood flow and is modeled by a convection-diffusion equation. The Low Density Lipoprotein (LDL) transports in lumen of the vessel and through the vessel tissue (which has a mass consumption term) are coupled by Kedem-Katchalsky equations. The inflammatory process is modeled using three additional reaction-diffusion partial differential equations. A full three-dimensional model was created. Furthermore, features potentially affecting plaque growth, such as patient risk score, circulating biomarkers, localization and composition of the initial plaque, and coronary vasodilating capability were also investigated. The proof of concept of the model effectiveness was assessed 6 months after the baseline evaluation.