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Results for “Translational Research, Biomedical”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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At least 145 records · Page 8Linked to original sources
PubMed2005

Translational clinical trials: an entropy-based approach to sample size.

Translational clinical trials are small studies of therapies emerging from the laboratory. These trials are essential for generating early evidence regarding the effects of treatment on specific targets in the disease pathway and for guiding the next studies to be done. The statistical properties of such studies have been neglected, in part, because they do not fit the well-known clinical trials developmental paradigm. This paper discusses the translational trial setting, and presents an information (entropy) based approach to understanding the properties and use of these trials. The combination of biological knowledge with a designed experiment (albeit a small one) is a powerful device for resolving much of the considerable uncertainty surrounding an emerging therapeutic concept. An approach to motivating the sample size for translational trials is presented.

Animals↗
PubMedDate not supplied

The Michigan Clinical Research Collaboratory: following the NIH Roadmap to the community.

PURPOSE: This case study describes a successful National Institutes of Health (NIH) Roadmap application that has created a new and innovative translational science partnership at the University of Michigan. METHODS: We describe the institution within which the grant application was developed, the role played by the Department of Family Medicine, the role of prior successes in translational and community-based research, the associated infrastructure development, the application development process, and some features of the final project. RESULTS: A partnership among 2 clinical and research centers of excellence at the University of Michigan, 3 practice-based research networks, and a clinical research center was created as the platform to support an NIH Roadmap. The result was a funded 3.3 million dollar, 3-year project supporting the creation of the Michigan Clinical Research Collaboratory (MCRC), a research infrastructure that will support the design, conduct, and dissemination of community-based clinical translation research. The MCRC depends to a considerable degree on the experience, expertise, and infrastructure in community-based translational research of the Department of Family Medicine. CONCLUSIONS: The successful funding of the MCRC grant will support influential translational research programs of high impact and visibility that would not otherwise have occurred. The MCRC grant is an acknowledgment of the important research to be done in the community, the critical nature of infrastructure investment and prior work in competing successfully for such funding, and the personnel and information technology investments required for success. Collaboration between practice-based family medicine investigators and traditional clinical investigators at the University of Michigan has led to successful competition for an NIH Roadmap grant, which has led in turn to greater institutional recognition for the importance and legitimacy of community-based translational research.

Academic Medical Centers↗
PubMed2005

The challenge of cross-cultural clinical trials research: case report from the Tibetan Autonomous Region, People's Republic of China.

Efforts to conduct Western clinical research in non-Western medical settings with little or no familiarity with such methodologies are on the rise, but documented accounts of the ways that biomedical science requires negotiation and translation across cultures are not plentiful. This article adds to this literature through analysis of an NICHD-funded collaborative research effort in women's health carried out in the Tibetan Autonomous Region of the People's Republic of China. The research involved a feasibility study for an eventual clinical trial comparing Tibetan medicine with misoprostol for preventing postpartum hemorrhage in delivering women. It explores strategies of negotiation and translation in and around notions of the scientific method, informed consent procedures, randomization, blinding, placebo, and concepts of medical standardization.

Clinical Trials as Topic↗
PubMed2005

Emerging scientific advances: how do they enter dental curricula and the profession?

What is meant by emerging scientific advances? In brief, this terminology is equivalent to new research findings, however, the term "research" is often associated with scientific investigations that have very limited direct clinical relevance. Unfortunately, basic dental research and dental clinical practice have, in many instances, been considered to have nonoverlapping spheres of existence. The existence of mutually exclusive domains is rapidly changing with considerable translational activities between basic research investigation and clinical application developing. There is a growing emphasis at a national level for the importance of moving basic biomedical research laboratory findings into clinical patient-related applications to realize improvements in health based on the research findings. Ultimately, new approaches to diagnose, treat, and prevent disease will be available and represent the translation of the best scientific evidence into clinical applications. It is critical at this time to prepare our dental graduates to be members of the dental profession who will understand the implications that new scientific advances will have on their approach to patient care. The patterns and practices of oral health care delivery will undergo major changes during the careers of our new dental graduates. They need to be prepared to respond to these changes to the benefit of their patients.

Curriculum↗
PubMed2025

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data.

The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.

Journal Article↗
PubMed2004

Materializing research promises: opportunities, priorities and conflicts in translational medicine.

There is considerable evidence that the translation rate of major basic science promises to clinical applications has been inefficient and disappointing. The deficiencies of translational science have often been proposed as an explanation for this failure. An alternative explanation is that until recently basic science advances have made oversimplified assumptions that have not matched the true etiological complexity of most common diseases; while clinical science has suffered from poor research practices, overt biases and conflicts of interest. The advent of molecular medicine and the recasting of clinical science along the principles of evidence-based medicine provide a better environment where translational research may now materialize its goals. At the same time, priority issues need to be addressed in order to exploit the new opportunities. Translational research should focus on diseases with global impact, if true progress is to be made against human suffering. The health outcomes of interest for translational efforts need to be carefully defined and a balance must be struck between the subjective needs of healthcare consumers and objective health outcomes. Development of more simple, practical and safer interventions may be as important a target for translational research as the development of cures for diseases where no effective interventions are available at all. Moreover, while the role of the industry is catalytic in translating research advances to licensed interventions, academic independence needs to be sustained and strengthened at a global level. Conflicts of interest may stifle translational research efforts internationally. The profit motive is unlikely to be sufficient alone to advance biomedical research towards genuine progress.

Editorial↗
PubMed2002

A rule driven bi-directional translation system for remapping queries and result sets between a mediated schema and heterogeneous data sources.

As the number of online biomedical data sources increases, so too do the number of ways to access such data. The research described herein focuses on creating a data access system that provides bi-directional translation and mapping of data between heterogeneous databases and a mediated schema. Semantic mapping rules stored in a knowledge base are used by our generalized software to convert XML query results obtained from each data source to a common schema representing a single ontology. We apply this approach to the domain of online genetic databases, demonstrating the system's scalability and integratability.

Databases as Topic↗