Literature DB >> 27380817

Programmable Bio-nanochip Platform: A Point-of-Care Biosensor System with the Capacity To Learn.

Michael P McRae1, Glennon Simmons2, Jorge Wong1,3, John T McDevitt1,2,3.   

Abstract

The combination of point-of-care (POC) medical microdevices and machine learning has the potential transform the practice of medicine. In this area, scalable lab-on-a-chip (LOC) devices have many advantages over standard laboratory methods, including faster analysis, reduced cost, lower power consumption, and higher levels of integration and automation. Despite significant advances in LOC technologies over the years, several remaining obstacles are preventing clinical implementation and market penetration of these novel medical microdevices. Similarly, while machine learning has seen explosive growth in recent years and promises to shift the practice of medicine toward data-intensive and evidence-based decision making, its uptake has been hindered due to the lack of integration between clinical measurements and disease determinations. In this Account, we describe recent developments in the programmable bio-nanochip (p-BNC) system, a biosensor platform with the capacity for learning. The p-BNC is a "platform to digitize biology" in which small quantities of patient sample generate immunofluorescent signal on agarose bead sensors that is optically extracted and converted to antigen concentrations. The platform comprises disposable microfluidic cartridges, a portable analyzer, automated data analysis software, and intuitive mobile health interfaces. The single-use cartridges are fully integrated, self-contained microfluidic devices containing aqueous buffers conveniently embedded for POC use. A novel fluid delivery method was developed to provide accurate and repeatable flow rates via actuation of the cartridge's blister packs. A portable analyzer instrument was designed to integrate fluid delivery, optical detection, image analysis, and user interface, representing a universal system for acquiring, processing, and managing clinical data while overcoming many of the challenges facing the widespread clinical adoption of LOC technologies. We demonstrate the p-BNC's flexibility through the completion of multiplex assays within the single-use disposable cartridges for three clinical applications: prostate cancer, ovarian cancer, and acute myocardial infarction. Toward the goal of creating "sensors that learn", we have developed and describe here the Cardiac ScoreCard, a clinical decision support system for a spectrum of cardiovascular disease. The Cardiac ScoreCard approach comprises a comprehensive biomarker panel and risk factor information in a predictive model capable of assessing early risk and late-stage disease progression for heart attack and heart failure patients. These marker-driven tests have the potential to radically reduce costs, decrease wait times, and introduce new options for patients needing regular health monitoring. Further, these efforts demonstrate the clinical utility of fusing data from information-rich biomarkers and the Internet of Things (IoT) using predictive analytics to generate single-index assessments for wellness/illness status. By promoting disease prevention and personalized wellness management, tools of this nature have the potential to improve health care exponentially.

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Year:  2016        PMID: 27380817      PMCID: PMC6504240          DOI: 10.1021/acs.accounts.6b00112

Source DB:  PubMed          Journal:  Acc Chem Res        ISSN: 0001-4842            Impact factor:   22.384


  17 in total

1.  Rapid single-molecule digital detection of protein biomarkers for continuous monitoring of systemic immune disorders.

Authors:  Yujing Song; Erin Sandford; Yuzi Tian; Qingtian Yin; Andrew G Kozminski; Shiuan-Haur Su; Tao Cai; Yuxuan Ye; Meng Ting Chung; Ryan Lindstrom; Annika Goicochea; Jenny Barabas; Mary Olesnavich; Michelle Rozwadowski; Yongqing Li; Hasan B Alam; Benjamin H Singer; Monalisa Ghosh; Sung Won Choi; Muneesh Tewari; Katsuo Kurabayashi
Journal:  Blood       Date:  2021-03-25       Impact factor: 22.113

2.  Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors.

Authors:  Zachary S Ballard; Hyou-Arm Joung; Artem Goncharov; Jesse Liang; Karina Nugroho; Dino Di Carlo; Omai B Garner; Aydogan Ozcan
Journal:  NPJ Digit Med       Date:  2020-05-07

Review 3.  Biomolecular engineering for nanobio/bionanotechnology.

Authors:  Teruyuki Nagamune
Journal:  Nano Converg       Date:  2017-04-24

4.  Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19.

Authors:  Michael P McRae; Glennon W Simmons; Nicolaos J Christodoulides; Zhibing Lu; Stella K Kang; David Fenyo; Timothy Alcorn; Isaac P Dapkins; Iman Sharif; Deniz Vurmaz; Sayli S Modak; Kritika Srinivasan; Shruti Warhadpande; Ravi Shrivastav; John T McDevitt
Journal:  Lab Chip       Date:  2020-06-03       Impact factor: 6.799

Review 5.  Point-of-care diagnostics for infectious diseases: From methods to devices.

Authors:  Chao Wang; Mei Liu; Zhifei Wang; Song Li; Yan Deng; Nongyue He
Journal:  Nano Today       Date:  2021-02-06       Impact factor: 20.722

Review 6.  The future of early cancer detection.

Authors:  Rebecca C Fitzgerald; Antonis C Antoniou; Ljiljana Fruk; Nitzan Rosenfeld
Journal:  Nat Med       Date:  2022-04-19       Impact factor: 87.241

Review 7.  Point-of-Care Diagnostics: Recent Developments in a Connected Age.

Authors:  Samiksha Nayak; Nicole R Blumenfeld; Tassaneewan Laksanasopin; Samuel K Sia
Journal:  Anal Chem       Date:  2016-12-13       Impact factor: 6.986

8.  A digital protein microarray for COVID-19 cytokine storm monitoring.

Authors:  Yujing Song; Yuxuan Ye; Shiuan-Haur Su; Andrew Stephens; Tao Cai; Meng-Ting Chung; Meilan K Han; Michael W Newstead; Lenar Yessayan; David Frame; H David Humes; Benjamin H Singer; Katsuo Kurabayashi
Journal:  Lab Chip       Date:  2020-11-19       Impact factor: 6.799

Review 9.  Futuristic biosensors for cardiac health care: an artificial intelligence approach.

Authors:  Rajat Vashistha; Arun Kumar Dangi; Ashwani Kumar; Deepak Chhabra; Pratyoosh Shukla
Journal:  3 Biotech       Date:  2018-08-03       Impact factor: 2.406

10.  Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19.

Authors:  Michael P McRae; Glennon W Simmons; Nicolaos J Christodoulides; Zhibing Lu; Stella K Kang; David Fenyo; Timothy Alcorn; Isaac P Dapkins; Iman Sharif; Deniz Vurmaz; Sayli S Modak; Kritika Srinivasan; Shruti Warhadpande; Ravi Shrivastav; John T McDevitt
Journal:  medRxiv       Date:  2020-04-22
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