With COVID-19 forcing many companies into undergoing a rapid and often premature digital transformation this can raise challenges for the business units. In the laboratory it is almost impossible to perform all work remotely and we have seen multiple strategies such as rotating shifts to avoid infection and those who can work from home do so. One of the main enabling factors is having the right laboratory informatics, LIMS, ELN and supporting infrastructure.
Colles Price, M.S., PhD is a Research Fellow in Medicine at Dana Farber Cancer Institute, Harvard Medical School, and a Postdoctoral Scholar, Cancer Program, Broad Institute. He is currently identifying and validating targets from the Cancer Dependency Map. In particular, he is interested in understanding the anti-cancer effects of hypoxia-related genes and how polar-related genes control cell proliferation and viability. We had the chance to sit down with him and learn about how COVID-19 is affecting his research.
As an industry that relies on having employees in the lab, biotech has been disrupted by COVID-19 and the dramatic and unexpected set of changes it has introduced. For many scientists, working remotely is a brand new concept. And yet, the scientific industry is also being called upon to develop tests, therapies, and vaccines that put this crisis to rest. To share how biotechs around the world are managing their projects, caring for their people, and supporting the community during coronavirus, we built a guide with best practices and unique ideas from our customers.
In this brief editorial in the journal Diagnostics, Mashamba-Thompson et al. state their case for a potentially ideal solution to COVID-19 testing in resource-poor communities: using mobile health (mHealth) solutions in conjunction with a blockchain and AI-driven data acquisition and transfer system. They add that the "AI component of this technology enables powerful data collection (patient information, geographic location of the patient, and test results), security, analysis, and curation of disparate and clinical data sets from federated blockchain platforms to derive triangulated data at very high degrees of confidence and speed. " Unfortunately, what isn't clear in their argument is how the self-testing step would actually work, let alone how the system would realistically be implemented in resource-poor settings. One could argue, however, despite the relatively few details, the sharing of ideas on how to address COVID-19 testing in various environments still has value.