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.

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.

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.

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.

Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques.

In this work, the effect of an image enhancement processing stage and the parameter tuning of a computer-aided detection (CAD) system for the detection of microcalcifications in mammograms is assessed. Five (5) image enhancement algorithms were tested introducing the contrast-limited adaptive histogram equalization (CLAHE), the local range modification (LRM) and the redundant discrete wavelet (RDW) linear stretching and shrinkage algorithms. CAD tuning optimization was targeted to the percentage of the most contrasted pixels and the size of the minimum detectable object which could satisfactorily represent a microcalcification. The highest performance in two mammographic datasets, were achieved for LRM (A(Z)=0.932) and the wavelet-based linear stretching (A(Z)=0.926) methodology.

Phase resetting in one-dimensional model of the sinoatrial node.

In this paper, we use a one-dimensional model of the rabbit sinoatrial node (SAN), and we investigate the response of the model to hyperpolarizing and depolarizing stimulus. Depending on the stimulus timing, either a delay or an advance in the occurrence of next action potential is produced. This resetting behavior of the model is quantified in terms of phase transition curves (PTCs) for short electrical current pulses of varying amplitude which span the whole period. The main focus of this paper is to compare the dynamic properties of the spatially extended system and the single cell model. The detailed analysis of the results provides new insights in the understanding of the transition from the theoretical single cell models to the spatially extended systems.

A Kalman filter based methodology for EEG spike enhancement.

In this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives.

Phase response characteristics of sinoatrial node cells.

In this work, the dynamic response of the sinoatrial node (SAN), the natural pacemaker of the heart, to short external stimuli is investigated using the Zhang et al. model. The model equations are solved twice for the central cell and for the peripheral cell. A short current pulse is applied to reset the spontaneous rhythmic activity of the single sinoatrial node cell. Depending on the stimulus timing either a delay or an advance in the occurrence of next action potential is produced. This resetting behavior is quantified in terms of phase transition curves (PTCs) for short electrical current pulses of varying amplitude which span the whole period. For low stimulus amplitudes the transition from advance to delay is smooth, while at higher amplitudes abrupt changes and discontinuities are observed in PTCs. Such discontinuities reveal critical stimuli, the application of which can result in annihilation of activity in central SAN cells. The detailed analysis of the ionic mechanisms involved in its resetting behavior of sinoatrial node cell models provides new insight into the dynamics and physiology of excitation of the sinoatrial node of the heart.

The 5A structure of heterologously expressed plant aquaporin SoPIP2;1.

SoPIP2;1 is one of the major integral proteins in spinach leaf plasma membranes. In the Xenopus oocyte expression system its water channel activity is regulated by phosphorylation at the C terminus and in the first cytosolic loop. To assess its structure, SoPIP2;1 was heterologously expressed in Pichia pastoris as a His-tagged protein and in the non-tagged form. Both forms were reconstituted into 2D crystals in the presence of lipids. Tubular crystals and double-layered crystalline sheets of non-tagged SoPIP2;1 were observed and analyzed by cryo-electron microscopy. Crystalline sheets were highly ordered and diffracted electrons to a resolution of 2.96A. High-resolution projection maps of tilted specimens provided a 3D structure at 5A resolution. Superposition of the SoPIP2;1 potential map with the atomic model of AQP1 demonstrates the generally well conserved overall structure of water channels. Differences concerning the extracellular loop A explain the particular crystal contacts between oppositely oriented membrane sheets of SoPIP2;1 2D crystals, and may have a function in rapid volume changes observed in stomatal guard cells or mesophyll protoplasts. This crystal packing arrangement provides access to the phosphorylated C terminus as well as the loop B phosphorylation site for studies of channel gating.

A novel method for automated EMG decomposition and MUAP classification.

OBJECTIVE: This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. METHODOLOGY: The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (EMG) recordings, (ii) MUAP detection and clustering and (iii) MUAP classification. RESULTS: The approach has been validated using a dataset of EMG recordings and an annotated collection of MUAPs. The correct identification rate for MUAP clustering is 93, 95 and 92% for normal, myopathic and neuropathic, respectively. Ninety-one percent of the superimposed MUAPs were correctly identified. The obtained accuracy for MUAP classification is about 86%. CONCLUSION: The proposed method, apart from efficient EMG decomposition addresses automatic MUAP classification to neuropathic, myopathic or normal classes directly from raw EMG signals.

A method for classification of transient events in EEG recordings: application to epilepsy diagnosis.

OBJECTIVES: The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. METHODS: A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. RESULTS: The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. CONCLUSIONS: The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

Conformations of the rhodopsin third cytoplasmic loop grafted onto bacteriorhodopsin.

BACKGROUND: The third cytoplasmic loop of rhodopsin (Rho EF) is important in signal transduction from the retinal in rhodopsin to its G protein, transducin. This loop also interacts with rhodopsin kinase, which phosphorylates light-activated rhodopsin, and arrestin, which displaces transducin from light-activated phosphorylated rhodopsin. RESULTS: We replaced eight residues of the EF loop of bacteriorhodopsin (BR) with 24 residues from the third cytoplasmic loop of bovine Rho EF. The surfaces of purple membrane containing the mutant BR (called IIIN) were imaged by atomic force microscopy (AFM) under physiological conditions to a resolution of 0.5-0.7 nm. The crystallinity and extracellular surface of IIIN were not perturbed, and the cytoplasmic surface of IIIN increased in height compared with BR, consistent with the larger loop. Ten residues of Rho EF were excised by V8 protease, revealing helices E and F in the AFM topographs. Rho EF was modeled onto the BR structure, and the envelope derived from the AFM data of IIIN was used to select probable models. CONCLUSIONS: A likely conformation of Rho EF involves some extension of helices E and F, with the tip of the loop lying over helix C and projecting towards the C terminus. This is consistent with mutagenesis data showing the TTQ transducin-binding motif close to loop CD, and cysteine cross-linking data indicating the C-terminal part of Rho EF to be close to the CD loop.

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.

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.