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1. Biomedical entrepreneurship in U.S. regions

   https://doi.org/10.1007/s10961-023-09996-8  

   Park, Sang-Min; Vonortas, Nicholas S. (February 2023, The journal of technology transfer)

   Entrepreneurial ecosystem researchers generally focus on the few dominant factors affecting entrepreneurship. Insufficient attention has been paid to the interdependencies among regional conditions within an entrepreneurial ecosystem. We focus on the collective effects of factors for regional biomedical entrepreneurship. We use the fuzzy-set qualitative comparative analysis (fsQCA) method to identify sets of regional conditions promoting biomedical entrepreneurship in all 381 U.S. metropolitan areas. The results indicate three configurations contributing to high levels of regional biomedical entrepreneurship: the first one combines public sector biomedical R&D, biomedical patents, and human capital, thus stressing science conditions and related human capital; the second combines public sector biomedical R&D, biomedical patents, clinical trials, and venture capital, thus placing more emphasis on the regional infrastructure sustaining entrepreneurial activity; the third combines private sector biomedical R&D, biomedical patents, human capital, per capita income, population density, and venture capital, thus emphasizing the private sector’s role on boosting regional biomedical entrepreneurship. There is no single recipe for a region to increase its level of biomedical entrepreneurship. 

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2. The diversity of experimental organisms in biomedical research may be influenced by biomedical funding

   https://doi.org/10.1002/bies.201600258  

   Erick Peirson, B. R.; Kropp, Heather; Damerow, Julia; Laubichler, Manfred D. (March 2017, BioEssays)

   Contrary to concerns of some critics, we present evidence that biomedical research is not dominated by a small handful of model organisms. An exhaustive analysis of research literature suggests that the diversity of experimental organisms in biomedical research has increased substantially since 1975. There has been a longstanding worry that organism‐centric funding policies can lead to biases in experimental organism choice, and thus negatively impact the direction of research and the interpretation of results. Critics have argued that a focus on model organisms has unduly constrained the diversity of experimental organisms. The availability of large electronic databases of scientific literature, combined with interest in quantitative methods among philosophers of science, presents new opportunities for data‐driven investigations into organism choice in biomedical research. The diversity of organisms used in NIH‐funded research may be considerably lower than in the broader biomedical sciences, and may be subject to greater constraints on organism choice. 

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3. Network Analysis and Recommendation for Infectious Disease Clinical Trial Research

   Elkin, Magdalyn; Andrews, Whitney A.; Zhu, Xingquan (September 2019, Proc. of the 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB-2019))

   Clinical trials are crucial for the advancement of treatment and knowledge within the medical community. Since 2007, US federal government took the initiative and requires organizations sponsoring clinical trials with at least one site in the United States to submit information on these clinical trials to the ClinicalTrials.gov database, resulting in a rich source of information for clinical trial research. Nevertheless, only a handful of analytic studies have been carried out to understand this valuable data source. In this study, we propose to use network analysis to understand infectious disease clinical trial research. Our goal is to answer two important questions: (1) what are the concentrations and characteristics of infectious disease clinical trail research? and (2) how to accurately predict what type of clinical trials a sponsor (or an investigator) is interested in? The answers to the first question provide effective ways to summarize clinical trial research related to particular disease(s), and the answers to the second question help match clinical trial sponsors and investigators for information recommendation. By using 4,228 clinical trails as the test bed, our study involves 4,864 sponsors and 1,879 research areas characterized by Medical Subject Heading (MeSH) keywords. We extract a set of network measures to show patterns of infectious disease clinical trials, and design a new community based link prediction approach to predict sponsors' interests, with significant improvement compared to baselines. This trans-formative study concludes that using network analysis can tremendously help the understanding of clinical trial research for effective summarization, characterization, and prediction. 

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4. HEREDITARY ENDOCRINE TUMOURS: CURRENT STATE-OF-THE-ART AND RESEARCH OPPORTUNITIES: MEN1-related pancreatic NETs: identification of unmet clinical needs and future directives

   https://doi.org/10.1530/ERC-19-0441  

   Pieterman, C R; Sadowski, S M; Maxwell, J E; Katz, M H; Lines, K E; Heaphy, C M; Tirosh, A; Blau, J E; Perrier, N D; Lewis, M A; et al (August 2020, Endocrine-Related Cancer)

   The PanNET Working Group of the 16th International Multiple Endocrine Neoplasia Workshop (MEN2019) convened in Houston, TX, USA, 27–29 March 2019 to discuss key unmet clinical needs related to PanNET in the context of MEN1, with a special focus on non-functioning (nf)-PanNETs. The participants represented a broad range of medical scientists as well as representatives from patient organizations, pharmaceutical industry and research societies. In a case-based approach, participants addressed early detection, surveillance, prognostic factors and management of localized and advanced disease. For each topic, after a review of current evidence, key unmet clinical needs and future research directives to make meaningful progress for MEN1 patients with nf-PanNETs were identified. International multi-institutional collaboration is needed for adequately sized studies and validation of findings in independent datasets. Collaboration between basic, translational and clinical scientists is paramount to establishing a translational science approach. In addition, bringing clinicians, scientists and patients together improves the prioritization of research goals, assures a patient-centered approach and maximizes patient involvement. It was concluded that collaboration, research infrastructure, methodologic and reporting rigor are essential to any translational science effort. The highest priority for nf-PanNETs in MEN1 syndrome are (1) the development of a data and biospecimen collection architecture that is uniform across all MEN1 centers, (2) unified strategies for diagnosis and follow-up of incident and prevalent nf-PanNETs, (3) non-invasive detection of individual nf-PanNETs that have an increased risk of metastasis, (4) chemoprevention clinical trials driven by basic research studies and (5) therapeutic targets for advanced disease based on biologically plausible mechanisms. 

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5. The Pacific Innovations, Knowledge, and Opportunities (PIKO) Program: A Data Lifecycle Research Experience

   Chong, Rylan; Tipton, Laura (October 2023, Hawaii journal of health social welfare)

   Pacific evidence-based clinical and translational research is greatly needed. However, there are research challenges that stem from the creation, accessibility, availability, usability, and compliance of data in the Pacific. As a result, there is a growing demand for a complementary approach to the traditional Western research process in clinical and translational research. The data lifecycle is one such approach with a history of use in various other disciplines. It was designed as a data management tool with a set of activities that guide researchers and organizations on the creation, management, usage, and distribution of data. This manuscript describes the data lifecycle and its use by the Biostatistics, Epidemiology, and Research Design core data science team in support of the Center for Pacific Innovations, Knowledge, and Opportunities program. 

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