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Intro

The Crick’s core science and technology platforms (STPs) are unrivalled. Led by world class technical experts, these facilities push forward the frontiers of biomedicine.

Experiments that took weeks or months can now be carried out in a matter of days or even seconds. Innovations in techniques and technologies can provide new ways to tackle challenging questions. It can unlock entirely new fields of research in which experiments were previously unfeasible.   

Philanthropy can ensure that the Crick remains at the forefront of technological innovation, in both our equipment and expertise.   

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Accessing world-class technical experts

The Crick is one of the very few places in the world to have live-cell electron microscopy workflows that combine live-cell imaging in high-risk infectious material with 3D electron microscopy. 

Together our researchers are uncovering how antibiotics work within cells, helping to develop a better approach to tuberculosis (TB) treatment.   

Lucy Collinson leads our electron microscopy facility and works closely with many labs throughout the Crick. Her collaboration with TB researcher Max Gutierrez has allowed them to tackle the complex nature of antibiotic resistance while simultaneously pushing the limits of the technology - making significant contributions in their respective fields.  

Harnessing machine learning to improve cancer treatment

Charlotte Spencer and Amy Strange
Charlotte Spencer and Amy Strange

The challenge of identifying high risk cancer patients requires an innovative and ambitious approach.  

Supported by Amy Strange, the Crick’s head of Software Engineering and Artificial Intelligence STP, Clinical Research Fellow Charlotte Spencer is using machine learning to identify features in kidney tumours that indicate whether it will be aggressive and require additional treatment. They are ultimately working towards personalised medicine where patients are given the therapy that best suits their cancer type.  

This dynamic partnership has been mutually beneficial to researchers and the machine learning engineers alike. Their cutting-edge approach has developed a blueprint for how this technology can be used to build a more accurate picture of cancer progression, while allowing other scientists to exploit these technological advances, accelerating progress in further areas of research. 

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