Nanotechnologies for biomedical science and translational medicine.
Ontology highlight
ABSTRACT:
SUBMITTER: Heath JR
PROVIDER: S-EPMC4664315 | biostudies-other | 2015 Nov
REPOSITORIES: biostudies-other
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Project description:This national survey aimed to identify how biomedical researchers using vertebrate animals viewed issues of significance for translational science, including oversight and public engagement, and to analyze how researcher characteristics and animal model choice correlate with those views. Responses from 1,187 researchers showed awareness of, and concerns about, problems of translation, reproducibility and rigor. Surveyed scientists were nevertheless optimistic about the value of animal studies, were favorable about research oversight and reported openness with non-scientists in discussing their animal work. Differences in survey responses among researchers also point to diverse perspectives within the animal research community on these matters. Most significant was variability associated with the primary type of animal that surveyed scientists used in their work. Other significant divergence in opinion appeared on the basis of professional role factors, including the type of degree held, workplace setting, type of funding, experience on an institutional animal care and use committee and personal demographic characteristics of age and gender.
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Project description:Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.
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Project description:Hodgkin lymphoma (HL) is a lymphoid malignancy that is typically derived from germinal-center B cells. EBV infection, mutations in NF-κB pathway genes, and genetic susceptibility are known risk factors for developing HL. CD30 and NF-κB have been identified as potential biomarkers in pediatric HL patients, and these molecules may represent therapeutic targets. Although current risk adapted and response based treatment approaches yield overall survival rates of >95%, treatment of relapse or refractory patients remains challenging. Targeted HL therapy with the antibody-drug conjugate Brentuximab vedotin (Bv) has proven to be superior to conventional salvage chemotherapy and clinical trials are being conducted to incorporate Bv into frontline therapy that substitutes Bv for alkylating agents to minimize secondary malignancies. The appearance of secondary malignancies has been a concern in pediatric HL, as these patients are at highest risk among all childhood cancer survivors. The risk of developing secondary leukemia following childhood HL treatment is 10.4 to 174.8 times greater than the risk in the general pediatric population and the prognosis is significantly poorer than the other hematological malignancies with a mortality rate of nearly 100%. Therefore, identifying clinically valuable biomarkers is of utmost importance to stratify and select patients who may or may not need intensive regimens to maintain optimal balance between maximal survival rates and averting late effects. Here we discuss epidemiology, risk factors, staging, molecular and genetic prognostic biomarkers, treatment for low and high-risk patients, and the late occurrence of secondary malignancies in pediatric HL.
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Project description:BackgroundThe Clinical and Translational Science Award (CTSA) program aims to strengthen and support translational research by accelerating the process of translating laboratory discoveries into treatments for patients, training a new generation of clinical and translational researchers, and engaging communities in clinical research efforts. Yet, little is known about how emergency care researchers have interacted with and utilized the resources of academic institutions with CTSAs.ObjectivesThe purpose of this survey was to describe how emergency care researchers use local CTSA resources, to ascertain what proportion of CTSA consortium members have active emergency care research (ECR) programs, and to solicit participation in a national CTSA-associated emergency care translational research network.MethodsThis study was a survey of all emergency departments affiliated with a CTSA.ResultsOf the 65 CTSA consortium members, three had no ECR program and we obtained responses from 46 of the remaining 62 (74% response rate). The interactions with and resources used by emergency care researchers varied widely. Methodology and biostatistics support was most frequently accessed (77%), followed closely by education and training programs (60%). Several ECR programs (76%) had submitted for funding through CTSAs, with 71% receiving awards. Most CTSA consortium members had an active ECR infrastructure: 21 (46%) had 24/7 availability to recruit and screen for research, and 21 (46%) had less than 24/7 research recruitment. A number of emergency care research programs participated in National Institutes of Health research networks with the Neurological Emergencies Treatment Trials network most highly represented with 23 (59%) sites. Most ECR programs (96%) were interested in participating in a CTSA-based emergency care translational research network.ConclusionsDespite little initial involvement in development of the CTSA program, there has been moderate interaction between CTSAs and emergency care. There is considerable interest in participating in a CTSA consortium-based emergency care translational research network.
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Project description:Medical students enter clerkships with the requisite biomedical science knowledge to engage in supervised patient care. While poised to apply this knowledge, students face the cognitive challenge of transfer: applying knowledge learned in one context (i.e., preclinical classroom) to solve problems in a different context (i.e., patients in the clinic). To help students navigate this challenge, a structured reflection exercise was developed using Kolb’s experiential learning cycle as an organizing framework. Students selected a patient encounter (concrete experience), wrote and addressed biomedical science learning objectives related to the care of the patient (reflective observation), reflected on how addressing the learning objectives influenced patient care (abstract conceptualization), and described their attending engaging in a similar process (active experimentation). A directed content analysis of students’ written reflections revealed that most students wrote clinical science learning objectives in addition to biomedical science learning objectives. When viewed through the lenses of knowledge encapsulation theory and illness script theory, some students recognized knowledge encapsulation as a process beginning to occur in their own approach and their attendings’ approach to clinical reasoning. Students readily applied their biomedical science knowledge to explain the pathophysiologic basis of disease (fault illness script domain) and signs and symptoms (consequence illness script domain), with fewer addressing predisposing conditions (enabling conditions illness script domain). Instances in which students observed their attending applying biomedical science knowledge were rare. Implications for using structured reflective writing as a tool to facilitate student application of their biomedical science knowledge in clerkships are discussed. Supplementary Information The online version contains supplementary material available at 10.1007/s40670-022-01697-5.
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Project description:BackgroundTranslational research is a process of applying knowledge from basic biology and clinical trials to techniques and tools that address critical medical needs. Translational research is less explored in the Ugandan health system, yet, it is fundamental in enhancing human health and well-being. With the current high disease burden in Uganda, there are many opportunities for exploring, developing and utilising translational research.Main bodyIn this article, we described the current state, barriers and opportunities for translational research in Uganda. We noted that translational research is underutilised and hindered by limited funding, collaborations, laboratory infrastructure, trained personnel, equipment and research diversity. However, with active collaborations and funding, it is possible to set up and develop thriving translational research in Uganda. Researchers need to leverage existing international collaborations to enhance translational research capacity development.ConclusionExpanding the integration of clinical and translational research in Uganda health care system will improve clinical care.
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Project description:This article describes efforts at the National Institutes of Health (NIH) from 2013 to 2016 to train a national workforce in biomedical data science. We provide an analysis of the Big Data to Knowledge (BD2K) training program strengths and weaknesses with an eye toward future directions aimed at any funder and potential funding recipient worldwide. The focus is on extramurally funded programs that have a national or international impact rather than the training of NIH staff, which was addressed by the NIH's internal Data Science Workforce Development Center. From its inception, the major goal of BD2K was to narrow the gap between needed and existing biomedical data science skills. As biomedical research increasingly relies on computational, mathematical, and statistical thinking, supporting the training and education of the workforce of tomorrow requires new emphases on analytical skills. From 2013 to 2016, BD2K jump-started training in this area for all levels, from graduate students to senior researchers.
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