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COVID-19 age-dependent immunology and clinical outcomes: implications for vaccines
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- Azza Sarfraz, Saman Hasan Siddiqui, Junaid Iqbal, Syed Asad Ali, Zahra Hasan, Zouina Sarfraz, Najeeha Talat Iqbal
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- Journal:
- Journal of Developmental Origins of Health and Disease / Volume 13 / Issue 3 / June 2022
- Published online by Cambridge University Press:
- 21 July 2021, pp. 277-283
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Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leading to acute respiratory distress syndrome (ARDS). Understanding the evolution of the virus, and immune-pathogenic processes are critical for designing future therapeutic interventions. In this review, we collate information on the structure, genome, viral life cycle, and adult and pediatric host immune responses in response to SARS-CoV-2. The immunological responses are a prototype of the developmental origins of health and disease (DOHaD) hypothesis to explain the socio-geographic differences impacting the severity and mortality rates in SARS-CoV-2 infections. The DOHaD hypothesis identifies the relevance of trained innate immunity, age groups, and geography for effective vaccinations. As COVID-19 vaccines are being rolled out, it may be pertinent to assess population-based immunological responses to understand the effectiveness and safety across different populations and age groups.
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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- Article
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- Open access
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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.