Highly Sensitive Electrochemical BioMEMS for TNF-α Detection in Humansaliva: Heart Failure

Abstract Prediction of disease progression using saliva as a diagnostic medium has roused the interest of scientific researchers in the 10 last past years. Potentially important biomarkers are increased in saliva during local and systemic inflammation. In the present study we have developed a highly sensitive biosensor for TNF-α detection in human saliva of patients suffering from heart failure. Therefore, a fully integrated electrochemical BioMEMS was developed in order to increase the sensitivity of detection, decrease the time of analysis, and to simultaneously detect varying cytokine biomarkers using eight gold working microelectrodes (WE). The monoclonal antibodies (mAb) anti-human Tumor Necrosis Factor alpha (anti-TNF-α) were immobilized onto gold microelectrodes through functionalization with carboxyl diazonium. Cyclic voltammetry (CV) was applied during the microelectrode functionalization process to characterize the gold microelectrode surface properties. Finally, electrochemical impedance spectroscopy (EIS) characterized the modified gold microelectrodes, and the detection range of TNF-α cytokines was from 1pg/mL to 15 pg/mL

Predicting Heart Failure Patient Events by Exploiting Saliva and Breath Biomarkers Information

The aim of this work is to present a machine learning based method for the prediction of adverse events (mortality and relapses) in patients with heart failure (HF) by exploiting, for the first time, measurements of breath and saliva biomarkers (Tumor Necrosis Factor Alpha, Cortisol and Acetone). Data from 27 patients are used in the study and the prediction of adverse events is achieved with high accuracy (77%) using the Rotation Forest algorithm. As in the near future, biomarkers can be measured at home, together with other physiological data, the accurate prediction of adverse events on the basis of home based measurements can revolutionize HF management

KardiaTool: An Integrated POC Solution for Non-invasive Diagnosis and Therapy Monitoring of Heart Failure Patients

The aim of this work is to present KardiaTool platform, an integrated Point of Care (POC) solution for noninvasive diagnosis and therapy monitoring of Heart Failure (HF) patients. The KardiaTool platform consists of two components, KardiaPOC and KardiaSoft. KardiaPOC is an easy to use portable device with a disposable Lab-on-Chip (LOC) for the rapid, accurate, non-invasive and simultaneous quantitative assessment of four HF related biomarkers, from saliva samples. KardiaSoft is a decision support software based on predictive modeling techniques that analyzes the POC data and other patient's data, and delivers information related to HF diagnosis and therapy monitoring. It is expected that identifying a source comparable to blood, for biomarker information extraction, such as saliva, that is cost-effective, less invasive, more convenient and acceptable for both patients and healthcare professionals would be beneficial for the healthcare community. In this work the architecture and the functionalities of the KardiaTool platform are presented

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented. Keywords: Heart failure, Diagnosis, Prediction, Severity estimation, Classification, Data minin

Public Perspectives on Lifestyle-Related Behavior Change for Dementia Risk Reduction:An Exploratory Qualitative Study in The Netherlands

BACKGROUND: There is accumulating evidence that addressing modifiable risk and protective factors has an impact on dementia rates. Insight into the public's perspectives on dementia risk reduction is needed to inform future individual-level interventions and public health approaches. OBJECTIVE: This study explores the publics' openness towards dementia risk reduction and willingness towards changing lifestyle behavior to reduce the future risk for dementia. METHODS: Using a screening questionnaire, participants were purposively selected based on lifestyle behaviors that are associated with dementia risk. One-on-one interviews were used to explore their openness towards dementia risk reduction and willingness towards behavior change. Independently, two researchers performed an inductive content analysis. RESULTS: Interviews were conducted with 23 participants aged from 40 to 79 years. Main themes that were identified from the data were: 1) abstractness of dementia risk reduction, 2) ambivalence towards changing behavior, 3) negative self-image and low behavioral control, and 4) all-or-nothing thinking about lifestyle change. CONCLUSIONS: The concept of dementia risk reduction seems difficult to translate to the personal context, particularly if individuals perceive that dementia would occur decades in the future. This is problematic because a large proportion of the public needs a healthier lifestyle to reduce the incidence of dementia. Translating healthy intentions into behavior is complex and involves overcoming a variety of barriers that complicate dementia risk reduction initiatives. Support is needed for individuals who experience additional obstacles that obstruct commencing to a healthier lifestyle (e.g., negative self-image, engaging in multiple unhealthy behaviors, unrealistic perceptions about lifestyle change)

A proof-of-concept study for the simulation of blood flow in a post arterial segment for different blood rheology models

: Cardiovascular disease (CVD) and especially atherosclerosis are chronic inflammatory diseases which cause the atherosclerotic plaque growth in the arterial vessels and the blood flow reduction. Stents have revolutionized the treatment of this disease to a great extent by restoring the blood flow in the vessel. The present study investigates the performance of the blood flow after stent implantation in patient-specific coronary artery and demonstrates the effect of using Newtonian vs. non-Newtonian blood fluid models in the distribution of endothelial shear stress. In particular, the Navier-Stokes and continuity equations were employed, and three non-Newtonian fluid models were investigated (Carreau, Carreau-Yasuda and the Casson model). Computational finite elements models were used for the simulation of blood flow. The comparison of the results demonstrates that the Newtonian fluid model underestimates the calculation of Endothelial Shear Stress, while the three non-Newtonian fluids present similar distribution of shear stress. Keywords: Blood flow dynamics, stented artery, non-Newtonian fluid. Clinical Relevance- This work demonstrates that when blood flow modeling is performed at stented arteries and predictive models are developed, the non-Newtonian nature of blood must be considered

An in silico trials platform for the evaluation of stent design effect in post-implantation outcomes

: Bioresorbable Vascular Scaffolds (BVS), developed to allow drug deliver and mechanical support, followed by complete resorption, have revolutionized atherosclerosis treatment. InSilc is a Cloud platform for in silico clinical trials (ISCT) used in the design, development and evaluation pipeline of stents. The platform integrates beyond the state-of-the-art multi-disciplinary and multiscale models, which predict the scaffold's performance in the short/acute and medium/long term. In this study, a use case scenario of two Bioabsorbable Vascular Stents (BVSs) implanted in the same arterial anatomy is presented, allowing the whole InSilc in silico pipeline to be applied and predict how the different aspects of this intervention affect the success of stenting process

In Silico analysis of stent deployment- effect of stent design

Coronary artery disease (CAD) remains the leading cause of death in Europe and worldwide. One of the most common pathologic processes involved in CAD is atherosclerosis. Coronary stents are expandable scaffolds that are used to widen the occluded arteries and enable the blood flow restoration. To achieve an adequate delivery and placement of coronary stents different parameters play a significant role. Due to the strain that the stents are exposed to and the forces they should withstand, the stent design is dominant. This study focuses on investigating the effect of the stent design in two finite element models using two stents with difference in the strut thickness. The in silico deployment is performed in a reconstructed patient specific arterial segment. The results are analyzed in terms of stress in the stent and the arterial wall and demonstrate how stent expansion is extensively affected by the scaffold's design

A Novel Approach to Generate a Virtual Population of Human Coronary Arteries for <italic>In Silico</italic> Clinical Trials of Stent Design

Goal: To develop a cardiovascular virtual population using statistical modeling and computational biomechanics. Methods: A clinical data augmentation algorithm is implemented to efficiently generate virtual clinical data using a real clinical dataset. An atherosclerotic plaque growth model is employed to 3D reconstructed coronary arterial segments to generate virtual coronary arterial geometries (geometrical data). Last, the combination of the virtual clinical and geometrical data is achieved using a methodology that allows for the generation of a realistic virtual population which can be used in in silico clinical trials. Results: The results show good agreement between real and virtual clinical data presenting a mean gof 0.1 &#x00B1; 0.08. 400 virtual coronary arteries were generated, while the final virtual population includes 10,000 patients. Conclusions: The virtual arterial geometries are efficiently matched to the generated clinical data, both increasing and complementing the variability of the virtual population

Highly Sensitive Electrochemical BioMEMS for TNF-α Detection in Humansaliva: Heart Failure

Prediction of disease progression using saliva as a diagnostic medium has roused the interest of scientific researchers in the 10 last past years. Potentially important biomarkers are increased in saliva during local and systemic inflammation. In the present study we have developed a highly sensitive biosensor for TNF-alpha detection in human saliva of patients suffering from heart failure. Therefore, a fully integrated electrochemical BioMEMS was developed in order to increase the sensitivity of detection, decrease the time of analysis, and to simultaneously detect varying cytokine biomarkers using eight gold working microelectrodes (WE). The monoclonal antibodies (mAb) anti-human Tumor Necrosis Factor alpha (anti-TNF-alpha) were immobilized onto gold icroelectrodes through functionalization with carboxyl diazonium. Cyclic voltammetry (CV) was applied during the microelectrode functionalization process to characterize the gold microelectrode surface properties. Finally, electrochemical impedance spectroscopy (EIS) characterized the modified gold microelectrodes, and the detection range of TNF-alpha cytokines was from 1pg/mL to 15 pg/mL