Project description:Research biobanks are non-profit structures that collect, manipulate, store, analyze and distribute systematically organized biological samples and data for research and development purposes. Over the recent years, we have established a biobank, the Rheumatology BioBank (RheumaBank) headed by the Medicine and Rheumatology unit of the IRCCS Istituto Ortopedico Rizzoli (IOR) in Bologna, Italy for the purpose of collecting, processing, storing, and distributing biological samples and associated data obtained from patients suffering from inflammatory joint diseases. RheumaBank is a research biobank, and its main objective is to promote large-scale, high-quality basic, translational, and clinical research studies that can help elucidate pathogenetic mechanisms and improve personalization of treatment choice in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) and other spondyloarthritides (SpA).
Project description:Biobanking in health care has evolved over the last few decades from simple biological sample repositories to complex and dynamic units with multi-organizational infrastructure networks and has become an essential tool for modern medical research. Cardiovascular tissue biobanking provides a unique opportunity to utilize cardiac and vascular samples for translational research into heart failure and other related pathologies. Current techniques for diagnosis, classification, and treatment monitoring of cardiac disease relies primarily on interpretation of clinical signs, imaging, and blood biomarkers. Further research at the disease source (i.e. myocardium and blood vessels) has been limited by a relative lack of access to quality human cardiac tissue and the inherent shortcomings of most animal models of heart disease. In this review, we describe a model for cardiovascular tissue biobanking and databasing, and its potential to facilitate basic and translational research. We share techniques to procure endocardial samples from patients with hypertrophic cardiomyopathy, heart failure with reduced ejection fraction, and heart failure with preserved ejection fraction, in addition to aortic disease samples. We discuss some of the issues with respect to data collection, privacy, biobank consent, and the governance of tissue biobanking. The development of tissue biobanks as described here has significant scope to improve and facilitate translational research in multi-omic fields such as genomics, transcriptomics, proteomics, and metabolomics. This research heralds an era of precision medicine, in which patients with cardiovascular pathology can be provided with optimized and personalized medical care for the treatment of their individual phenotype.
Project description:* To evaluate genome-wide drug variants in the Somali population * To be able to detect pharmacogenetic variants of importance in the Somali populaion * To infer HLA alleles and blood group variants in Somalia
Project description:A timely understanding of the biological secrets of complex diseases will ultimately benefit millions of individuals by reducing the high risks for mortality and improving the quality of life with personalized diagnoses and treatments. Due to the advancements in sequencing technologies and reduced cost, genomics data are developing at an unmatched pace and levels to foster translational research and precision medicine. Over 10 million genomics datasets have been produced and publicly shared in 2022. Diverse and high-volume genomics and clinical data have the potential to broaden the scope of biological discoveries and insights by extracting, analyzing and interpreting the hidden information. However, the current and still unresolved challenges include the integration of genomic profiles of the patients with their medical records. The definition of disease in genomics medicine is simplified, whereas in the clinical world, diseases are classified, identified and adopted with their International Classification of Diseases (ICD) codes, which are maintained by the World Health Organization. Several biological databases have been produced, which include information about human genes and related diseases. However, still, there is no database that exists, which can precisely link clinical codes with relevant genes and variants to support genomic and clinical data integration for clinical and translational medicine. In this project, we focused on the development of an annotated gene-disease-code database, which is accessible through an online, cross-platform and user-friendly application, i.e. PROMIS-APP-SUITE-Gene-Disease-Code. However, our scope is limited to the integration of ICD-9 and ICD-10 codes with the list of genes approved by the American College of Medical Genetics and Genomics. The results include over 17 000 diseases and 4000 ICD codes, and over 11 000 gene-disease-code combinations. Database URL https://promis.rutgers.edu/pas/.
Project description:The recombinant fusion protein HELP-UnaG (HUG) is a bifunctional product that exhibits human elastin-like polypeptide (HELP)-specific thermal behavior, defined as a reverse phase transition, and UnaG-specific bilirubin-dependent fluorescence emission. HUG provides an interesting model to understand how its two domains influence each other's properties. Turbidimetric, calorimetric, and light scattering measurements were used to determine different parameters for the reverse temperature transition and coacervation behavior. This shows that the UnaG domain has a measurable but limited effect on the thermal properties of HELP. Although the HELP domain decreased the affinity of UnaG for bilirubin, HUG retained the property of displacing bilirubin from bovine serum albumin and thus remains one of the strongest bilirubin-binding proteins known to date. These data demonstrate that HELP can be used to create new bifunctional fusion products that pave the way for expanded technological applications.
Project description:AimsPreclinical results suggest therapeutic potential of mild hyperbilirubinemia in T2DM and cardiovascular disease. Translational data are limited, because an appropriate bilirubin formulation for parenteral human use is lacking. Considering its use in both clinical practice and medical research in the past, we explored the feasibility to reintroduce parenteral bilirubin for translational experiments.MethodsWe developed a preparation method in accordance with good manufacturing practice and evaluated the parenteral applicability in healthy volunteers (n = 8). Explorative pharmacokinetic and safety data were compared to the results from a literature study on the former parenteral use of bilirubin. Bilirubin was administered intra-arterially to raise the local plasma concentration in the forearm vascular bed (n = 4) and intravenously to raise the systemic plasma concentration (n = 4). Finally, pharmacokinetic characteristics were studied following a single bolus infusion (n = 3).ResultsDuring parenteral application, no side effects occurred. Adverse events mentioned during the two-week observation period were in general mild and self-limiting. Three more significant adverse events (appendicitis, asymptomatic cardiac arrhythmia and atopic eczema) were judged unrelated by independent physicians. A dose-concentration relationship appeared sufficiently predictable for both intra-arterial and intravenous administration. In line with existing knowledge, bilirubin pharmacokinetics could be described best according to a two-compartment model with a volume of distribution of 9.9 (±2.0) l and a total plasma clearance of 36 (±16) ml per minute.ConclusionsSupported by previous reports, our data suggest that it is both feasible and safe to perform translational experiments with parenteral albumin bound bilirubin.
Project description:BackgroundPsoriasis is a recurrent, chronic, inflammation- and immune-mediated skin disease with multiple causative factors. However, the genetic markers associated with recurrence have not yet been fully elucidated. Accordingly, in this study, we aimed to identify markers associated with the recurrence of psoriasis.MethodsWe analyzed differentially expressed genes to determine which targets were associated with the recurrence of psoriasis and used these data to construct a protein-protein interaction network using Cytoscape software. The results were then validated by analysis of core targets using Gene Expression Omnibus (GEO) datasets and clinical samples. Functional enrichment analysis was used to explore the potential mechanisms mediating the recurrence of psoriasis.ResultsWe screened out six core targets that played important roles in recurrence of psoriasis, and validation of GEO datasets and clinical samples showed that the expression levels of five core targets were higher in patients with psoriasis than in healthy individuals. Functional enrichment analysis revealed that the cell cycle and oocyte meiosis signaling pathways were involved in the recurrence of psoriasis.ConclusionOur findings provided insights into the mechanisms mediating the onset and recurrence of psoriasis.
Project description:The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.