DigEST: Digital plug-n-probe disease Endotyping Sensor Technology

Antra Ganguly, Tahmineh Ebrahimzadeh, Jessica Komarovsky, Philippe E. Zimmern, Nicole J. De Nisco, Shalini Prasad

Research output: Contribution to journalArticlepeer-review


In this work, we propose a novel diagnostic workflow—DigEST—that will enable stratification of disease states based on severity using multiplexed point of care (POC) biosensors. This work can boost the performance of current POC tests by enabling clear, digestible, and actionable diagnoses to the end user. The scheme can be applied to any disease model, which requires time-critical disease stratification for personalized treatment. Here, urinary tract infection is explored as the proof-of-concept disease model and a four-class classification of disease severity is discussed. Our method is superior to traditional enzyme-linked immunosorbent assay (ELISA) as it is faster and can work with multiple disease biomarkers and categorize diseases by endotypes (or disease subtype) and severity. To map the nonlinear nature of biochemical pathways of complex diseases, the method utilizes an established supervised machine learning model for digital classification. This scheme can potentially boost the diagnostic power of current electrochemical biosensors for better precision therapy and improved patient outcomes.

Original languageEnglish (US)
JournalBioengineering and Translational Medicine
StateAccepted/In press - 2022


  • Boolean logic
  • digital biosensor
  • disease endotyping
  • electrochemical impedance spectroscopy
  • random-forest

ASJC Scopus subject areas

  • Biotechnology
  • Biomedical Engineering
  • Pharmaceutical Science


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