Elizabeth Cherry on Muck Rack

Elizabeth Cherry

  • Associate Professor, Sociology and Anthropology, Manhattanville College and Contributor, Freelance
New York
As seen in: Memory Studies, PLOS, Science Magazine, Cell Press, AIP Publishing, Pharmacy Times, Washington Independent Review of Books
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Articles

Exploring healthcare providers’ experiences with specialty medication and limited distribution networks

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By Megan Peter, Autumn D. Zuckerman, Elizabeth Cherry, David G Schlundt
| PLOS
Communication with insurance providers. For most patients with prescription drug insurance, clinic staff communicate with the patient’s insurance provider to approve coverage of medication. As demonstrated in the following exchanges, paperwork required by insurance providers can be cumbersome and, when not completed correctly, initiate back-and-forth between the clinic and insurance provider: Another participant expressed challenges with communicating with insurance providers via phone.
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A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition

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By Mario Mendez, Mario Méndez Verified, Virginia Commonwealth, Matthew Hoffman, Elizabeth Cherry
| Cell Press Verified
Epithelial-mesenchymal transition. J. Cell Sci. 2005; 118: 4325-4326 Thiery J.P. Acloque H. Nieto M.A. et al. Epithelial-mesenchymal transitions in development and disease. Cell. 2009; 139: 871-890 Brabletz T. Kalluri R. Weinberg R.A. et al. EMT in cancer. Nat. Rev. Cancer. 2018; 18: 128-134 Epithelial-mesenchymal transition and its implications for fibrosis. J. Clin. Invest. 2003; 112: 1776-1784 Miettinen P.J. Ebner R. Derynck R. et al.
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A machine-learning approach for long-term prediction of experimental cardiac action potential time series using an autoencoder and echo state networks

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By Flavio H. Fenton, Elizabeth Cherry, Shahrokh Shahi
| AIP Publishing
Computational modeling and experimental/clinical prediction of the complex signals during cardiac arrhythmias have the potential to lead to new approaches for prevention and treatment. Machine-learning (ML) and deep-learning approaches can be used for time-series forecasting and have recently been applied to cardiac electrophysiology.
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