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Ellen Barlow on Muck Rack

Ellen Barlow

As seen in: AIChE Journal, MDPI, BioRxiv, Brighton & Hove Independent
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Articles

Development and validation of a computational tool to predict treatment outcomes in cells from high‐grade serous ovarian cancer patients

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By Marilisa Cortesi, Dongli Liu, Elyse Powell, Ellen Barlow
| AIChE Journal Verified
Digital twins, virtual replicas of living and non living entities, are increasingly applied to treatment personalisation, as they can provide insights on the response of individual patients to a given therapy. This technology is particularly valuable when applied to diseases characterized by poor prognosis and limited markers of response, such as high grade serous ovarian cancer (HGSOC).
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Development and validation of a computational tool to predict treatment outcomes in cells from High-Grade Serous Ovarian Cancer patients

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By Marilisa Cortesi, Dongli Liu, Elyse Powell, Ellen Barlow
| BioRxiv
Abstract Treatment of High-Grade Serous Ovarian Cancer (HGSOC) is often ineffective due to frequent late-stage diagnosis and development of resistance to therapy. Timely selection of the most effective (combination of) drug(s) for each patient would improve outcomes, however the tools currently available to clinicians are poorly suited to the task. We here present a computational simulator capable of recapitulating cell response to treatment in ovarian cancer.
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Conservative Management of Vulvar Cancer-Where Should We Draw the Line?

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By Neville F. Hacker, Ellen Barlow
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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