Smartphone-based point-of-care detection systems for infectious diseases

Sun, Fu

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https://hdl.handle.net/2142/114093 Copy

Description

Title

Smartphone-based point-of-care detection systems for infectious diseases

Author(s)

Sun, Fu

Issue Date

2021-12-01

Director of Research (if dissertation) or Advisor (if thesis)

Cunningham, Brian T.

Doctoral Committee Chair(s)

Cunningham, Brian T.

Committee Member(s)

• Do, Minh N.
• Smith, Rebecca Lee
• Zhao, Yang

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Point-of-care
• Diagnosis
• Infectious disease

Abstract

Infectious diseases are still among the world’s leading causes of mortality, where disease burden disproportionately impacts the low-income, resource-limited regions. Early detection of infectious disease outbreaks can reduce the ultimate size of the outbreak and the overall morbidity and mortality due to the disease, in which diagnosis plays an important role. As available diagnostic technologies remain expensive, complex, time-consuming, labor-intensive and mainly limited to centralized healthcare facilities or research laboratories, there is an urgent need for low-cost portable platforms that can provide fast, accurate, and ideally multiplex diagnosis of infectious diseases at the point of care. This dissertation reports on the design, fabrication, and demonstration of smartphone-based detection systems for infectious diseases including equine respiratory diseases, coronavirus disease 2019 (COVID-19) and Zika virus disease. Rapid results have been achieved by employing microfluidics-based sample preparation and loop-mediated isothermal amplification (LAMP). The systems have demonstrated high sensitivity and specificity comparable to state-of-the-art laboratory tests. The work reported in this dissertation shows the strong potential of smartphone-based systems for point-of-care diagnosis.

Graduation Semester

2021-12

Type of Resource

Thesis

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http://hdl.handle.net/2142/114093

Copyright and License Information

Copyright 2021 Fu Sun

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