Optimising a microfluidic assay for sepsis diagnostics by combining numerical simulations with machine learning

Improving healthcare is one of the global challenges of our time. Sepsis (a life-threatening organ dysfunction caused by a dysregulated host immune response to infection) is linked to 20% of all deaths in the world. Diagnosing sepsis quickly is of utmost importance to the survival of a patient, as mortality from sepsis increases as much as 8% for every hour that treatment is delayed.

The emergence of microfluidics has enabled fascinating opportunities for disease diagnostics. The US-based company Cytovale (https://cytovale.com/) is developing microfluidic technologies for rapid (less than 10 min) sepsis diagnostics. Despite recent progress, there are several open questions that will be addressed in this PhD project:

How are properties of blood cells (e.g. viscoelasticity, size) linked to the observed physical cell behaviour in Cytovale's microfluidic devices for sepsis diagnostics?; How can machine learning and data mining be used to build a predictive model of the cell behaviour?; How can the microfluidic device be optimised for maximal diagnostic performance?

Beside the generation of new fundamental knowledge in microfluidics and cell mechanics, the ultimate outcome of the project is a software tool for the optimisation of microfluidic devices that probe mechanical properties of biological cells.