TechCrunch
DECEMBER 15, 2021
Prellis Biologics , a company developing the tools to create 3D-printed organs, announced a $14.5 Up until this point, Prellis has focused on 3D printing vascular scaffolds that would allow companies to grow healthy and oxygenated human organs (or miniature versions, called organoids) for drug testing, and eventually transplant.
OTS Solutions
JUNE 3, 2022
Modern technical advancements in healthcare have made it possible to quickly handle critical medical data, medical records, pharmaceutical orders, and other data. In addition, pharmaceutical businesses can generate more effective drugs and improve medical research and experimentation using machine learning.
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CableLabs
NOVEMBER 30, 2021
To raise awareness about the 10G network , we’ve joined forces with outstanding organizations like Corning to get innovators thinking about how to build technologies that will work on the network of the future with the 10G Challenge. As an organization, Corning’s growth is fueled by a commitment to innovation.
CableLabs
NOVEMBER 30, 2021
To raise awareness about the 10G network , we’ve joined forces with outstanding organizations like Corning to get innovators thinking about how to build technologies that will work on the network of the future with the 10G Challenge. As an organization, Corning’s growth is fueled by a commitment to innovation.
Modus Create
MARCH 26, 2020
Universities and pharmaceutical companies are racing to speed up COVID-19 testing, others are partnering together to create a vaccine , while the World Health Organization launches “mega trials” for existing drugs to treat the disease. ” – UW Institute for Protein Design , February 24, 2020.
Trigent
APRIL 27, 2022
Artificial Intelligence is now part of the everyday workflows of top healthcare organizations. No wonder AI spending in the healthcare and pharmaceutical industries is predicted to surge. It provides predictive algorithms, and organizations are quick to acknowledge its benefits. Precision medicine.
InfoBest
SEPTEMBER 19, 2021
When an organization decides to use machine learning it needs to think about the value the prediction will bring and if it will offer it an edge over the competition. Organizations must be prepared to invest in resources and specialized infrastructure if they want to scale out their machine learning development pilot projects.
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