Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Translational behavioral research in respiratory medicine.

ABSTRACT: Human behavior can prevent or invite disease and is a major determinant of treatment success. Consequently, many efforts have been directed toward developing interventions to promote behaviors essential to managing or preventing respiratory disease. The process of developing, testing, and disseminating health behavior interventions should closely follow the translational research paradigm. However, most behavioral investigators have failed to adequately apply the translational research paradigm to behavioral research. The final stage of translation, consisting of testing the effectiveness of interventions in broad clinical settings after efficacy has been demonstrated in randomized controlled trials, is too often omitted. Additionally, the important task of understanding why any given health behavior intervention succeeds with some people but fails to change behavior in others is inconsistently pursued and seldom used to develop the robust theories of behavior change needed to improve respiratory health. Bringing health behavior research through the full translational process has the potential to significantly enhance respiratory health with specific behavioral targets, including smoking cessation, obesity prevention, TB control, and adherence to a multitude of respiratory treatments.

SUBMITTER: Bender BG

PROVIDER: S-EPMC4694175 | biostudies-other | 2011 Jun

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Detection and characterization of translational research in cancer and cardiovascular medicine.

Project description:BackgroundScientists and experts in science policy have become increasingly interested in strengthening translational research. Efforts to understand the nature of translational research and monitor policy interventions face an obstacle: how can translational research be defined in order to facilitate analysis of it? We describe methods of scientometric analysis that can do this.MethodsWe downloaded bibliographic and citation data from all articles published in 2009 in the 75 leading journals in cancer and in cardiovascular medicine (roughly 15,000 articles for each field). We calculated citation relationships between journals and between articles and we extracted the most prevalent natural language concepts.ResultsNetwork analysis and mapping revealed polarization between basic and clinical research, but with translational links between these poles. The structure of the translational research in cancer and cardiac medicine is, however, quite different. In the cancer literature the translational interface is composed of different techniques (e.g., gene expression analysis) that are used across the various subspecialties (e.g., specific tumor types) within cancer research and medicine. In the cardiac literature, the clinical problems are more disparate (i.e., from congenital anomalies to coronary artery disease); although no distinctive translational interface links these fields, translational research does occur in certain subdomains, especially in research on atherosclerosis and hypertension.ConclusionsThese techniques can be used to monitor the continuing evolution of translational research in medicine and the impact of interventions designed to enhance it.

| S-EPMC3100249 | biostudies-literature

PlatformTM, a standards-based data custodianship platform for translational medicine research.

Project description:Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. There is currently a lack of dedicated infrastructure focused on the 'manageability' of the data lifecycle in TM research between data collection and analysis. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets.

| S-EPMC6692384 | biostudies-literature

CRISPR/Cas9-mediated genome editing: From basic research to translational medicine.

Project description:The recent development of the CRISPR/Cas9 system as an efficient and accessible programmable genome-editing tool has revolutionized basic science research. CRISPR/Cas9 system-based technologies have armed researchers with new powerful tools to unveil the impact of genetics on disease development by enabling the creation of precise cellular and animal models of human diseases. The therapeutic potential of these technologies is tremendous, particularly in gene therapy, in which a patient-specific mutation is genetically corrected in order to treat human diseases that are untreatable with conventional therapies. However, the translation of CRISPR/Cas9 into the clinics will be challenging, since we still need to improve the efficiency, specificity and delivery of this technology. In this review, we focus on several in vitro, in vivo and ex vivo applications of the CRISPR/Cas9 system in human disease-focused research, explore the potential of this technology in translational medicine and discuss some of the major challenges for its future use in patients.

| S-EPMC7171402 | biostudies-literature

Supporting implementation of evidence-based behavioral interventions: the role of data liquidity in facilitating translational behavioral medicine.

Project description:The advancement of translational behavioral medicine will require that we discover new methods of managing large volumes of data from disparate sources such as disease surveillance systems, public health systems, and health information systems containing patient-centered data informed by behavioral and social sciences. The term "liquidity," when applied to data, refers to its availability and free flow throughout human/computer interactions. In seeking to achieve liquidity, the focus is not on creating a single, comprehensive database or set of coordinated datasets, nor is it solely on developing the electronic health record as the "one-stop shopping" source of health-related data. Rather, attention is on ensuring the availability of secure data through the various methods of collecting and storing data currently existent or under development-so that these components of the health information infrastructure together support a liquid data system. The value of accessible, interoperable, high-volume, reliable, secure, and contextually appropriate data is becoming apparent in many areas of the healthcare system, and health information liquidity is currently viewed as an important component of a patient-centered healthcare system. The translation from research interventions to behavioral and psychosocial indicators challenges the designers of healthcare systems to include this new set of data in the correct context. With the intention of advancing translational behavioral medicine at the local level, "on the ground" in the clinical office and research institution, this commentary discusses data liquidity from the patient's and clinician's perspective, requirements for a liquid healthcare data system, and the ways in which data liquidity can support translational behavioral medicine.

| S-EPMC3717701 | biostudies-other

Models of cardiovascular surgery biobanking to facilitate translational research and precision medicine.

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.

| S-EPMC8787984 | biostudies-literature

Challenges and opportunities to bridge translational to clinical research for personalized mitochondrial medicine.

Project description:Mitochondrial disorders are a group of rare and heterogeneous genetic diseases characterized by dysfunctional mitochondria leading to deficient adenosine triphosphate synthesis and chronic energy deficit in patients. The majority of these patients exhibit a wide range of phenotypic manifestations targeting several organ systems, making their clinical diagnosis and management challenging. Bridging translational to clinical research is crucial for improving the early diagnosis and prognosis of these intractable mitochondrial disorders and for discovering novel therapeutic drug candidates and modalities. This review provides the current state of clinical testing in mitochondrial disorders, discusses the challenges and opportunities for converting basic discoveries into clinical settings, explores the most suited patient-centric approaches to harness the extraordinary heterogeneity among patients affected by the same primary mitochondrial disorder, and describes the current outlook of clinical trials.

| S-EPMC10903101 | biostudies-literature

Using deep learning to identify translational research in genomic medicine beyond bench to bedside.

Project description:Tracking scientific research publications on the evaluation, utility and implementation of genomic applications is critical for the translation of basic research to impact clinical and population health. In this work, we utilize state-of-the-art machine learning approaches to identify translational research in genomics beyond bench to bedside from the biomedical literature. We apply the convolutional neural networks (CNNs) and support vector machines (SVMs) to the bench/bedside article classification on the weekly manual annotation data of the Public Health Genomics Knowledge Base database. Both classifiers employ salient features to determine the probability of curation-eligible publications, which can effectively reduce the workload of manual triage and curation process. We applied the CNNs and SVMs to an independent test set (n = 400), and the models achieved the F-measure of 0.80 and 0.74, respectively. We further tested the CNNs, which perform better results, on the routine annotation pipeline for 2 weeks and significantly reduced the effort and retrieved more appropriate research articles. Our approaches provide direct insight into the automated curation of genomic translational research beyond bench to bedside. The machine learning classifiers are found to be helpful for annotators to enhance the efficiency of manual curation.

| S-EPMC6367517 | biostudies-literature

Vasculopathy in sickle cell disease: Biology, pathophysiology, genetics, translational medicine, and new research directions.

Project description:Sickle cell disease has been very well characterized as a single amino acid molecular disorder of hemoglobin leading to its pathological polymerization, with resulting red cell rigidity that causes poor microvascular blood flow, with consequent tissue ischemia and infarction. More recently, an independent spectrum of pathophysiology of blood vessel function has been demonstrated, involving abnormal vascular tone and activated, adhesive endothelium. These vasculopathic abnormalities are attributable to pathways involving hemolysis-associated defects in nitric oxide bioavailability, oxidative stress, ischemia-reperfusion injury, hemostatic activation, leukocytes and platelets. Vasculopathy of sickle cell disease has been implicated in the development of pulmonary hypertension, stroke, leg ulceration and priapism, particularly associated with hemolytic severity, and reported also in other severe hemolytic disorders. This vasculopathy might also play a role in other chronic organ dysfunction in patients with sickle cell disease. These pathways present novel targets for pharmacologic intervention, and several clinical trials are already under way. The authors present their perspectives of a workshop held at the National Institutes of Health in August 2008 on vasculopathy in sickle cell disease, along with meritorious future scientific questions on the topic of vascular complications of sickle cell disease.

| S-EPMC3209715 | biostudies-literature

Nanoscale Bilirubin Analysis in Translational Research and Precision Medicine by the Recombinant Protein HUG

Project description: Not available

| S-EPMC10671013 | biostudies-literature

An Official American Thoracic Society Workshop Report: Translational Research in Rare Respiratory Diseases.

Project description:Rare respiratory diseases (RRDs) are a heterogeneous group of disorders that collectively represent a significant health care burden. In recent years, strong advocacy and policy initiatives have led to advances in the implementation of research and clinical care for rare diseases. The development of specialized centers and research networks has facilitated support for affected individuals as well as emerging programs in basic, translational, and clinical research. In selected RRDs, subsequent gains in knowledge have informed the development of targeted therapies and effective diagnostic tests, but many gaps persist. There was therefore a desire to identify the elements contributing to an effective translational research program in RRDs. To this end, a workshop was convened in October 2015 with a focus on the implementation of effective transnational research networks and collaborations aimed at developing novel diagnostic and therapeutic tools. Key elements included an emphasis on molecular pathogenesis, the continuing engagement of patient advocacy groups and policy makers, the effective use of preclinical models in the translational research pipeline, and the detailed phenotyping of patient cohorts. During the course of the workshop, current logistical and knowledge gaps were identified, and new solutions or opportunities were highlighted.

| S-EPMC5946685 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data