Health Technology Assessment for In Silico Medicine: Social, Ethical and Legal Aspects

The application of in silico medicine is constantly growing in the prevention, diagnosis, and treatment of diseases. These technologies allow us to support medical decisions and self- management and reduce, refine, and partially replace real studies of medical technologies. In silico medicine may challenge some key principles: transparency and fairness of data usage; data privacy and protection across platforms and systems; data availability and quality; data integration and interoperability; intellectual property; data sharing; equal accessibility for persons and populations. Several social, ethical, and legal issues may consequently arise from its adoption. In this work, we provide an overview of these issues along with some practical suggestions for their assessment from a health technology assessment perspective. We performed a narrative review with a search on MEDLINE/Pubmed, ISI Web of Knowledge, Scopus, and Google Scholar. The following key aspects emerge as general reflections with an impact on the operational level: cultural resistance, level of expertise of users, degree of patient involvement, infrastructural requirements, risks for health, respect of several patients’ rights, potential discriminations for access and use of the technology, and intellectual property of innovations. Our analysis shows that several challenges still need to be debated to allow in silico medicine to express all its potential in healthcare processes

Reconstruction of Cochlea Based on Micro-CT and Histological Images of the Human Inner Ear

The study of the normal function and pathology of the inner ear has unique difficulties as it is inaccessible during life and, so, conventional techniques of pathologic studies such as biopsy and surgical excision are not feasible, without further impairing function. Mathematical modelling is therefore particularly attractive as a tool in researching the cochlea and its pathology. The first step towards efficient mathematical modelling is the reconstruction of an accurate three dimensional (3D) model of the cochlea that will be presented in this paper. The high quality of the histological images is being exploited in order to extract several sections of the cochlea that are not visible on the micro-CT (mCT) images (i.e., scala media, spiral ligament, and organ of Corti) as well as other important sections (i.e., basilar membrane, Reissner membrane, scala vestibule, and scala tympani). The reconstructed model is being projected in the centerline of the coiled cochlea, extracted from mCT images, and represented in the 3D space. The reconstruction activities are part of the SIFEM project, which will result in the delivery of an infrastructure, semantically interlinking various tools and libraries (i.e., segmentation, reconstruction, and visualization tools) with the clinical knowledge, which is represented by existing data, towards the delivery of a robust multiscale model of the inner ear

Polymerase chain reaction (PCR) and sequence specific oligonucleotide probes (SSOP) genotyping assay for detection of genes associated with rheumatoid arthritis and multiple sclerosis

Abstract-In this paper an assay for the detection of genes associated with rheumatoid arthritis (RA) and multiple sclerosis, using polymerase chain reaction (PCR) and sequence specific oligonucleotide probes (SSOP) is presented, in order to be further applied in a portable Lab-On-Chip (LOC) device. A substantial part of these reagents were based on the literature (11 th International Histocompatibility Workshop, IHW), bearing the advantage of proven successful implementation in genotyping, while others were designed for this study. More precisely, our methodology discriminates HLA-DRB1 as DRB1*01, *04 and *10, which include shared epitope (SE) alleles associated with RA and additionally DRB1*15 allele, including DRB1*1501 associated with MS (broad genotyping method). To further present the basic elements of the assay for high resolution genotyping of SE DRB1 alleles, we provide as an example the case of HLA-DRB1*10 alleles (HLA-DRB1*100101, *100102, *100103, *1002 and *1003). Regarding the methodology for developing a detection assay, for SNPs associated with RA or MS the basic steps are presented. DNA sequence data are obtained from IMGT/HLA and SNP database. Online software tools are used to define hybridization specificity of primers and probes towards human DNA, leading to hybridization patterns that uniquely designate a target allele and evaluate parameters influencing PCR efficiency. Respecting current technological limitations of autonomous molecular-based LOC systems the approach of broad genotyping of HLA-DRB1*01/*04/*10/*15 genes, is intended to be initially used, leaving, high resolution genotyping of SE alleles for future implementations. This method is easy to be updated and extended to detect additional associated loci with RA or MS

Effects of mycophenolate mofetil vs cyclosporine administration on graft survival and function after islet allotransplantation in diabetic rats

AIM: To develop an experimental model of islet allotransplantation in diabetic rats and to determine the positive or adverse effects of MMF as a single agent. METHODS: Thirty-six male Wistar rats and 18 male Lewis rats were used as recipients and donors respectively. Diabetes was induced by the use of streptozotocin (60 mg/kg) intraperitoneally. Unpurified islets were isolated using the collagenase digestion technique and transplanted into the splenic parenchyma. The recipients were randomly assigned to one of the following three groups: group A (control group) had no immunosuppression; group B received cyclosporine (CsA) (5 mg/kg); group C received mycophenolate mofetil (MMF) (20 mg/kg). The animals were killed on the 12(th) d. Blood and grafted tissues were obtained for laboratory and histological assessment. RESULTS: Median allograft survival was significantly higher in the two therapy groups than that in the controls (10 and 12 d for CsA and MMF respectively vs 0 d for the control group, P<0.01). No difference in allograft survival between the CsA and MMF groups was found. However, MMF had less renal and hepatic toxicity and allowed weight gain. CONCLUSION: Monotherapy with MMF for immunosuppression was safe in an experimental model of islet allotransplantation and was equally effective with cyclosporine, with less toxicity. (C) 2005 The WJG Press and Elsevier Inc. All rights reserved

Health Technology Assessment for In Silico Medicine: Social, Ethical and Legal Aspects

The application of in silico medicine is constantly growing in the prevention, diagnosis, and treatment of diseases. These technologies allow us to support medical decisions and self-management and reduce, refine, and partially replace real studies of medical technologies. In silico medicine may challenge some key principles: transparency and fairness of data usage; data privacy and protection across platforms and systems; data availability and quality; data integration and interoperability; intellectual property; data sharing; equal accessibility for persons and populations. Several social, ethical, and legal issues may consequently arise from its adoption. In this work, we provide an overview of these issues along with some practical suggestions for their assessment from a health technology assessment perspective. We performed a narrative review with a search on MEDLINE/Pubmed, ISI Web of Knowledge, Scopus, and Google Scholar. The following key aspects emerge as general reflections with an impact on the operational level: cultural resistance, level of expertise of users, degree of patient involvement, infrastructural requirements, risks for health, respect of several patients’ rights, potential discriminations for access and use of the technology, and intellectual property of innovations. Our analysis shows that several challenges still need to be debated to allow in silico medicine to express all its potential in healthcare processes

Predicting adherence of patients with HF through machine learning techniques

International audienceHeart failure (HF) is a chronic disease characterised by poor quality of life, recurrent hospitalisation and high mortality. Adherence of patient to treatment suggested by the experts has been proven a significant deterrent of the above-mentioned serious consequences. However, the non-adherence rates are significantly high; a fact that highlights the importance of predicting the adherence of the patient and enabling experts to adjust accordingly patient monitoring and management. The aim of this work is to predict the adherence of patients with HF, through the application of machine learning techniques. Specifically, it aims to classify a patient not only as medication adherent or not, but also as adherent or not in terms of medication, nutrition and physical activity (global adherent). Two classification problems are addressed: (i) if the patient is global adherent or not and (ii) if the patient is medication adherent or not. About 11 classification algorithms are employed and combined with feature selection and resampling techniques. The classifiers are evaluated on a dataset of 90 patients. The patients are characterised as medication and global adherent, based on clinician estimation. The highest detection accuracy is 82 and 91% for the first and the second classification problem, respectively

Machine Learning Approaches on High Throughput NGS Data to Unveil Mechanisms of Function in Biology and Disease

In this review, the fundamental basis of machine learning (ML) and data mining (DM) are summarized together with the techniques for distilling knowledge from state-of-the-art omics experiments. This includes an introduction to the basic mathematical principles of unsupervised/supervised learning methods, dimensionality reduction techniques, deep neural networks architectures and the applications of these in bioinformatics. Several case studies under evaluation mainly involve next generation sequencing (NGS) experiments, like deciphering gene expression from total and single cell (scRNA-seq) analysis; for the latter, a description of all recent artificial intelligence (AI) methods for the investigation of cell sub-types, biomarkers and imputation techniques are described. Other areas of interest where various ML schemes have been investigated are for providing information regarding transcription factors (TF) binding sites, chromatin organization patterns and RNA binding proteins (RBPs), while analyses on RNA sequence and structure as well as 3D dimensional protein structure predictions with the use of ML are described. Furthermore, we summarize the recent methods of using ML in clinical oncology, when taking into consideration the current omics data with pharmacogenomics to determine personalized treatments. With this review we wish to provide the scientific community with a thorough investigation of main novel ML applications which take into consideration the latest achievements in genomics, thus, unraveling the fundamental mechanisms of biology towards the understanding and cure of diseases

A training tool to support the management and diagnosis of Sjögren's syndrome

Objective. The objective of this work is to present a Training Tool designed to support healthcare professionals involved in the diagnosis and management of Sjögren's syndrome. Methods. The Training Tool aims to fulfil the gap of targeted education by providing a structured protocol of training including state of the art guidelines and practices. For the development of the Training Tool, latest relevant technologies have been used to assure efficiency and usability. Core functionalities include training by a series of multimedia courses, testing during the learning process, and profiling for monitoring the progress. An iterative requirement analysis process was established involving a large number of clinical experts, with the objective to identify user's training needs. Results. Comprehensive usability evaluation was performed by applying, an Unmoderated Remote Usability Test resulting to 97.2% Success Rate; and the well-established System Usability Scale, reaching a score of 90.4 which classifies the Training Tool as "A"gradedexcellent. Conclusion. The Training Tool offers open-online training of healthcare professionals involved in the diagnosis and management of Sjögren's syndrome, using a well-designed training protocol in highly usable manner. To our knowledge, this is the first such tool for Sjögren's syndrome.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Estimation of Heart Failure Patients Medication Adherence through the Utilization of Saliva and Breath Biomarkers and Data Mining Techniques

International audienceThe aim of this work is to estimate the medication adherence of patients with heart failure through the application of a data mining approach on a dataset including information from saliva and breath biomarkers. The method consists of two stages. In the first stage, a model for the estimation of adherence risk of a patient, exploiting anamnestic and instrumental data, is applied. In the second stage, the output of the model, accompanied with data from saliva and breath biomarkers, is given as input to a classification model for determining if the patient is adherent, in terms of medication. The method is evaluated on a dataset of 29 patients and the achieved accuracy is 96%