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Inspiring diverse researchers in Virginia: Cultivating research excellence through a career-building program
- Lina V. Mata-McMurry, Jennifer V. Phillips, Sandra G. Burks, Adam Greene, Sana Syed, Karen C. Johnston
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- Journal:
- Journal of Clinical and Translational Science / Volume 8 / Issue 1 / 2024
- Published online by Cambridge University Press:
- 22 January 2024, e27
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Historically underrepresented groups in biomedical research have continued to experience low representation despite shifting demographics. Diversity fosters inclusive, higher quality, and innovative team science. One avenue for diversifying research teams is integrating diversity-focused initiatives into Clinical and Translational Science Award (CTSA) Programs, such as the integrated Translational Health Research Institute of Virginia (iTHRIV). In 2020, iTHRIV participated in Building Up, developed by the University of Pittsburgh CTSA, and intended to increase representation and improve career support for underrepresented groups in the biomedical workforce. Drawing lessons from this study, iTHRIV implemented the “inspiring Diverse Researchers in Virginia” (iDRIV) program. This yearlong program provided education, coaching, mentoring, and sponsorship for underrepresented early career investigators in the biomedical workforce. To date, 24 participants have participated in the program across three cohorts. Participants have been predominantly female (92%), with 33% identifying as Hispanic/Latinx, 29% as Black, and 13% as Asian. Notably, 38% of scholars have subsequently achieved at least one accomplishment, such as receiving a local research honor or award and an extramural funding award from a foundation or federal agency. The iTHRIV iDRIV program serves as a model for providing career support to developing investigators from underrepresented backgrounds, with the overall goal of improving patient health.
3165 Diseased and Healthy Gastrointestinal Tissue Data Mining requires an Engaged Transdisciplinary team
- Sana Syed, Marium Naveed Khan, Alexis Catalano, Zambia Team, Pakistan Team, Christopher Moskaluk, Jason Papin, S. Asad Ali, Sean R. Moore, Donald E. Brown
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- Journal:
- Journal of Clinical and Translational Science / Volume 3 / Issue s1 / March 2019
- Published online by Cambridge University Press:
- 26 March 2019, pp. 131-132
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OBJECTIVES/SPECIFIC AIMS: To establish an effective team of researchers working towards developing and validating prognostic models employing use of image analyses and other numerical metadata to better understand pediatric undernutrition, and to learn how different approaches can be brought together collaboratively and efficiently. METHODS/STUDY POPULATION: Over the past 18 months we have established a transdisciplinary team spanning three countries and the Schools of Medicine, Engineering, Data Science and Global Health. We first identified two team leaders specifically a pediatric physician scientist (SS) and a data scientist/engineer (DB). The leaders worked together to recruit team members, with the understanding that different ideas are encouraged and will be used collaboratively to tackle the problem of pediatric undernutrition. The final data analytic and interpretative core team consisted of four data science students, two PhD students, an undergraduate biology major, a recent medical graduate, and a PhD research scientist. Additional collaborative members included faculty from Biomedical Engineering, the School of Medicine (Pediatrics and Pathology) along with international Global Health faculty from Pakistan and Zambia. We learned early on that it was important to understand what each of the member’s motivation for contributing to the project was along with aligning that motivation with the overall goals of the team. This made us help prioritize team member tasks and streamline ideas. We also incorporated a mechanism of weekly (monthly/bimonthly for global partners) meetings with informal oral presentations which consisted of each member’s current progress, thoughts and concerns, and next experimental goals. This method enabled team leaders to have a 3600 mechanism of feedback. Overall, we assessed the effectiveness of our team by two mechanisms: 1) ongoing team member feedback, including team leaders, and 2) progress of the research project. RESULTS/ANTICIPATED RESULTS: Our feedback has shown that on initial development of the team there was hesitance in communication due to the background diversity of our various member along with different cultural/social expectations. We used ice-breaking methods such as dedicated time for brief introductions, career directions, and life goals for each team member. We subsequently found that with the exception of one, all other team members noted our working environment professional and conducive to productivity. We also learnt from our method of ongoing constant feedback that at times, due to the complexity of different disciplines, some information was lost due to the difference in educational backgrounds. We have now employed new methods to relay information more effectively, with the use of not just sharing literature but also by explaining the content. The progress of our research project has varied over the past 4-6 months. There was a steep learning curve for almost every member, for example all the data science students had never studied anything related to medicine during their education, including minimal if none exposure to the ethics of medical research. Conversely, team members with medical/biology backgrounds had minimal prior exposure to computational modeling, computer engineering and the verbage of communicating mathematical algorithms. While this may have slowed our progress we learned that by asking questions and engaging every member it was easier to delegate tasks effectively. Once our team reached an overall understanding of each member’s goals there was a steady progress in the project, with new results and new methods of analysis being tested every week. DISCUSSION/SIGNIFICANCE OF IMPACT: We expect that our on-going collaboration will result in the development of new and novel modalities to understand and diagnose pediatric undernutrition, and can be used as a model to tackle several other problems. As with many team science projects, credit and authorship are challenges that we are outlining creative strategies for as suggested by International Committee of Medical Journal Editors (ICMJE) and other literature.
Rogues' Gallery of Contributing Authors
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- By Ramon Abola, Rishimani Adsumelli, Syed Azim, Tazeen Beg, Helene Benveniste, Louis Chun, Ramtin Cohanim, Dominick Coleman, Joseph Conrad, Tommy Corrado, Jason Daras, Michelle DiGuglielmo, Vedan Djesevic, Andrew Drollinger, Kathleen Dubrow, Brian Durkin, Ralph Epstein, Christopher J. Gallagher, Xiaojun Guo, Sofie Hussain, Ron Jasiewicz, Anna Kogan, Ursula Landman, Rany Makaryus, Daryn Moller, Tate Montgomery, Matthew Neal, Khoa Nguyen, Marco Palmieri, Shaji Poovathor, Eric Posner, Deborah Richman, Andrew Rozbruch, Misako Sakamaki, Joy Schabel, Bharathi Scott, Peggy Seidman, Shiena Sharma, Vishal Sharma, Ellen Steinberg, Neera Tewari, Jane Yi, Jonida Zeqo, Peter Chung, John Denny, Steven H. Ginsberg, Jeremy Grayson, Jonathan Kraidin, Stephen Lemke, Tejal Patel, Salvatore Zisa, Charles Cowles, Marc Rozner, Shawn Banks, Deborah Brauer, Lebron Cooper, V. Samepathi David, Steve Gayer, Steven Gil, Eric A. Harris, Murlikrishna Kannan, Michael C. Lewis, David A. Lindley, Carlos M. Mijares, Sana Nini, Shafeena Nurani, Sujatha Pentakota, Edgar Pierre, Amy Klash Pulido, Michael Rossi, Miguel Santos, Nancy Setzer-Saade, Adam Sewell, Omair H. Toor, Ashish Udeshi, Patricia Wawroski, Lauren C. Berkow, Dan Berkowitz, Ramola Bhambhani, Kerry K. Blaha, Veronica Busso, Adam J. Carinci, Paul J. Christo, R. Blaine Easley, Ralph J. Fuchs, Samuel M. Galvagno, Nishant Gandhi, Andrew Goins, Robert S. Greenberg, Sayeh Hamzehzadeh, Theresa L. Hartsell, Eugenie Heitmiller, Jeremy M. Huff, Brijen L. Joshi, Sapna Kudchadkar, Jennifer K. Lee, Ira Lehrer, Peter Lin, Justin Lockman, Christine L. Mai, Christina Miller, Nanhi Mitter, Gillian Newman, Daniel Nyhan, Lale Odekon, Rabi Panigrahi, Melissa Pant, Alexander Papangelou, Mark Rossberg, Adam Schiavi, Steven J. Schwartz, Deborah A. Schwengel, Brandon M. Togioka, Tina Tran, Emmett Whitaker, Bradford D. Winters, Christopher Wu, Elena J. Holak, Paul S. Pagel
- Edited by Christopher J. Gallagher, State University of New York, Stony Brook, Michael C. Lewis, University of Miami School of Medicine, Deborah A. Schwengel
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- Book:
- Core Clinical Competencies in Anesthesiology
- Published online:
- 06 July 2010
- Print publication:
- 12 April 2010, pp xi-xii
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Seasonal variations in the chemical composition of camel milk in Jordan
- Malik SY Haddadin, Sana I Gammoh, Richard K Robinson
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- Journal:
- Journal of Dairy Research / Volume 75 / Issue 1 / February 2008
- Published online by Cambridge University Press:
- 31 October 2007, pp. 8-12
- Print publication:
- February 2008
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The principal chemical components of milk from the Arabian camel (Camelus dromedarius) were monitored in Jordan over one year. The analyses included total solids, fat, protein, vitamins, minerals and organic acids. Large seasonal variations in total solids and fat were apparent, with maxima in mid-winter of 139 and 39·0 g/l, respectively, and minima in August of 102 and 25·0 g/l. These differences may be sufficient to alter the sensory properties of the milk, and the fat: casein ratio may need standardisation for cheesemaking. The mean values of trace elements like zinc (5·8 mg/l), iron (4·4 mg/l) and manganese (0·05 mg/l) in Jordanian camel milk could provide valuable additions to the diet of urban populations, as could the mean concentration of vitamin C (33 mg/l). The levels of organic acids were generally higher than in bovine milk and, as with all the constituents of the milk, there were discernible patterns linking concentration and season of the year.