University of Illinois Chicago
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Intelligent Salivary Biosensors for Systemic Disease Risk Prediction

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posted on 2025-05-01, 00:00 authored by Haritha George
The mouth is said to be the mirror to health, but how can health be assessed? With the COVID era, saliva has become the most promising biofluid for disease testing due to its non-invasiveness and its ability to reflect the disease markers as we see in blood. One of the ways to know about overall health is to start with an oral disease and find a linkage to the systemic disease so that the systemic disease’s underlying issues, like oxidative stress, can be detected. The use of non-protein molecules that could be used as biomarkers, with electrochemistry and machine learning models gave an insight to the oxidative stress aspect. As a proof of concept, the oxidative stress and periodontal biomarker Matrix Metallo Proteinase 9 (MMP9) and its usefulness for overall health deduction and systemic disease detection or risk of occurrence were looked into. Together with the broad biomarker MMP9, more specific disease biomarkers interleukin 6 (IL6), human cytokeratin fragment antigen 21-1 (CYFRA21-1), and glutathione (GSH) were also tested electrochemically for simulated Diabetes, Oral Cancer, and Stroke like conditions. The Machine learning-based detection of the electrochemical data was developed which will enable a biomolecule level and associated risk to be predicted for the disease occurrence early on, which could prompt a decision to be healthier. Verification of the developed biosensor and machine learning system was done by testing the simulated stroke-like condition in the microglia HMC3 cell line with Lipopolysaccharide (LPS), and antioxidant Glutathione (GSH) was used to check the continuous monitoring possibilities. Validation of the biosensor system was done with volunteer saliva samples. Intelligent salivary biosensor testing in the future can be turned into affordable point-of-care testing at the dentist's office, workplace, home, or remote setting. Having an early prediction of the disease risk will help with better disease management as well as give the patient and their caregivers a better quality of life. The effect of the drugs as a measure of reduction in biomarkers can also be checked using this salivary diagnosis. This early disease prediction or risk prediction will also reduce the healthcare cost and insurance load on society.

History

Advisor

Mathew T Mathew

Department

Biomedical Engineering

Degree Grantor

University of Illinois Chicago

Degree Level

  • Doctoral

Degree name

PhD, Doctor of Philosophy

Committee Member

Russell Pesavento Christos Takoudis Praveen Gajendrareddy Yan Yan Xue-Jun Li Krishna Kumar Veeravalli

Thesis type

application/pdf

Language

  • en

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