A rapid liquid biopsies-based diagnostic platform to detect neonatal infections
In his PhD project, Kevin Yim has developed a noninvasive diagnostic pipeline based on serum liquid biopsies analysis proven in COVID-19 related studies. In his fellowship, he will use these results to develop a diagnostic platform that can provide rapid and precise answers from very small samples. The new platforms hold great promise in the improvement of treatment decisions in neonatal infections.
High body temperature (fever) in newborn babies often indicates blood infections (e.g. E. Coli, Strep. B) that could lead to serious sepsis conditions. Identification of causing pathogens is crucial for clinicians to apply precise treatment as early as possible. Unfortunately, status quo detection relies on blood culture systems which are very invasive (2-5 mL whole blood), tedious (2-5 days turnover), and inaccurate (over 30% false-positive) leading to a high mortality rate (> 20%; 1.4 M/year globally) in modern standards. These undesirable elements lead to the usage of unnecessary and excessive antimicrobial treatment during infection and result in many serious and life-long side effects such as allergic conditions and antimicrobial resistance.
With the new platform, it will be possible to obtain pathogen-specific results within 2 hours. This will immensely improve the turnover of diagnostic procedures, the precision of treatment decisions, reduce the invasiveness in newborns, and ultimately lower the mortality rate of infected infants. In the next months, Kevin will establish a liquid biopsies markers cocktail specific for infections in neonates and optimize the analytical parameters in flow-based platforms. He aims to prove the accuracy and consistency of the platform in an upcoming retrospective study in collaboration with the biobank in Kinderspital Zurich to meet the clinical diagnostics standards. The dataset would allow the platform to screen for infections in neonates with fast turnover and very low input material (microlitre scale) to provide multiplex molecular information.
Affiliation: Prof. Dr. Richard Chahwan
Start date: 01/2022