1,165 research outputs found

    Bench, Bedside, Curbside, and Home: Translational Research to Include Transformative Change Using Educational Research

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    Translational research originated in the medical field during the 1990s to describe taking discovery based research through the steps of applying it to clinical research and patient-oriented care. This model is implicitly linear, depicting the flow of information from researchers’ bench, to a clinical trial bedside, to a primary care physician’s practice. The prevailing model of translational research, referred to as “Bench to Bedside to Curbside,” is limited in that it does not adequately incorporate stakeholders outside of the professional or research community because Curbside refers to physician care delivered to patients. This omits the transformative impact that research can have on the general populace if implemented through educational research, disseminating knowledge to people who can use it. In this article we argue that a fourth category needs to be incorporated into the previous T1-T3 Bench to Bedside to Curbside model, and this fourth category represents T4, “Home.” We seek to further define and describe, while providing a new model for translational research that is more circular in nature and inclusive of the general populace. We also suggest that the incorporation of educational researchers and practitioners would expand the current collaborative nature of translational research and is a way to expand the translational model. This promises more adequate, effective, and sustainable impacts on a target population

    Biomedical Informatics Applications for Precision Management of Neurodegenerative Diseases

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    Modern medicine is in the midst of a revolution driven by “big data,” rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine

    Towards an alternative framework for the evaluation of translational research initiatives

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    Ingenio Working Paper SeriesThis paper will propose a framework for evaluating translational research by identifying the way in which translational research occurs in practice (rather than the formal linear stages in which the results of such process are typically presented). Following previous work on methods to evaluation science-society interactions, our approach will focus on the processes of TR and the ways in which public initiatives to support new ways of conducting research succeed or fail. Our starting point is that TR is expressed through complex cycles where knowledge is moving back and forth through the bedside-to-bench continuum across various channels, giving rise to complex interactions between research performers and the user of the results of such research. The approach is rooted on empirical context of IDIBAPS, a university-hospital joint institute in Barcelona, one of the European centre of excellence for TR, and a study on social networks and knowledge flows in the Spanish Biomedical Research Networking Centres (CIBERs). Further, we suggest that interactions between biomedical actors are less than optimal because the distances that separate these different groups make the interactions difficult. We end up by stating that learning processes and knowledge exchange interactions are facilitated and strengthened by five forms of proximity: cognitive, social, organisational, institutional and spatial.N

    Translational insights into the genetic etiology of mental health disorders: Examining risk factor models, neuroimaging, and current dissemination practices

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    Psychiatric genetics is a basic science field that has potential for practical application and effective translation. To date, translational frameworks utilized by this field have been linear (e.g., sequential) in nature, focusing on molecular genetic information. It is proposed that non-linear (e.g., socio-ecological) frameworks are a better way to immediately translate non-molecular genetic information. This dissertation explored the translation of psychiatric genetic information in two ways. First, a survey was sent to academic stakeholders to assess the state of the science regarding the translation of genetic information to the clinical care of mental health disorders. Findings from this indicate a translation-genetic competence gap whereby genetic knowledge reinforces linear frameworks and genetic competence is needed to achieve effective translation in this content area. Second, a new risk factor model for social anxiety was created that incorporated genetic, environmental, and neurophysiological risk factors (behavioral inhibition, parental bonding, emotion reactivity). Findings indicate that genetic etiology is more informative knowledge that can influence risk factor models and possibly prevention and intervention efforts for social anxiety. Overall this dissertation paves the way for examining the translational capacity of psychiatric genetics in a clinical setting. It constitutes the first examination of barriers to and a potential solution for the most effective translation of psychiatric genetic information

    Multiomics tools for improved atherosclerotic cardiovascular disease management

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    Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple ‘omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care

    Personalized Medicine: the Future of Health Care

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    BACKGROUND: Most medical treatments have been designed for the “average patients”. As a result of this “one-size-fits-all-approach”, treatments can be very successful for some patients but not for others. The issue is shifting by the new innovation approach in diseases treatment and prevention, precision medicine, which takes into account individual differences in people\u27s genes, environments, and lifestyles. This review was aimed to describe a new approach of healthcare performance strategy based on individual genetic variants.CONTENT: Researchers have discovered hundreds of genes that harbor variations contributing to human illness, identified genetic variability in patients\u27 responses to different of treatments, and from there begun to target the genes as molecular causes of diseases. In addition, scientists are developing and using diagnostic tests based on genetics or other molecular mechanisms to better predict patients\u27 responses to targeted therapy.SUMMARY: Personalized medicine seeks to use advances in knowledge about genetic factors and biological mechanisms of disease coupled with unique considerations of an individual\u27s patient care needs to make health care more safe and effective. As a result of these contributions to improvement in the quality of care, personalized medicine represents a key strategy of healthcare reform

    The Search

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    https://mouseion.jax.org/search/1020/thumbnail.jp

    Concept of the Munich/Augsburg Consortium Precision in Mental Health for the German Center of Mental Health

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    The Federal Ministry of Education and Research (BMBF) issued a call for a new nationwide research network on mental disorders, the German Center of Mental Health (DZPG). The Munich/Augsburg consortium was selected to participate as one of six partner sites with its concept “Precision in Mental Health (PriMe): Understanding, predicting, and preventing chronicity.” PriMe bundles interdisciplinary research from the Ludwig-Maximilians-University (LMU), Technical University of Munich (TUM), University of Augsburg (UniA), Helmholtz Center Munich (HMGU), and Max Planck Institute of Psychiatry (MPIP) and has a focus on schizophrenia (SZ), bipolar disorder (BPD), and major depressive disorder (MDD). PriMe takes a longitudinal perspective on these three disorders from the at-risk stage to the first-episode, relapsing, and chronic stages. These disorders pose a major health burden because in up to 50% of patients they cause untreatable residual symptoms, which lead to early social and vocational disability, comorbidities, and excess mortality. PriMe aims at reducing mortality on different levels, e.g., reducing death by psychiatric and somatic comorbidities, and will approach this goal by addressing interdisciplinary and cross-sector approaches across the lifespan. PriMe aims to add a precision medicine framework to the DZPG that will propel deeper understanding, more accurate prediction, and personalized prevention to prevent disease chronicity and mortality across mental illnesses. This framework is structured along the translational chain and will be used by PriMe to innovate the preventive and therapeutic management of SZ, BPD, and MDD from rural to urban areas and from patients in early disease stages to patients with long-term disease courses. Research will build on platforms that include one on model systems, one on the identification and validation of predictive markers, one on the development of novel multimodal treatments, one on the regulation and strengthening of the uptake and dissemination of personalized treatments, and finally one on testing of the clinical effectiveness, utility, and scalability of such personalized treatments. In accordance with the translational chain, PriMe’s expertise includes the ability to integrate understanding of bio-behavioral processes based on innovative models, to translate this knowledge into clinical practice and to promote user participation in mental health research and care

    Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach

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    BACKGROUND: Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications. METHODS: Based on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier. RESULTS: The definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating curves (AUC), with an AUC of 0.94 for the T0 classifier, 0.84 for T1/T2, and 0.92 for T3/T4. CONCLUSIONS: The combination of definitions agreed upon by five CTSA hubs, a checklist that facilitates more uniform definition interpretation, and algorithms that perform well in classifying publications along the translational spectrum provide a basis for establishing and applying uniform definitions of translational research categories. The classification algorithms allow publication analyses that would not be feasible with manual classification, such as assessing the distribution and trends of publications across the CTSA network and comparing the categories of publications and their citations to assess knowledge transfer across the translational research spectrum
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