Privacy-Preserving Partner Notification Service to Control Sexually Transmitted Infections.

Partner Notification (PN) processes are typically part of wider combination prevention efforts and focus on the notification of sexual partners to prevent Sexually Transmitted Infections (STIs), including Human Immunodeficiency Viruses and viral hepatitis. We present a free, voluntary, anonymous and GDPR-compliant Partner Notification service that offers enhanced security and privacy through a web and mobile application via a unique random codes.

A GDPR-Compliant Partner Notification Service.

Partner Notification processes focus on the notification of sexual partners to prevent the transmission of Sexually Transmitted Infections (STIs). The INTEGRATE Joint Action provides an integrated platform called RiskRadar, for combination prevention activities targeting STIs, including an anonymous, free and voluntary Partner Notification service. The presented service information flow ensures privacy, security and GDPR compliance which were identified as vital with similar tools. The service is available via web and mobile interfaces using a unique random code provided from authorised healthcare professionals to support privacy.

RiskRadar: development and pilot results of a technical intervention targeting combination prevention regarding HIV, viral hepatitis, sexually transmitted infections and tuberculosis.

BACKGROUND: The HIV pandemic impacts the lives of millions and despite the global coordinated response, innovative actions are still needed to end it. A major challenge is the added burden of coinfections such as viral hepatitis, tuberculosis and various sexually transmitted infections in terms of prevention, treatment and increased morbidity in individuals with HIV infection. A need for combination prevention strategies, tailored to high-risk key populations arises and technology-based interventions can be a valuable asset. The COVID-19 pandemic challenged the delivery of existing services and added stress to existing public health and clinical structures but also highlighted the potential of exploiting technical solutions for interventions regarding infectious diseases. In this paper we report the design process, results and evaluation findings from the pilots of 'RiskRadar'-a web and mobile application aiming to support combination prevention, testing and linkage to care for HIV, viral hepatitis, various sexually transmitted infections and tuberculosis. METHODS: RiskRadar was developed for the INTEGRATE Joint Action's aim to improve, adapt and pilot innovative digital tools for combination prevention. RiskRadar was designed iteratively using informed end-user-oriented approaches. Emphasis was placed on the Risk Calculator that enables users to assess their risk of exposure to one or more of the four disease areas, make informed decisions to seek testing or care and adjust their behaviours ultimately aiming to harm/risk reduction. RiskRadar has been piloted in three countries, namely Croatia, Italy and Lithuania. RESULTS: RiskRadar has been used 1347 times across all platforms so far. More than 90% of users have found RiskRadar useful and would use it again, especially the Risk Calculator component. Almost 49.25% are men and 29.85% are in the age group of 25-34. The application has scored 5.2/7 in the User Experience Questionnaire, where it is mainly described as "supportive" and "easy-to-use". The qualitative evaluation of RiskRadar also yielded positive feedback. CONCLUSIONS: Pilot results demonstrate above average satisfaction with RiskRadar and high user-reported usability scores, supporting the idea that technical interventions could significantly support combination prevention actions on Sexually Transmitted Infections.

Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature.

BACKGROUND: Digital health interventions based on tools for Computerized Decision Support (CDS) and Machine Learning (ML), which take advantage of new information, sensing and communication technologies, can play a key role in childhood obesity prevention and treatment. OBJECTIVES: We present a systematic literature review of CDS and ML applications for the prevention and treatment of childhood obesity. The main characteristics and outcomes of studies using CDS and ML are demonstrated, to advance our understanding towards the development of smart and effective interventions for childhood obesity care. METHODS: A search in the bibliographic databases of PubMed and Scopus was performed to identify childhood obesity studies incorporating either CDS interventions, or advanced data analytics through ML algorithms. Ongoing, case, and qualitative studies, along with those not providing specific quantitative outcomes were excluded. The studies incorporating CDS were synthesized according to the intervention's main technology (e.g., mobile app), design type (e.g., randomized controlled trial), number of enrolled participants, target age of children, participants' follow-up duration, primary outcome (e.g., Body Mass Index (BMI)), and main CDS feature(s) and their outcomes (e.g., alerts for caregivers when BMI is high). The studies incorporating ML were synthesized according to the number of subjects included and their age, the ML algorithm(s) used (e.g., logistic regression), as well as their main outcome (e.g., prediction of obesity). RESULTS: The literature search identified 8 studies incorporating CDS interventions and 9 studies utilizing ML algorithms, which met our eligibility criteria. All studies reported statistically significant interventional or ML model outcomes (e.g., in terms of accuracy). More than half of the interventional studies (n = 5, 63 %) were designed as randomized controlled trials. Half of the interventional studies (n = 4, 50 %) utilized Electronic Health Records (EHRs) and alerts for BMI as means of CDS. From the 9 studies using ML, the highest percentage targeted at the prognosis of obesity (n = 4, 44 %). In the studies incorporating more than one ML algorithms and reporting accuracy, it was shown that decision trees and artificial neural networks can accurately predict childhood obesity. CONCLUSIONS: This review has found that CDS tools can be useful for the self-management or remote medical management of childhood obesity, whereas ML algorithms such as decision trees and artificial neural networks can be helpful for prediction purposes. Further rigorous studies in the area of CDS and ML for childhood obesity care are needed, considering the low number of studies identified in this review, their methodological limitations, and the scarcity of interventional studies incorporating ML algorithms in CDS tools.

Alzheimer's Disease: The Role of Mutations in Protein Folding.

Misfolded proteins result when a protein follows the wrong folding pathway. Accumulation of misfolded proteins can cause disorders, known as amyloid diseases. Unfortunately, some of them are very common. The most prevalent one is Alzheimer's disease. Alzheimer's disease is a neurodegenerative disorder and the commonest form of dementia. The current study aims to assess the impact of somatic mutations in PSEN1 gene. The said mutations are the most common cause of familial Alzheimer's disease. As protein functionality can be affected by mutations, the study of possible alterations in the tertiary structure of proteins may reveal new insights related to the relationship between mutations and protein functions. To examine the effect of mutations, the primary structures and their related mutations were retrieved from public databases. Each structure (mutated and unmutated) was predicted based on effective structure prediction methodologies. A benchmarking of the structural predictive tools was accomplished. Comparative analyses of mutated and unmutated proteins were performed based on classic bioinformatics methods (TM-Score, RMSD, etc.) as well as on established shape-based descriptors retrieved from object recognition methodologies. Unsupervised methodologies were applied to the structures, in order to identify groups of mutation with similar mutational impact. Our results provide an essential knowledge toward protein's functionality in structure-based drug design.

Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma.

BACKGROUND: Self-management programs have beneficial effects on asthma control, but their implementation in clinical practice is poor. Mobile health (mHealth) could play an important role in enhancing self-management. OBJECTIVE: To assess the clinical effectiveness and technology acceptance of myAirCoach-supported self-management on top of usual care in patients with asthma using inhalation medication. METHODS: Patients were recruited in 2 separate studies. The myAirCoach system consisted of an inhaler adapter, an indoor air-quality monitor, a physical activity tracker, a portable spirometer, a fraction exhaled nitric oxide device, and an app. The primary outcome was asthma control; secondary outcomes were exacerbations, quality of life, and technology acceptance. In study 1, 30 participants were randomized to either usual care or myAirCoach support for 3 to 6 months; in study 2, 12 participants were provided with the myAirCoach system in a 3-month before-after study. RESULTS: In study 1, asthma control improved in the intervention group compared with controls (Asthma Control Questionnaire difference, 0.70; P = .006). A total of 6 exacerbations occurred in the intervention group compared with 12 in the control group (hazard ratio, 0.31; P = .06). Asthma-related quality of life improved (mini Asthma-related Quality of Life Questionnaire difference, 0.53; P = .04), but forced expiratory volume in 1 second was unchanged. In study 2, asthma control improved by 0.86 compared with baseline (P = .007) and quality of life by 0.16 (P = .64). Participants reported positive attitudes toward the system. DISCUSSION: Using the myAirCoach support system improves asthma control and quality of life, with a reduction in severe asthma exacerbations. Well-validated mHealth technologies should therefore be further studied.

Disease-biased and shared characteristics of the immunoglobulin gene repertoires in marginal zone B cell lymphoproliferations.

The B cell receptor immunoglobulin (Ig) gene repertoires of marginal zone (MZ) lymphoproliferations were analyzed in order to obtain insight into their ontogenetic relationships. Our cohort included cases with MZ lymphomas (n = 488), i.e. splenic (SMZL), nodal (NMZL) and extranodal (ENMZL), as well as provisional entities (n = 76), according to the WHO classification. The most striking Ig gene repertoire skewing was observed in SMZL. However, restrictions were also identified in all other MZ lymphomas studied, particularly ENMZL, with significantly different Ig gene distributions depending on the primary site of involvement. Cross-entity comparisons of the MZ Ig sequence dataset with a large dataset of Ig sequences (MZ-related or not; n = 65 837) revealed four major clusters of cases sharing homologous ('public') heavy variable complementarity-determining region 3. These clusters included rearrangements from SMZL, ENMZL (gastric, salivary gland, ocular adnexa), chronic lymphocytic leukemia, but also rheumatoid factors and non-malignant splenic MZ cells. In conclusion, different MZ lymphomas display biased immunogenetic signatures indicating distinct antigen exposure histories. The existence of rare public stereotypes raises the intriguing possibility that common, pathogen-triggered, immune-mediated mechanisms may result in diverse B lymphoproliferations due to targeting versatile progenitor B cells and/or operating in particular microenvironments. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia.

BACKGROUND: Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. RESULTS: Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. CONCLUSIONS: The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.

Chronic Lymphocytic Leukemia Patient Clustering Based on Somatic Hypermutation (SHM) Analysis.

Somatic Hypermutation (SHM) load in the immunoglobulin heavy variable (IGHV) gene of the clonotypic B cell receptor immunoglobulin (BcR IG) is one of the most important prognostic markers in CLL, segregating patients into two distinct categories, with contrariwise disease course. Over the last years, immunogenetic studies have identified that ∼30% of CLL patients carry (quasi)identical BcR IG and thus can be assigned to different subsets with distinct clinicobiological profiles. This characterization was achieved by applying rules mainly concerning the diversity of the VH complementarity determining region 3 (CDR3). Following, studies have also identified subset-specific somatic hypermutation further highlighting antigen selection in disease ontogeny and evolution. In this study, an innovative attempt to explore possible associations amongst SHMs in different CLL patients is implemented and also the potential correlations with VH CDR3 stereotypy is examined, leading to a new classification algorithm implicating both SHM and CDR3 patterns. All results are classified to a ground level analysis, focusing on the most frequent SHMs, their paired associated amino acid changes and the formation of subgroups sharing the same VH CDR3 pattern, the latter being used as a similarity metric. In addition, all results are compared to established VH CDR3 patterns of the well-known CLL subsets in order to confirm the validity of our findings.

Notch signaling and ageing.

Notch signaling is a master controller of the neural stem cell and neural development maintaining a significant role in the normal brain function. Notch genes are involved in embryogenesis, nervous system, and cardiovascular and endocrine function. On the other side, there are studies representing the involvement of Notch mutations in sporadic Alzheimer disease, other neurodegenerative diseases such as Down syndrome, Pick's and Prion's disease, and CADASIL. This manuscript attempts to present a holistic view of the positive or negative contribution of Notch signaling in the adult brain, and at the same time to present and promote the promising research fields of study.

Molecular dynamics simulations through GPU video games technologies.

Bioinformatics is the scientific field that focuses on the application of computer technology to the management of biological information. Over the years, bioinformatics applications have been used to store, process and integrate biological and genetic information, using a wide range of methodologies. One of the most de novo techniques used to understand the physical movements of atoms and molecules is molecular dynamics (MD). MD is an in silico method to simulate the physical motions of atoms and molecules under certain conditions. This has become a state strategic technique and now plays a key role in many areas of exact sciences, such as chemistry, biology, physics and medicine. Due to their complexity, MD calculations could require enormous amounts of computer memory and time and therefore their execution has been a big problem. Despite the huge computational cost, molecular dynamics have been implemented using traditional computers with a central memory unit (CPU). A graphics processing unit (GPU) computing technology was first designed with the goal to improve video games, by rapidly creating and displaying images in a frame buffer such as screens. The hybrid GPU-CPU implementation, combined with parallel computing is a novel technology to perform a wide range of calculations. GPUs have been proposed and used to accelerate many scientific computations including MD simulations. Herein, we describe the new methodologies developed initially as video games and how they are now applied in MD simulations.