Improving Data Collection in Pregnancy Safety Studies: Towards Standardisation of Data Elements in Pregnancy Reports from Public and Private Partners, A Contribution from the ConcePTION Project.

INTRODUCTION AND OBJECTIVE The ConcePTION project aims to improve the way medication use during pregnancy is studied. This includes exploring the possibility of developing a distributed data processing and analysis infrastructure using a common data model that could form a foundational platform for future surveillance and research. A prerequisite would be that data from various data access providers (DAPs) can be harmonised according to an agreed set of standard rules concerning the structure and content of the data. To do so, a reference framework of core data elements (CDEs) recommended for primary data studies on drug safety during pregnancy was previously developed. The aim of this study was to assess the ability of several public and private DAPs using different primary data sources focusing on multiple sclerosis, as a pilot, to map their respective data variables and definitions with the CDE recommendations framework. METHODS Four pregnancy registries (Gilenya, Novartis; Aubagio, Sanofi; the Organization of Teratology Information Specialists [OTIS]; Aubagio, Sanofi; the Dutch Pregnancy Drug Register, Lareb), two enhanced pharmacovigilance programmes (Gilenya PRIM, Novartis; MAPLE-MS, Merck Healthcare KGaA) and four Teratology Information Services (UK TIS, Jerusalem TIS, Zerifin TIS, Swiss TIS) participated in the study. The ConcePTION primary data source CDE includes 51 items covering administrative functions, the description of pregnancy, maternal medical history, maternal illnesses arising in pregnancy, delivery details, and pregnancy and infant outcomes. For each variable in the CDE, the DAPs identified whether their variables were: identical to the one mentioned in the CDE; derived; similar but with a divergent definition; or not available. RESULTS The majority of the DAP data variables were either directly taken (85%, n = 305/357, range 73-94% between DAPs) or derived by combining different variables (12%, n = 42/357, range 0-24% between DAPs) to conform to the CDE variables and definitions. For very few of the DAP variables, alignment with the CDE items was not possible, either because of divergent definitions (1%, n = 3/357, range 0-2% between DAPs) or because the variables were not available (2%, n = 7/357, range 0-4% between DAPs). CONCLUSIONS Data access providers participating in this study presented a very high proportion of variables matching the CDE items, indicating that alignment of definitions and harmonisation of data analysis by different stakeholders to accelerate and strengthen pregnancy pharmacovigilance safety data analyses could be feasible

Quantitative single cell monitoring of protein synthesis at subcellular resolution using fluorescently labeled tRNA

We have developed a novel technique of using fluorescent tRNA for translation monitoring (FtTM). FtTM enables the identification and monitoring of active protein synthesis sites within live cells at submicron resolution through quantitative microscopy of transfected bulk uncharged tRNA, fluorescently labeled in the D-loop (fl-tRNA). The localization of fl-tRNA to active translation sites was confirmed through its co-localization with cellular factors and its dynamic alterations upon inhibition of protein synthesis. Moreover, fluorescence resonance energy transfer (FRET) signals, generated when fl-tRNAs, separately labeled as a FRET pair occupy adjacent sites on the ribosome, quantitatively reflect levels of protein synthesis in defined cellular regions. In addition, FRET signals enable detection of intra-populational variability in protein synthesis activity. We demonstrate that FtTM allows quantitative comparison of protein synthesis between different cell types, monitoring effects of antibiotics and stress agents, and characterization of changes in spatial compartmentalization of protein synthesis upon viral infection

Characterization and genotype-phenotype correlation of patients with Fanconi anemia in a multi-ethnic population

Fanconi anemia (FA), an inherited bone marrow failure (BMF) syndrome, caused by mutations in DNA repair genes, is characterized by congenital anomalies, aplastic anemia, high risk of malignancies and extreme sensitivity to alkylating agents. We aimed to study the clinical presentation, molecular diagnosis and genotype-phenotype correlation among patients with FA from the Israeli inherited BMF registry. Overall, 111 patients of Arab (57%) and Jewish (43%) descent were followed for a median of 15 years (range: 0.1-49); 63% were offspring of consanguineous parents. One-hundred patients (90%) had at least one congenital anomaly; over 80% of the patients developed bone marrow failure; 53% underwent hematopoietic stem-cell transplantation; 33% of the patients developed cancer; no significant association was found between hematopoietic stem-cell transplant and solid tumor development. Nearly 95% of the patients tested had confirmed mutations in the Fanconi genes FANCA (67%), FANCC (13%), FANCG (14%), FANCJ (3%) and FANCD1 (2%), including twenty novel mutations. Patients with FANCA mutations developed cancer at a significantly older age compared to patients with mutations in other Fanconi genes (mean 18.5 and 5.2 years, respectively, P=0.001); however, the overall survival did not depend on the causative gene. We hereby describe a large national cohort of patients with FA, the vast majority genetically diagnosed. Our results suggest an older age for cancer development in patients with FANCA mutations and no increased incidence of solid tumors following hematopoietic stem-cell transplant. Further studies are needed to guide individual treatment and follow-up programs

Embryonic Pig Pancreatic Tissue Transplantation for the Treatment of Diabetes

BACKGROUND: Transplantation of embryonic pig pancreatic tissue as a source of insulin has been suggested for the cure of diabetes. However, previous limited clinical trials failed in their attempts to treat diabetic patients by transplantation of advanced gestational age porcine embryonic pancreas. In the present study we examined growth potential, functionality, and immunogenicity of pig embryonic pancreatic tissue harvested at different gestational ages. METHODS AND FINDINGS: Implantation of embryonic pig pancreatic tissues of different gestational ages in SCID mice reveals that embryonic day 42 (E42) pig pancreas can enable a massive growth of pig islets for prolonged periods and restore normoglycemia in diabetic mice. Furthermore, both direct and indirect T cell rejection responses to the xenogeneic tissue demonstrated that E42 tissue, in comparison to E56 or later embryonic tissues, exhibits markedly reduced immunogenicity. Finally, fully immunocompetent diabetic mice grafted with the E42 pig pancreatic tissue and treated with an immunosuppression protocol comprising CTLA4-Ig and anti–CD40 ligand (anti-CD40L) attained normal blood glucose levels, eliminating the need for insulin. CONCLUSIONS: These results emphasize the importance of selecting embryonic tissue of the correct gestational age for optimal growth and function and for reduced immunogenicity, and provide a proof of principle for the therapeutic potential of E42 embryonic pig pancreatic tissue transplantation in diabetes

DC mobilization from the skin requires docking to immobilized CCL21 on lymphatic endothelium and intralymphatic crawling

Dermal DC mobilization requires docking to CCL21 on lymphatic endotheliu

From Population Databases to Research and Informed Health Decisions and Policy

BackgroundIn the era of big data, the medical community is inspired to maximize the utilization and processing of the rapidly expanding medical datasets for clinical-related and policy-driven research. This requires a medical database that can be aggregated, interpreted, and integrated at both the individual and population levels. Policymakers seek data as a lever for wise, evidence-based decision-making and information-driven policy. Yet, bridging the gap between data collection, research, and policymaking, is a major challenge.The modelTo bridge this gap, we propose a four-step model: (A) creating a conjoined task force of all relevant parties to declare a national program to promote collaborations; (B) promoting a national digital records project, or at least a network of synchronized and integrated databases, in an accessible transparent manner; (C) creating an interoperative national research environment to enable the analysis of the organized and integrated data and to generate evidence; and (D) utilizing the evidence to improve decision-making, to support a wisely chosen national policy. For the latter purpose, we also developed a novel multidimensional set of criteria to illuminate insights and estimate the risk for future morbidity based on current medical conditions.ConclusionUsed by policymakers, providers of health plans, caregivers, and health organizations, we presume this model will assist transforming evidence generation to support the design of health policy and programs, as well as improved decision-making about health and health care, at all levels: individual, communal, organizational, and national