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Endothelial wall shear stress (ESS) is a biomechanical force which plays a role in the formation and evolution of atherosclerotic lesions. The purpose of this study is to evaluate coronary computed tomography angiography (CCTA)-based ESS in coronary arteries without atherosclerosis, and to assess factors affecting ESS values. CCTA images from patients with suspected coronary artery disease were analyzed to identify coronary arteries without atherosclerosis. Minimal and maximal ESS values were calculated for 3-mm segments. Factors potentially affecting ESS values were examined, including sex, lumen diameter and distance from the ostium. Segments were categorized according to lumen diameter tertiles into small (

For many decades, the clinical unmet needs of primary Sjögren's Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity along with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs. © 2022 The Author(s)

Introduction: European Reference Networks (ERNs) are dedicated to rare complex diseases. Systemic autoimmune rheumatic diseases (SARDs) comprise a group of disorders, some of which are rare, complex, and chronic, characterized by relapsing-remitting course and requiring targeted treatments for long periods; SARDs are also associated with various co-morbidities and therefore health-care infrastructures, at the highest level of expertise are required. Areas covered: For the current work, literature on the basic characteristics of a center of excellence dedicated to SARDs, its advantages over the existing health infrastructures in order to improve health and social care, its contribution to the education of health-care workers, and the related research opportunities are presented. In addition, our experience, vision, and initiatives as a new member of the ERNs are reported. Expert opinion: A restructure in healthcare policy and resource allocation, based on centers of expertise, is necessary to improve the medical care of patients with SARDs. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Unlike autoimmune diseases, there is no known constitutive and disease-defining biomarker for systemic autoinflammatory diseases (SAIDs). Kawasaki disease (KD) is one of the “undiagnosed” types of SAIDs whose pathogenic mechanism and gene mutation still remain unknown. To address this issue, we have developed a sequential computational workflow which clusters KD patients with similar gene expression profiles across the three different KD phases (Acute, Subacute and Convalescent) and utilizes the resulting clustermap to detect prominent genes that can be used as diagnostic biomarkers for KD. Self-Organizing Maps (SOMs) were employed to cluster patients with similar gene expressions across the three phases through inter-phase and intra-phase clustering. Then, false discovery rate (FDR)-based feature selection was applied to detect genes that significantly deviate across the per-phase clusters. Our results revealed five genes as candidate biomarkers for KD diagnosis, namely, the HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, and CASD1. To our knowledge, these five genes are reported for the first time in the literature. The impact of the discovered genes for KD diagnosis against the known ones was demonstrated by training boosting ensembles (AdaBoost and XGBoost) for KD classification on common platform and cross-platform datasets. The classifiers which were trained on the proposed genes from the common platform data yielded an average increase by 4.40% in accuracy, 5.52% in sensitivity, and 3.57% in specificity than the known genes in the Acute and Subacute phases, followed by a notable increase by 2.30% in accuracy, 2.20% in sensitivity, and 4.70% in specificity in the cross-platform analysis. © 2021 The Authors

Introduction: Asymptomatic carotid artery stenosis (ACAS) may cause future stroke and therefore patients with ACAS require best medical treatment. Patients at high risk for stroke may opt for additional revascularization (either surgery or stenting) but the future stroke risk should outweigh the risk for peri/post-operative stroke/death. Current risk stratification for patients with ACAS is largely based on outdated rand-omized-controlled trials that lack the integration of improved medical therapies and risk factor control. Furthermore, recent circulating and imaging biomarkers for stroke have never been included in a risk stratification model. The TAXINOMISIS Project aims to develop a new risk stratification model for cerebrovascular complications in patients with ACAS and this will be tested through a prospective observational multicentre clinical trial performed in six major European vascular surgery centres. Methods and analysis: The risk stratification model will compromise clinical, circulating, plaque and imaging biomarkers. The prospective multicentre observational study will include 300 patients with 50%-99% ACAS. The primary endpoint is the three-year incidence of cerebrovascular complications. Biomarkers will be re-trieved from plasma samples, brain MRI, carotid MRA and duplex ultrasound. The TAXINOMISIS Project will serve as a platform for the development of new computer tools that assess plaque progression based on radiology images and a lab-on-chip with genetic variants that could predict medication response in individual patients. Conclusion: Results from the TAXINOMISIS study could potentially improve future risk stratification in patients with ACAS to assist personalized evidence-based treatment decision-making. © 2020 The Authors.

Background: To compare the clinical, serological and histologic features between male and female patients with Sjögren’s syndrome (SS) and explore the potential effect of gender on lymphoma development. Methods: From a multicenter population (Universities of Udine, Pisa and Athens, Harokopion and Ioannina (UPAHI)) consisting of consecutive SS patients fulfilling the 2016 ACR/EULAR criteria, male patients were identified, matched and compared with female controls. Data-driven multivariable logistic regression analysis was applied to identify independent lymphoma-associated factors. Results: From 1987 consecutive SS patients, 96 males and 192 matched female controls were identified and compared. Males had a higher frequency of lymphoma compared to females (18% vs. 5.2%, OR = 3.89, 95% CI: 1.66 to 8.67; p = 0.0014) and an increased prevalence of serum anti-La/SSB antibodies (50% vs. 34%, OR = 1.953, 95% CI: 1.19 to 3.25; p = 0.0128). No differences were observed in the frequencies of lymphoma predictors between the two genders. Data-driven multivariable logistic regression analysis revealed negative association of the female gender with lymphoma and positive association with lymphadenopathy. Conclusion: Male SS patients carry an increased risk of lymphoma development. Although statistics showed no difference in classical lymphoma predictors compared to females, data-driven analysis revealed gender and lymphadenopathy as independent lymphoma-associated features. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Background: mhealth, predominantly wearable technology and mobile apps, have been considered in Parkinson’s Disease to provide valuable ecological data between face to face visits and improve monitoring of motor symptoms remotely. Objective: In this study we explore the feasibility of using a technology based mhealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in the present study as the PD_manager system, to collect clinically meaningful data. We also explore outcomes and disease related factors which are important determinants to establish feasibility. Finally, we further validate a tremor evaluation method with data collected while patients perform their daily activities. Methods: PD_manager trial was an open label parallel group randomized study. The mheath platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps and a knowledge platform) serving as the cloud backend. The compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective UPDRS estimations by clinicians were explored. Results: There were 65 of the 75 study participants (87%) who completed the protocol. They used the PD_manager system for a median 11.57 days (Std. dev. 3.15). The regression analysis suggests that the main factor associated with high usage was caregivers’ burden. Motor Aspects of Experiences of Daily Living and patients’ self-rated health status also influence the system’s usage. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor. Conclusions: We found that PD patients, regardless of their demographics and disease characteristics, used the system for 11-14 days. The study further supports that mhealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mhealth platform can improve disease management and care.

Lymphoma development constitutes one of the most serious clinico-pathological manifestations of patients with Sjögren's Syndrome (SS). Over the last decades the risk for lymphomagenesis in SS patients has been studied aiming to identify novel biomarkers and risk factors predicting lymphoma development in this patient population. Objective: The current study aims to explore whether genetic susceptibility profiles of SS patients along with known clinical, serological and histological risk factors enhance the accuracy of predicting lymphoma development in this patient population. Methods: The potential predicting role of both genetic variants, clinical and laboratory risk factors were investigated through a Machine Learning-based (ML) framework which encapsulates ensemble classifiers. Results: Ensemble methods empower the classification accuracy with approaches which are sensitive to minor perturbations in the training phase. The evaluation of the proposed methodology based on a 10-fold stratified cross validation procedure yielded considerable results in terms of balanced accuracy (GB: 0.7780 ± 0.1514, RF Gini: 0.7626 ± 0.1787, RF Entropy: 0.7590 ± 0.1837). Conclusions: The initial clinical, serological, histological and genetic findings at an early diagnosis have been exploited in an attempt to establish predictive tools in clinical practice and further enhance our understanding towards lymphoma development in SS. © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

Goal: To present a framework for data sharing, curation, harmonization and federated data analytics to solve open issues in healthcare, such as, the development of robust disease prediction models. Methods: Data curation is applied to remove data inconsistencies. Lexical and semantic matching methods are used to align the structure of the heterogeneous, curated cohort data along with incremental learning algorithms including class imbalance handling and hyperparameter optimization to enable the development of disease prediction models. Results: The applicability of the framework is demonstrated in a case study of primary Sjögren's Syndrome, yielding harmonized data with increased quality and more than 85% agreement, along with lymphoma prediction models with more than 80% sensitivity and specificity. Conclusions: The framework provides data quality, harmonization and analytics workflows that can enhance the statistical power of heterogeneous clinical data and enables the development of robust models for disease prediction. © 2020 Lippincott Williams and Wilkins. All rights reserved.

Objectives: To study the clinical, serological and histologic features of primary Sjögren’s syndrome (pSS) patients with early (young ≤35 years) or late (old ≥65 years) onset and to explore the differential effect on lymphoma development. Methods: From a multicentre study population of 1997 consecutive pSS patients, those with early or late disease onset, were matched and compared with pSS control patients of middle age onset. Data driven analysis was applied to identify the independent variables associated with lymphoma in both age groups. Results: Young pSS patients (19%, n = 379) had higher frequency of salivary gland enlargement (SGE, lymphadenopathy, Raynaud’s phenomenon, autoantibodies, C4 hypocomplementemia, hypergammaglobulinemia, leukopenia, and lymphoma (10.3% vs. 5.7%, p = 0.030, OR = 1.91, 95% CI: 1.11–3.27), while old pSS patients (15%, n = 293) had more frequently dry mouth, interstitial lung disease, and lymphoma (6.8% vs. 2.1%, p = 0.011, OR = 3.40, 95% CI: 1.34–8.17) compared to their middle-aged pSS controls, respectively. In young pSS patients, cryoglobulinemia, C4 hypocomplementemia, lymphadenopathy, and SGE were identified as independent lymphoma associated factors, as opposed to old pSS patients in whom SGE, C4 hypocomplementemia and male gender were the independent lymphoma associated factors. Early onset pSS patients displayed two incidence peaks of lymphoma within 3 years of onset and after 10 years, while in late onset pSS patients, lymphoma occurred within the first 6 years. Conclusion: Patients with early and late disease onset constitute a significant proportion of pSS population with distinct clinical phenotypes. They possess a higher prevalence of lymphoma, with different predisposing factors and lymphoma distribution across time. © Copyright © 2020 Goules, Argyropoulou, Pezoulas, Chatzis, Critselis, Gandolfo, Ferro, Binutti, Donati, Zandonella Callegher, Venetsanopoulou, Zampeli, Mavrommati, Voulgari, Exarchos, Mavragani, Baldini, Skopouli, Fotiadis, De Vita, Moutsopoulos and Tzioufas.

To test the hypothesis that virtual functional assessment index (vFAI) is related with regional flow parameters derived by quantitative positron emission tomography (PET) and can be used to assess abnormal vasodilating capability in coronary vessels with stenotic lesions at coronary computed tomography angiography (CCTA). Methods and results: vFAI, stress myocardial blood flow (MBF), and myocardial flow reserve (MFR) were assessed in 78 patients (mean age 62.2 ± 7.7 years) with intermediate pre-test likelihood of coronary artery disease (CAD). Coronary stenoses ≥50% were considered angiographically significant. PET was considered positive for significant CAD, when more than one contiguous segments showed stress MBF ≤2.3 mL/g/min for O-water or

Data quality assessment has gained attention in the recent years since more and more companies and medical centers are highlighting the importance of an automated framework to effectively manage the quality of their big data. Data cleaning, also known as data curation, lies in the heart of the data quality assessment and is a key aspect prior to the development of any data analytics services. In this work, we present the objectives, functionalities and methodological advances of an automated framework for data curation from a medical perspective. The steps towards the development of a system for data quality assessment are first described along with multidisciplinary data quality measures. A three-layer architecture which realizes these steps is then presented. Emphasis is given on the detection and tracking of inconsistencies, missing values, outliers, and similarities, as well as, on data standardization to finally enable data harmonization. A case study is conducted in order to demonstrate the applicability and reliability of the proposed framework on two well-established cohorts with clinical data related to the primary Sjögren's Syndrome (pSS). Our results confirm the validity of the proposed framework towards the automated and fast identification of outliers, inconsistencies, and highly-correlated and duplicated terms, as well as, the successful matching of more than 85% of the pSS-related medical terms in both cohorts, yielding more accurate, relevant, and consistent clinical data. © 2019

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. © 2018, European Academy of Paediatric Dentistry.

In this review, the critical parts and milestones for data harmonization, from the biomedical engineering perspective, are outlined. The need for data sharing between heterogeneous sources paves the way for cohort harmonization; thus, fostering data integration and interdisciplinary research. Unmet needs in chronic diseases, as well as in other diseases, can be addressed based on the integration of patient health records and the sharing of information of the clinical picture and outcome. The stratification of patients, the determination of various clinical and outcome features, and the identification of novel biomarkers for the different phenotypes of the disease characterize the impact of cohort harmonization in patient-centered clinical research and in precision medicine. Subsequently, the establishment of matching techniques and ontologies for the creation of data schemas are also presented. The exploitation of web technologies and data-collection tools supports the opportunities to achieve new levels of integration and interoperability. Ethical and legal issues that arise when sharing and harmonizing individual-level data are discussed in order to evaluate the harmonization potential. Use cases that shape and test the harmonization approach are explicitly analyzed along with their significant results on their research objectives. Finally, future trends and directions are discussed and critically reviewed toward a roadmap in cohort harmonization for clinical medicine. © 2008-2011 IEEE.