Tools   Select Another Resource Report Type

http://www.jaeb.org/

Freestanding, nonprofit coordinating center for multi-center clinical trials and epidemiologic research that focus on projects involving eye disorders or type 1 diabetes.

Proper citation: Jaeb Center for Health Research (RRID:SCR_001513) Copy   

•   Source: SciCrunch Registry

https://repository.niddk.nih.gov/study/45

Study group and network for a 2008 longitudinal study for the etiology, diagnosis, treatment, and outcome of acute liver failure in infants, children, and adolescents. Data from patients include urine, bile, serum, liver tissue, cell lines derived from fibroblast culture, and DNA.

Proper citation: Pediatric Acute Liver Failure Study (RRID:SCR_001478) Copy   

•   Source: SciCrunch Registry

https://repository.niddk.nih.gov/study/81

Multi-center, randomized controlled study designed to determine if continuing interferon long term over several years will suppress the Hepatitis C virus, prevent progression to cirrhosis, prevent liver cancer and reduce the need for liver transplantation. Patient enrollment began in 2000 and was completed in 2003 at 10 clinical centers, which were supported by a data coordinating center, virological testing center, and central sample repository. Patients with chronic hepatitis C and advanced fibrosis or cirrhosis on liver biopsy who failed to respond to a previous course of interferon alfa were enrolled in this study. Patients were initially treated with a 24-week course of peginterferon alfa-2a and ribavirin. Patients who remained hepatitis C virus RNA positive were then randomized to receive maintenance, low-dose peginterferon or to be followed on no treatment. Liver biopsies were done before enrollment and after 2 and 4 years of treatment or follow-up. The endpoints were development of cirrhosis, hepatic decompensation, hepatocellular carcinoma, death, or liver transplantation. 1050 patients were randomized and followed through the 4 year randomized phase of the trial and as long as 4 years off treatment. Serum samples collected at multiple time points, DNA and liver tissue are available for scientific investigation.

Proper citation: HALT-C Trial (RRID:SCR_001534) Copy   

•   Source: SciCrunch Registry

http://www.brain-dynamics.net/

The Brain Dynamics Centre (BDC) is a network of centers and units. It achieves a unique exploration of the healthy brain and disorders of brain function. It translates these insights into new ways to tailor treatments to the individual. There approach is: "integrative neuroscience" - bringing together clinical observations, theory, and modern imaging technologies. And it's theoretical framework derives from linking physiology, psychology and evolution. Additionally, BDC also actively researches ADHD and conduct disorder, stress and trauma-related problems, depression and anxiety, anorexia nervosa, psychosis (including early onset) and conversion disorders. The research facilities DBC include assessment, rooms, two cognition-brain function laboratories, genotyping and an MRI Suite with 1.5 and 3T GE systems. BDC is the coordinating site for an international network - BRAINnet. It has over 180 members, and coordinates access to the first standardized database on the human brain for scientific purposes: Brain Resource International Database.

Proper citation: Brain Dynamics Centre (RRID:SCR_001685) Copy   

•   Source: SciCrunch Registry

http://dial.mc.duke.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. The Duke Image Analysis Laboratory (DIAL) is committed to providing comprehensive imaging support in research studies and clinical trials to various agencies. The capabilities of the lab include protocol development, site training and certification, and image archival and analysis for a variety of modalities including magnetic resonance imaging, magnetic resonance spectroscopy, computed tomography and nuclear medicine. DIAL uses the latest technologies to analyze Magnetic Resonance Imaging (MRI) data sets of the brain. Currently the lab is engaged in measurement of the hippocampus, amygdala, caudate, ventricular system, and other brain regional volumes. Each of these techniques have undergone a rigorous validation process. The measurements of brain structures provide a useful means of non-invasively testing for changes in the brain of the patient. Changes over time in the brain can be detected, and evaluated with respect to the treatment that the patient is receiving. Magnetic Resonance Spectroscopy (MRS) allows DIAL to obtain an accurate profile of the chemical content of the brain. This sensitive technique can detect small changes in the metabolic state of the brain; changes that vary in response to administration of therapeutic agents. The ability to detect these subtle shifts in brain chemistry allows DIAL to identify changes in the brain with more sensitivity than allowed by image analysis. In this respect, NMR spectroscopy can provide early detection of changes in the brain, and serves to compliment the data obtained from image analysis. Additionally, DIAL also contains SQUID (Scalable Query Utility and Image Database). It is an image management system developed to facilitate image management in research and clinical trials: SQUID offers secure, redundant image storage and organizational functions for sorting and searching digital images for a variety of modalities including MRI, MRS, CAT Scan, X-Ray and Nuclear Medicine. SQUID can access images directly from DUMC scanners. Data can also be loaded via DICOM CDs, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Duke University Medical Center: Duke Image Analysis Laboratory (RRID:SCR_001716) Copy   

•   Source: SciCrunch Registry

http://hopkinsneuro.org/research/jhu_nimh/

The Johns Hopkins NIMH Center is comprised of an interdisciplinary research team who has pooled their talents to study the nature of HIV-associated neurocognitive disorders (HAND). Their aim is to translate discoveries of the pathophysiological mechanisms into novel therapeutics for HAND. Objectives * To integrate aspects of ongoing research in HAND and SIV encephalitis * Develop high-throughput and screening assays for identifying novel therapeutic compounds * Use proteomics and lipidomics approaches to indentifying surrogate markers of disease activity * Disseminate information and education about HAND through existing and new educational systems, including the JHU AIDS Education Training Center and the JHU Center for Global Clinical Education * Facilitate the entry of new investigators into Neuro-AIDS research, and to catalyze new areas of research, particularly where relevant for drug discovery or the development of validated surrogate markers

Proper citation: Johns Hopkins NIMH Research Center Novel Therapeutics of HIV-associated Cognitive Disorders (RRID:SCR_001891) Copy   

•   Source: SciCrunch Registry

http://www.biospace.com/

Online community for industry news and careers for life science professionals.

Proper citation: BioSpace (RRID:SCR_012010) Copy   

•   Source: SciCrunch Registry

http://www.minituba.org

miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA represents in a network view possible influences that occur between time varying variables in your dataset. For these networks of influence, miniTUBA predicts time courses of disease progression or response to therapies. minTUBA offers a probabilistic framework that is suitable for medical inference in datasets that are noisy. It conducts simulations and learning processes for predictive outcomes. The DBN analysis conducted by miniTUBA describes from variables that you specify how multiple measures at different time points in various variables influence each other. The DBN analysis then finds the probability of the model that best fits the data. A DBN analysis runs every combination of all the data; it examines a large space of possible relationships between variables, including linear, non-linear, and multi-state relationships; and it creates chains of causation, suggesting a sequence of events required to produce a particular outcome. Such chains of causation networks - are difficult to extract using other machine learning techniques. DBN then scores the resulting networks and ranks them in terms of how much structured information they contain compared to all possible models of the data. Models that fit well have higher scores. Output of a miniTUBA analysis provides the ten top-scoring networks of interacting influences that may be predictive of both disease progression and the impact of clinical interventions and probability tables for interpreting results. The DBN analysis that miniTUBA provides is especially good for biomedical experiments or clinical studies in which you collect data different time intervals. Applications of miniTUBA to biomedical problems include analyses of biomarkers and clinical datasets and other cases described on the miniTUBA website. To run a DBN with miniTUBA, you can set a number of parameters and constrain results by modifying structural priors (i.e. forcing or forbidding certain connections so that direction of influence reflects actual biological relationships). You can specify how to group variables into bins for analysis (called discretizing) and set the DBN execution time. You can also set and re-set the time lag to use in the analysis between the start of an event and the observation of its effect, and you can select to analyze only particular subsets of variables.

Proper citation: miniTUBA (RRID:SCR_003447) Copy   

•   Source: SciCrunch Registry

http://ncit.nci.nih.gov/

A reference terminology and core biomedical ontology for NCI that covers approximately 100,000 key biomedical concepts with terms, codes, definitions, and more than 200,000 inter-concept relationships. It is the reference terminology for NCI, NCI Metathesaurus and NCI informatics infrastructure covering vocabulary for clinical care, translational and basic research, and public information and administrative activities. It includes broad coverage of the cancer domain, including cancer related diseases, findings and abnormalities; anatomy; agents, drugs and chemicals; genes and gene products and so on. In certain areas, like cancer diseases and combination chemotherapies, it provides the most granular and consistent terminology available. It combines terminology from numerous cancer research related domains, and provides a way to integrate or link these kinds of information together through semantic relationships. NCIt features: * Stable, unique codes for biomedical concepts; * Preferred terms, synonyms, definitions, research codes, external source codes, and other information; * Links to NCI Metathesaurus and other information sources; * Over 200,000 cross-links between concepts, providing formal logic-based definition of many concepts; * Extensive content integrated from NCI and other partners, much available as separate NCIt subsets * Updated frequently by a team of subject matter experts. NCIt is a widely recognized standard for biomedical coding and reference, used by a broad variety of public and private partners both nationally and internationally including the Clinical Data Interchange Standards Consortium Terminology (CDISC), the U.S. Food and Drug Administration (FDA), the Federal Medication Terminologies (FMT), and the National Council for Prescription Drug Programs (NCPDP).

Proper citation: NCI Thesaurus (RRID:SCR_003563) Copy   

•   Source: SciCrunch Registry

https://www.calindex.org/

A three-year consortium that brings together insurers and health care providers to share information from approximately 9 million patients, with a goal that insights from the data will bring down healthcare costs and improve outcomes. It aims to be one of the largest health information exchanges in the country, with the goal of better connecting the vast, often disparate healthcare landscape across California. The database that will house patient data will be overseen by Orion Health, an independent eHealth software company. The information will only be used for clinical purposes. Academic research institutions can apply to use the Cal INDEX de-identified data for research to benefit the public good, such as population health initiatives. Cal INDEX has five main goals: * Improve the quality of care by providing clinicians with a unified statewide source of integrated patient information * Provide patients with a seamless transition between health plans or across various healthcare professionals and hospitals * Improve efficiency and reduce the cost of healthcare * Encourage healthcare technology innovation * Improve public health by providing de-identified data for medical research. Cal INDEX plans to launch at the end of 2014 with approximately 9 million health information records from combined members of Dignity Health and Blue Shield of California and Anthem Blue Cross. Cal INDEX is open to any health data contributor. Cal INDEX will establish a bi-directional data interface with providers to exchange data with EMRs and other hospital and office-based systems.

Proper citation: California Integrated Data Exchange (RRID:SCR_003747) Copy   

•   Source: SciCrunch Registry

http://www.aditecproject.eu/

A consortium that aims to accelerate the development of immunization technologies for the next generation of human vaccines. The goals are to characterize the mode of action and conduct comparative effectiveness studies of: adjuvants, vectors, formulations, delivery devices, routes of immunization, homologous and heterologous primeboost schedules, on vaccine efficacy. As part of these clinical trials, the consortium will also investigate the impact of host factors such as age, gender, genetics and pathologies. The consortium hopes to use insights gained from their projects to advance the development of next-generation vaccines, using tools such as standardized animal models to select promising immunization technologies. The intended outcome of this partnership is to improve the vaccine development process by advancing: basic research, new technology development, and clinical trial methods. Scientific objectives: # Development of adjuvants, vectors, formulations, and delivery devices # Selection of candidates, routes of immunization, and prime-boost combinations in animal models # Assessment of the impact of host factors in response to vaccination # Development of concepts and tools from human immunization # Development of concepts and tools to address regulatory and ethical issues posed by novel immunization technologies # Creation of an internationally recognized training program for translational immunology and vaccinology. Data is shared across the research partners within and between the different workstreams. Additionally, the consortium has plans to create a clinical database that combines phenotypic and clinical information to study the immune response to influenza vaccination at a population level, in an effort to advance studies into the effects of genetic background, gender, and disease on vaccine response.

Proper citation: Advanced Immunization Technologies (RRID:SCR_003741) Copy   

•   Source: SciCrunch Registry

http://www.abirisk.eu/

A consortium that seeks to provide an integrated approach to anti-drug immunization by evaluating immunogenicity in hemophilia A, multiple sclerosis, and inflammatory diseases, and exploring new tools for protein drug immunogenicity. The data collected will be pooled in a single immunogenicity databank and will be standardized and used to develop models of anti-drug antibodies. By examining the correlation between patient and clinical factors and the incidence of immunogenicity, it hopes to reduce the regulatory and resource burdens of immunogenicity testing. The objectives of the consortium are: # Access to large cohorts of patients treated with marketed biopharmaceutical products # Complementary expertise for anti-drug antibodies (ADA) assays; standardization and characterization of ADA # Novel integrated approaches to characterize anti-drug lymphocyte responses # Development and validation of innovative prediction tools # Collection and integration of immunogenicity-related data and clinical relevance of ADA ABIRISK is grouped into five working projects, which communicate with one another and provide each other with results and data for analysis. The five working projects are: ADA assay development and validation and cohort management; cellular characterization and mechanisms of the AD immune response; evaluation and development of technologies for predicting immunogenicity; establishment of database, data analyses and integration; and project management and communication.

Proper citation: ABIRISK (RRID:SCR_003740) Copy   

•   Source: SciCrunch Registry

http://www.oncotrack.eu/

An international consortium to develop and assess novel approaches to identify and characterize biological markers for colon cancer that will deepen the understanding of the variable make-up of tumors and how this affects the way patients respond to treatment. They will use cutting edge laboratory-based genome sequencing techniques coupled to novel computer modelling approaches to study both the biological heterogeneity of colon cancers (i.e. patient to patient variability) as well as tumor variation within the patient for example, by comparing primary tumors with metastases. This five year project brings together top scientists from European academic institutions offering a wide range of expertise, and partners them with pharmaceutical companies. The project is based on the premise that this genetic and epigenetic information, combined with a description of the molecular pathology of the tumor, will allow OncoTrack to generate a more accurate in-silico model of the cancer cell. This will facilitate the identification of predictive markers that can be used to guide the optimal therapy strategy at the level of the individual patient - and will also provide on-going prognostic guidance for the clinician. This project will not only advance understanding of the fundamental biology of colon cancers but will provide the means and approach for the identification of previously undetected biomarkers not only in the cancer under study, but potentially also in other solid cancers and, in doing so, open the door for personalized management of the oncology patient.

Proper citation: OncoTrack (RRID:SCR_003767) Copy   

•   Source: SciCrunch Registry

http://c-path.org/programs/pkd/

Consortium to develop evidence supporting the use of imaging Total Kidney Volume (TKV) as a prognostic biomarker that predicts the progression of Autosomal Dominant Polycystic Kidney Disease (ADPKD) to select patients likely to respond to therapy into clinical trials. It aims to replace the currently used measurement of glomerular filtration rate (GFR). Scientists will use the data collected to develop a disease progression model that will evaluate the relationship between TKV and the known complications of ADPKD, including rate of loss of kidney function, hypertension, gross hematuria, kidney stones, urinary tract infections, development of end-stage renal disease, and mortality. These analyses will be used to support the regulatory qualification of TKV as an accepted measure for assessing the progression of ADPKD in clinical trials in which new therapies are tested. PKDOC has the following goals: # Develop standard clinical data elements and definitions that are specific to ADPKD # Create a database of aggregated data from existing multiple, longitudinal, and well-characterized research registries maintained over decades by the leading institutions in ADPKD clinical investigation # Advance and harmonize the missions of regulatory agencies by creating tools that help with the evaluation of new pharmaceutical compounds # Develop a quantitative disease progression model to examine the linkage between TKV and disease outcomes

Proper citation: Polycystic Kidney Disease Outcomes Consortium (RRID:SCR_003674) Copy   

•   Source: SciCrunch Registry

http://www.transceleratebiopharmainc.com/

Non-profit research organization aiming to accelerate drug development by increasing the quality and efficiency of clinical studies through the development of shared tools, methods, and platforms. Consortium partnerships are limited to pharmaceutical and biotechnology companies with research & development operations, although there are collaborations with external organizations such the Clinical Data Interchange Standards Consortium (CDISC). Its current focus is to collaborate on: * Standardizing risk-based monitoring * Development of methods to qualify and train clinical trial sites * Development of a common investigator web portal * Development of clinical data standards on efficacy, and methods for comparator drug trials It currently has 5 projects: # Standardized Approach for High-Quality, Risk-Based Monitoring program aims to develop an industry-wide standard and approach for risk-based monitoring of clinical trials in order to enhance patient safety and ensure the quality of clinical trial data. # Shared Site Qualification and Training program aims to standardize GCP training and site qualification credentials in order to realize efficiencies and accelerate study start-up timelines. # Common Investigator Site Portal is a platform designed to streamline investigator and site access through harmonized delivery of content and services. # Data Standards project is a partnership with CDISC to develop industry-wide data standards in priority therapeutic areas to support the exchange and submission of clinical research and meta-data, improving patient safety and outcomes. # Comparator Drugs project aims to establish reliable, rapid sourcing of quality products for use in clinical trials through a comparator supply model enabling accelerated trial timelines and enhanced patient safety.

Proper citation: TransCelerate BioPharma (RRID:SCR_003728) Copy   

•   Source: SciCrunch Registry

http://www.themmrf.org/research-programs/commpass-study/

A personalized medicine initiative to discover biomarkers that can better define the biological basis of multiple myeloma to help stratify patients. This effort hopes to obtain samples from approximately 1,000 multiple myeloma patients and follow them over time to identify how a patient's genetic profile is related to clinical progression and treatment response. As a partnership between 17 academic centers, 5 pharmaceuticals and the Department of Veterans Affairs, the goal of this eight year study is to create a database that can accelerate future clinical trials and personalized treatment strategies. MMRF's CoMMpass Study has the following goals: * Create a guide to which treatments work best for specific patient subgroups. * Share data with researchers to accelerate drug development for specific subtypes of multiple myeloma patients. In order to facilitate discoveries and development related to targeted therapies, the comprehensive data from CoMMpass is placed in an open-access research portal. The data will be part of the Multiple Myeloma Research Foundation's (MMRF) Personalized Medicine Platform combines CoMMpass data with those collected from MMRF's Genomics Initiative. It is hoped that the longitudinal data, combined with the annotated bio-specimens will help provide insights that can accelerate personalized therapies.

Proper citation: MMRF CoMMpass Study (RRID:SCR_003721) Copy   

•   Source: SciCrunch Registry

http://www.cbioc.com/en/

Commercial organization that discovers, validates & analyzes genomic biomarkers with a focus on body fluid samples. Take advantage of their proven expertise in biomarker signature development and speed up your biomarker studies.

Proper citation: Comprehensive Biomarker Center (RRID:SCR_003901) Copy   

•   Source: SciCrunch Registry

http://www.imi.europa.eu/content/eu-aims

Consortium aiming to generate tools that will enhance understanding of autism spectrum disorders (ASD) and pave the way for the development of new, safe and effective treatments for use in both children and adults. For example, the team will gather samples from people bearing certain mutations associated with ASD; this will pave the way for the generation of cell lines that can be used to test treatments. Elsewhere, the researchers will advance the use of brain scans as a tool to boost ASD drug discovery and also identify which people with ASD might respond best to a given drug. The project will also create a pan-European network of clinical sites. As well as making it easier to run clinical trials, this network will create an interactive platform for those with ASD and professionals. By the end of the 5 year project they expect to provide novel validated cellular assays, animal models, new fMRI methods with dedicated analysis techniques, new PET radioligands, as well as new genetic and proteomic biomarkers for patient-segmentation or individual response prediction. They will provide a research network that can rapidly test new treatments in man. These tools should provide their EFPIA partners with an added competitive advantage in developing new drugs for ASD.

Proper citation: EU-AIMS (RRID:SCR_003861) Copy   

•   Source: SciCrunch Registry

http://earip.eu/

Consortium that convenes asthma experts from across Europe to define research gaps to reduce the impact of asthma. The project activities range from basic cell science research, to assessing and improving European healthcare systems. Their activities include workshops, prioritization exercises, consensus strategies, and the development and publication of a set of recommendations about what's needed to reduce asthma deaths and hospitalizations. The eventual goal is to have a comprehensive R&D roadmap for asthma. EARIP will target a number of asthma research areas to ensure a comprehensive overview of all current research strategies from across Europe is included in the project road map. These include: * Research into biological targets, aiming to discover new targets and better define the role of existing biological targets * Identify new systems, models and tools for phenotypic stratification * Develop better and more efficient healthcare systems across Europe * Define and develop new diagnostic tools * Assess and improve patient self-management systems and provide suggestions for how these can be developed * Identify how to establish a European Innovation Partnership (EIP) for the management of asthma * Establish a European research network of clinical asthma research facilities

Proper citation: EARIP (RRID:SCR_003854) Copy   

•   Source: SciCrunch Registry

http://www.transformproject.eu/portfolio-item/d6-2-clinical-research-information-model/

A clinical research information model for the integration of clinical research covering randomized clinical trials (RCT), case-control studies and database searches into the TRANSFoRm application development. TRANSFoRm clinical research is based on primary care data, clinical data and genetic data stored in databases and electronic health records and employs the principle of reusing primary care data, adapting data collection by patient reported outcomes (PRO) and eSource based Case Report Forms. CRIM was developed using the TRANSFoRm clinical use cases of GORD and Diabetes. Their use case driven approach consisted of three levels of modelling drawing heavily on the clinical research workflow of the use cases. Different available information models were evaluated for their usefulness to represent TRANSFoRm clinical research, including for example CTOM of caBIG, Primary Care Research Object Model (PRCOM) of ePCRN and BRIDG of CDISC. The PCROM model turned out to be the most suitable and it was possible to extend and modify this model with only 12 new information objects, 3 episode of care related objects and 2 areas to satisfy all requirements of the TRANSFoRm research use cases. Now the information model covers Good Clinical Practice (GCP) compliant research, as well as case control studies and database search studies, including the interaction between patient and GP (family doctor) during patient consultation, appointment, screening, patient recruitment and adverse event reporting.

Proper citation: TRANSFoRm Clinical Research Information Model (RRID:SCR_003889) Copy   

•   Source: SciCrunch Registry