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

Abstract

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-ofconcept 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.

Inflammatory markers for improved recurrent UTI diagnosis in postmenopausal women

“…(Jagannath et al, 2021). Validation of these or other urinary cytokines may facilitate the development of similar sensors into POC UTI diagnostic tools (Ganguly et al, 2023). One limitation of a cytokine-based UTI diagnosis is that it does not include antimicrobial susceptibility testing and thereby may not reduce the use of broad-spectrum antibiotics.…”

Machine Learning in Bioelectrocatalysis

“…In bioelectrocatalysis, PPV, TPR, and F1 score are often used to evaluate classification models. Ganguly et al [45] used multiplexed point of care (POC) biosensors to classify disease states based on severity, in which an RF model was used for digital classification. In the "infectious, systemic" state, PPV, TPR, and F1 score exhibited their highest values.…”