Postdoctoral Excellence Programme (PEP)

Patch-IT: Multi-Sensor Sensor Nodes for Continuous Vital Sign Monitoring to Identify Novel Digital Biomarkers for Sepsis Detection in Neonatal Intensive Care
Neonatal sepsis has a high incidence globally and is a major cause of mortality worldwide. The project Patch-IT proposes a solution for improved neonatal sepsis monitoring and management. The team aims to develop a multi-sensor electronic epidermal system that incorporates wire-free, battery-free, non-invasive and autonomous monitoring of multiple vital signs continuously and in real time. The device will also employ in-sensor data analytics powered by state-of-the-art sensor-fusion algorithms to enable personalised patient monitoring. PEP Fellow Kanika Dheman (currently Department of Mechanical and Process Engineering, ETHZ) will join the lab of Dr Michele Magno (Department of Information Technology and Electrical Engineering, ETHZ).

Developing Novel Drug Strategies for the Treatment of Fragile X by Functional Screening of Human Pluripotent Stem Cell Models
Fragile X Syndrome (FXS) is the most common inherited cause of intellectual disability. FXS occurs due to epigenetic silencing, or non-expression, of a specific gene, FMR1. The goal of this project is to identify and test new drugs using human stem cells with the ability to induce re-expression of FMR1 and reverse FXS symptoms and effects. This proposal has 3 aims: 1. Establish baseline levels of FMR1 expression and downstream targets in normal and FXS stem cells 2. Screen novel categories of drug compounds and 3. Establish organoids, a type of tissue culture, from FXS stem cells to perform anatomical validation of drug efficacy. A PEP Fellow will conduct this work in the lab of Prof Verdon Taylor (Department of Biomedicine, University of Basel). The consortium also involves international collaboration with Prof Nissim Benvenisty (Department of Genetics, The Hebrew University of Jerusalem, Israel).

Bioinspired, Low-Cost Device for Minimally Invasive Blood Sampling
More than 70% of medical decisions depend on laboratory results and blood sampling is the most prevalent route for disease diagnosis and monitoring. The researchers propose to develop a versatile microsampling device for the collection of blood with minimal invasiveness, low manufacturing costs and sufficient volume retrieval for point-of-care tests or laboratory analysis. This device may be particularly suited for children where traditional blood draws using needles can cause distress. A prototype will be manufactured by 3D printing and validated pre-clinically ex vivo, in vivo and in combination with a commercially available point-of-care test for the detection of malaria. PEP Fellow Dr Nicole Zoratto (currently Department of Chemistry and Pharmaceutical Technologies, Sapienza University of Rome, Italy) will join the lab of Prof Jean-Christophe Leroux (Department of Chemistry and Applied Biosciences, ETHZ).

Electronic Clinical Decision Support and Machine Learning to Improve Care Quality and Clinical Outcomes of Sick Young Infants in Low-Resource Settings
Almost half of all deaths in children in the first five years of life occur in the neonatal and early infant period. Electronic Clinical Decision Support Algorithms (eCDSAs) can help guide health workers in appropriate and evidence-based patient evaluation and management, and have demonstrated benefit in improving clinical care for children. However, no such tool has been validated or tested for managing sick young infants in outpatient care settings in low- and middle-income countries (LMICs). The researchers will evaluate the effects of an eCDSA for neonates and young infants on the quality of care delivered and clinical outcomes among young infants in five LMICs. The investigators aim to enhance the prognostic and diagnostic performance of the algorithm using machine learning methods. PEP Fellow Dr Gillian Levine (currently Department of Epidemiology and Public Health, Household Economics and Health Systems Research Unit, Swiss TPH) will join the research group of Dr Tracy Glass (Department of Medicine, Swiss TPH).

Harnessing Machine Learning and Mechanistic Modelling for Personalised Radiotherapy of Paediatric Diffuse Midline Glioma
Diffuse midline glioma, a primary tumor within the most sensitive part of the brain, is a fatal disease primarily affecting children between 4-7 years of age. The project proposes to develop a digital health tool to guide doctors in designing optimal treatment strategies for affected children and their families. The overarching aim of this project is to build a treatment decision support platform facilitating personalised radiotherapy (RT) optimisation based on MRI for afflicted paediatric patients. The researchers will develop an analytical pipeline bridging mechanistic modelling and data-driven machine learning to refine patient stratification, discover imaging biomarkers, and inform RT scheduling and dosing by an individualised radiosensitivity score. PEP Fellow Dr Sarah Brüningk (currently Department of Biosystems Science and Engineering, ETHZ) will work with Prof Catherine Jutzeler (Department of Health Sciences and Technology, ETHZ), in collaboration with Prof Karsten Borgwardt (Max Planck Institute for Biochemistry) and Prof Javad Nazarian (DMG Research Center, University Children’s Hospital Zurich).