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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-03-01
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| Series: | Bioengineering & Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/btm2.10437 |
| _version_ | 1797870974528389120 |
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| author | Antra Ganguly Tahmineh Ebrahimzadeh Jessica Komarovsky Philippe E. Zimmern Nicole J. De Nisco Shalini Prasad |
| author_facet | Antra Ganguly Tahmineh Ebrahimzadeh Jessica Komarovsky Philippe E. Zimmern Nicole J. De Nisco Shalini Prasad |
| author_sort | Antra Ganguly |
| collection | DOAJ |
| description | 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‐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. |
| first_indexed | 2024-04-10T00:36:56Z |
| format | Article |
| id | doaj.art-f088a04b03aa44a6bf64320989d3cdc3 |
| institution | Directory Open Access Journal |
| issn | 2380-6761 |
| language | English |
| last_indexed | 2024-04-10T00:36:56Z |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Bioengineering & Translational Medicine |
| spelling | doaj.art-f088a04b03aa44a6bf64320989d3cdc32023-03-14T16:53:48ZengWileyBioengineering & Translational Medicine2380-67612023-03-0182n/an/a10.1002/btm2.10437DigEST: Digital plug‐n‐probe disease Endotyping Sensor TechnologyAntra Ganguly0Tahmineh Ebrahimzadeh1Jessica Komarovsky2Philippe E. Zimmern3Nicole J. De Nisco4Shalini Prasad5Department of Bioengineering University of Texas at Dallas Richardson Texas USADepartment of Biological Sciences University of Texas at Dallas Richardson Texas USADepartment of Biological Sciences University of Texas at Dallas Richardson Texas USADepartment of Urology University of Texas Southwestern Medical Center Dallas Texas USADepartment of Biological Sciences University of Texas at Dallas Richardson Texas USADepartment of Bioengineering University of Texas at Dallas Richardson Texas USAAbstract 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.https://doi.org/10.1002/btm2.10437Boolean logicdigital biosensordisease endotypingelectrochemical impedance spectroscopyrandom‐forest |
| spellingShingle | Antra Ganguly Tahmineh Ebrahimzadeh Jessica Komarovsky Philippe E. Zimmern Nicole J. De Nisco Shalini Prasad DigEST: Digital plug‐n‐probe disease Endotyping Sensor Technology Bioengineering & Translational Medicine Boolean logic digital biosensor disease endotyping electrochemical impedance spectroscopy random‐forest |
| title | DigEST: Digital plug‐n‐probe disease Endotyping Sensor Technology |
| title_full | DigEST: Digital plug‐n‐probe disease Endotyping Sensor Technology |
| title_fullStr | DigEST: Digital plug‐n‐probe disease Endotyping Sensor Technology |
| title_full_unstemmed | DigEST: Digital plug‐n‐probe disease Endotyping Sensor Technology |
| title_short | DigEST: Digital plug‐n‐probe disease Endotyping Sensor Technology |
| title_sort | digest digital plug n probe disease endotyping sensor technology |
| topic | Boolean logic digital biosensor disease endotyping electrochemical impedance spectroscopy random‐forest |
| url | https://doi.org/10.1002/btm2.10437 |
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