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http://www.hopkins-hivguide.org/

Launched in 2004, the HIV Guide is a single disease resource, with two main parts: the HIV database, which is accessed by searching on diagnosis, drug name, pathogen, or management or by accessing the resistance tool, and there are also browsable areas of the site, which include news, features, continuing medical education programs and other types of additional readings and information. Guides are authored by academic clinicians and subject to rigorous peer review. You may browse the guide by: Diagnosis Covering opportunistic infections, malignancies, and complications of therapy. Drugs Includes indications, dosing, drug interactions, and author recommendations. Pathogen - Describes microbiology, clinical syndromes, and therapy. Management Including antiretroviral therapy guidelines and strategies. Resistance Tool Provides up-to-date interpretation of genotypic resistance test results. Whether searching for a drug, a pathogen, a diagnosis, or a management issue, your search results will be delivered in a concise and standard form designed to give you the most clinically useful information first, with the option to go deeper if you choose. If you search by diagnosis, you will receive a page listing points covering establishment of a diagnosis, related pathogens, treatment recommendations, issues to consider on follow up, references and more. At each step, we provide you immediately with the information you need to treat the diagnosis and give you the option to read more or more deeply if you choose. On the diagnosis page, you are also provided with links to the information sheet for each drug that may be prescribed, and if you indicate which drug you intend to use, you will be provided with relevant drug selected comments. If you search by drug, you will receive a page listing FDA indications, usual adult dosing, adverse drug reactions, drug interactions, spectrum, and forms. You are also able to access full pharmacological information (mechanism, absorption, Cmax, volume of distribution, protein binding, metabolism/excretion, t _, dosing for glomerular filtration of 50-80, dosing for glomerular filtration of 10-50, dosing for glomerular filtration of <10 ml/min, dosing in hemodialysis, dosing in peritoneal dialysis, dosing in cavh, dosing for decreased hepatic function, pregnancy risk, and breast feeding compatibility). If you search by pathogen, you will receive a page covering the microbiology, clinical relevance, sites of infection, drug selected comments, other information and references. You are also provided with links to information for each drug that may be prescribed, and if you indicate which drug you intend to use, you will be provided with the drug selected comments for that choice. If you search by management, you will receive a page listing definition, indications, and clinical recommendations and additional details, including references. If you click on more wherever it appears on a page, you will find more detailed material about the topic. In addition, the HIV Guide homepage contains a Features section and Literature Review that contain synopses and articles about pertinent topics. The Publications section also provides .pdf versions of the Hopkins HIV Report. Prices represent the cost per unit specified, reflecting the Average Wholesale Price (AWP). AWP prices are taken from the Red Book, manufacturer information, and the McKesson database. These prices are updated every six months. We have listed up to 10 FDA-approved indications for uses of drugs. Though in some cases more may exist, for brevity and formatting issues authors and editors have chosen what they deem the most important. Also listed are disease states for which a drug may be likely prescribed regardless of FDA approval status (see Non-FDA approved uses). The HIV Guide is primarily focused on adult care but does cover issues of perinatal transmission. The material presented on this site represents the considered opinion of the Hopkins expert listed as the author of the module as of the date indicated. The reference section contains an annotated list of the articles that the author considers to be most relevant to the topic. Where authoritative guidelines exist, such as CDC, IDSA or Medical Letter guidelines, they are referenced and discussed along with the author''s recommendations presented.

Proper citation: HIV Guide (RRID:SCR_008252) Copy   

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http://www.atlas.or.kr/

Database of images on medical parasitology created to provide educational materials for medical students primarily, but professional workers in medical or paramedical fields may also refer to this site covering the significant parasites in the world. Each database of protozoans, nematodes, trematodes, cestodes and arthropods contains information on the morphology, life cycle, geographical distribution, symptoms, prevention, etc. Users who wish to contribute can send the editor unpublished images of human parasites (microscopical, clinical, radiological or epidemiological aspects of human parasitic infections) by mail or e-mail. Pathology specimens (slide, samples) are welcome too. The A.M.P. received the citation of reliable sources such as Parasitology today and The Lancet, and is now listed in the Internet Resources on Specific Infectious Diseases Topics of the Mandell, Douglas and Bennets Principles and Practice of Infectious Diseases Fifth Edition.
This website was established with a great contribution of the PROJECT COLLABORATORS and many contributors of The Korean Society for Parasitology.

Proper citation: Atlas of Medical Parasitology (RRID:SCR_008163) Copy   

•   Source: SciCrunch Registry

http://www.hopkins-abxguide.org/

Concise, clinically useful information for diagnosing, managing and treating infectious diseases in adults; however it does cover some pediatric topics including vaccines. It is designed for primary care providers and other non-infectious disease specialists as a tool that can be used at the point of care to assist in prescribing antibiotics.

Proper citation: ABX Guide (RRID:SCR_008214) Copy   

•   Source: SciCrunch Registry

http://www.crdamc.amedd.army.mil/behav-health/strong-star.aspx

A multidisciplinary and multi-institutional research consortium to develop and evaluate the most effective early interventions possible for the detection, prevention, and treatment of combatrelated posttraumatic stress disorder (PTSD) in activeduty military personnel and recently discharged veterans. Complementary investigations are focused on the root causes of PTSD, including biological factors that influence PTSD susceptibility and recovery; the influence of comorbid physical and psychological ailments; and the interaction of cognitive-behavioral therapies and pharmacologic treatments. The full cohort of STRONG STAR trials include: Treatment Studies, Biological Studies, Epidemiological Studies, and Preclinical Studies. STRONG STAR is currently conducting three clinical treatment trials at Carl R. Darnall Army Medical Center (CRDAMC). The studies are examining the effectiveness of Cognitive Processing Therapy (CPT), Prolonged Exposure Therapy (PE) and Cognitive Behavioral Therapy for Insomnia (CBTi) with active duty service members. Treatments are offered in individual, group, and online formats, and last from two to eight weeks. Study participants must be active duty service members who will remain in the Ft Hood area for at least 34 months to complete initial assessments and treatment programs. Referrals to the treatment studies can be made through a behavioral health provider or through selfreferral., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Strong Star (RRID:SCR_003132) Copy   

•   Source: SciCrunch Registry

http://edoctoring.ncl.ac.uk/Public_site/

Online educational tool that brings challenging clinical practice to your computer, providing medical education that is engaging, challenging and interactive. While there is no substitute for real-life direct contact with patients or colleagues, research has shown that interactive online education can be a highly effective and enjoyable method of learning many components of clinical medicine, including ethics, clinical management, epidemiology and communication skills. eDoctoring offers 25 simulated clinical cases, 15 interactive tutorials and a virtual library containing numerous articles, fast facts and video clips. Their learning material is arranged in the following content areas: * Ethical, Legal and Social Implications of Genetic Testing * Palliative and End-of-Life Care * Prostate Cancer Screening and Shared Decision-Making

Proper citation: eDoctoring (RRID:SCR_003336) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/CMO

An ontology designed to be used to standardize morphological and physiological measurement records generated from clinical and model organism research and health programs.

Proper citation: Clinical Measurement Ontology (RRID:SCR_003291) Copy   

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http://purl.bioontology.org/ontology/MMO

An ontology designed to represent the variety of methods used to make qualitative and quantitative clinical and phenotype measurements both in the clinic and with model organisms.

Proper citation: Measurement Method Ontology (RRID:SCR_003373) Copy   

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https://cabig.nci.nih.gov/tools/caTRIP

THIS RESOURCE IS NO LONGER IN SERVICE documented June 4, 2013. Allows users to query across a number of caBIG data services, join on common data elements (CDEs), and view results in a user-friendly interface. With an initial focus on enabling outcomes analysis, caTRIP allows clinicians to query across data from existing patients with similar characteristics to find treatments that were administered with success. In doing so, caTRIP can help inform treatment and improve patient care, as well as enable the searching of available tumor tissue, enable locating patients for clinical trials, and enable investigating the association between multiple predictors and their corresponding outcomes such as survival caTRIP relies on the vast array of open source caBIG applications, including: * Tumor Registry, a clinical system that is used to collect endpoint data * cancer Text Information Extraction System (caTIES), a locator of tissue resources that works via the extraction of clinical information from free text surgical pathology reports. while using controlled terminologies to populate caBIG-compliant data structures * caTissue CORE, a tissue bank repository tool for biospecimen inventory, tracking, and basic annotation * Cancer Annotation Engine (CAE), a system for storing and searching pathology annotations * caIntegrator, a tool for storing, querying, and analyzing translational data, including SNP data Requires Java installation and network connectivity.

Proper citation: caTRIP (RRID:SCR_003409) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/IDOMAL

An application ontology to cover all aspects of malaria (clinical, epidemiological, biological, etc) as well as the intervention attempts to control it, extending the infectious disease ontology (IDO).

Proper citation: Malaria Ontology (RRID:SCR_003369) 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   

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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   

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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.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.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://www.transformproject.eu/

Project to develop a ''rapid learning healthcare system'' driven by advanced computational infrastructure that can improve both patient safety and the conduct and volume of clinical research in Europe. Three carefully chosen clinical ''use cases'' will drive, evaluate and validate the approach to the ICT (information and communications technology) challenges. The project will build on existing work at international level in clinical trial information models (BRIDG and PCROM), service-based approaches to semantic interoperability and data standards (ISO11179 and controlled vocabulary), data discovery, machine learning and electronic health records based on open standards (openEHR). TRANSFoRm will extend this work to interact with individual eHR systems as well as operate within the consultation itself providing both diagnostic support and support for the identification and follow up of subjects for research. The approach to system design will be modular and standards-based, providing services via a distributed architecture, and will be tightly linked with the user community. Four years of development and testing will end with a fifth year that will be dedicated to summative validation of the project deliverables in the Primary Care setting. In order to support patient safety in both clinical and research settings, significant ICT challenges need to be overcome in the areas of interoperability, common standards for data integration, data presentation, recording, scalability, and security., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: TRANSFoRm (RRID:SCR_003888) Copy   

•   Source: SciCrunch Registry

http://www.alzheimer-europe.org/Research/PharmaCog

Project aiming to tackle bottlenecks in Alzheimer''''s disease research and drug discovery by developing and validating new tools to test candidate drugs for the treatment of symptoms and disease in a faster and more sensitive way. They will provide the tools needed to define more precisely the potential of a drug candidate, reduce the development time of new medicines and thus accelerate the approvals of promising new medicines. By bringing together databases of previously conducted clinical trials and combining the results from blood tests, brain scans and behavioral tests, the scientists will develop a ''''signature'''' that gives more accurate information on the progression of the disease and the effect of candidate drugs than current methods do. The scientists will conduct parallel studies in laboratory models, healthy volunteers and patients in order to better predict good new drugs as early as possible. This will enable them, for instance, to find out how memory loss in Alzheimer''''s disease can be simulated in healthy volunteers, for example with sleep deprivation or drugs that temporarily affect the memory, in order to test the effect of candidate-medicines early in the drug development process.

Proper citation: PharmaCog (RRID:SCR_003878) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/p2tconsortium/

A three-member pharmaceutical industry consortium that aims to provide a new platform to improve access to information about clinical trials for patients and providers. The platform aims to enhance the existing clinicaltrials.gov by providing more detailed and patient-friendly information about available trials and embedding a machine-readable target health profile to improve the ability of healthcare software to match individual health profiles with applicable clinical trials. Using clinicaltrials.gov as its foundation and Eli Lilly''''s Application Programming Interface (API), the consortium is focused on creating an open platform to make this data more amenable to patients and providers, as well as creating an opportunity to integrate a patient''''s electronic health record into the clinical trial matching service. This feature will allow patients to search for trials using their own Blue Button data. The following features are planned add-ons to clinicaltrials.gov: * Target Profile is a machine readable query, that can be executed against an electronic file (or record) with patient health data such as an Electronic Health Record (EHR), an Electronic Medical Record (EMR) or Personally Controlled Health Record (PCHR) * Augmented Content is public, IRB approved information about the study that has not been published on clinicaltrials.gov, and that is shared with / targeted for patients with a matching Target Profile. The following are the incremental goals of the consortium: * Advancement of the Lilly API platform to support read/write interaction and additional data objects and information. * The initial 3 sponsor organizations - Lilly, Pfizer and Novartis - will upload Target Profiles for a select set of clinical trials. A Target Profile is a machine interpretable description of the characteristics of patients who may qualify for that trial i.e. a query that can be executed against a patient''''s electronic health record or personal health record. Additionally, sponsors of clinical research studies will also be able to upload Augmented Content to the Lilly Platform to supplement information on clinicaltrials.gov with additional, patient-focused information about the study, e.g., a study brochure and practical information on how to contact investigational sites. * A matching service, developed by Corengi, will compare Target Profiles to a de-dentified personally controlled health record (PCHR), represented by patient''''s Blue Button Plus CCDA XML document. * Integration into a patient community platform from Avado for providing the patient PCHR and presenting the results of the match service. The patient will be able to explore the respective matching studies for additional information and next steps such as contacting a nearby investigator clinic or hospital. The first demo of the prototype was made available on June 2014, built on a database of anonymized patient health records from different clinical research studies sponsored by Lilly, Novartis, and Pfizer. Other website: http://portal.lillycoi.com/

Proper citation: Patients to Trials Consortium (RRID:SCR_003877) Copy   

•   Source: SciCrunch Registry