Critical Issues for the Endotype/Biomarkers Approach

Agache, Ioana; Cojanu, Cătălina; Rogozea, Liliana

doi:10.1016/b978-0-12-813471-9.00019-0

Cited by 1 publication

(3 citation statements)

References 25 publications

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“…In recent years, there has been increased traction in the research for host response‐based diagnostics for personalized treatment and precision medicine 4 . There has been a shift toward customized disease management through endotype‐driven diagnostics in which triage is initiated keeping in mind the dynamic biological variability due to genetic predisposition, treatment response, and triggered biological pathways 5–7 . Disease endotypes are characterized by immunological biomarkers associated with the complex biological pathways at play 6–8 .…”

Section: Introductionmentioning

confidence: 99%

“…4 There has been a shift toward customized disease management through endotype-driven diagnostics in which triage is initiated keeping in mind the dynamic biological variability due to genetic predisposition, treatment response, and triggered biological pathways. [5][6][7] Disease endotypes are characterized by immunological biomarkers associated with the complex biological pathways at play. [6][7][8] Leveraging this, we put forward a versatile, plug-n-probe modular workflow using traditional electrochemical biosensors to map the chronological spread of infection using urinary tract infection or UTI as the proof-of-concept infectious disease model.…”

mentioning

confidence: 99%

“…[5][6][7] Disease endotypes are characterized by immunological biomarkers associated with the complex biological pathways at play. [6][7][8] Leveraging this, we put forward a versatile, plug-n-probe modular workflow using traditional electrochemical biosensors to map the chronological spread of infection using urinary tract infection or UTI as the proof-of-concept infectious disease model. UTIs are among the most common bacterial infections and occur when certain bacterial species enter and multiply in the urinary tract.…”

mentioning

confidence: 99%

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

Ganguly

Ebrahimzadeh

Komarovsky

et al. 2022

Bioengineering & Transla Med

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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.

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Section: Introductionmentioning

confidence: 99%