東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Intelligent Salivary Biosensors for ...

George, Haritha,

Linked to FindBook

Google Book

Amazon

博客來

Intelligent Salivary Biosensors for Systemic Disease Risk Prediction /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent Salivary Biosensors for Systemic Disease Risk Prediction // Haritha George.
Author:	George, Haritha,
Description:	1 electronic resource (259 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Biomedical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32154506
ISBN:	9798280768543

Intelligent Salivary Biosensors for Systemic Disease Risk Prediction /
George, Haritha,

Intelligent Salivary Biosensors for Systemic Disease Risk Prediction /Haritha George. - 1 electronic resource (259 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

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.

English

ISBN: 9798280768543Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Electrochemical biosensing

Intelligent Salivary Biosensors for Systemic Disease Risk Prediction /
LDR:03899nmm a22004573i 4500 001 2434930
005 20251001062558.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798280768543
035 $a (MiAaPQD)AAI32154506
035 $a (MiAaPQD)0799vireo869George
035 $a AAI32154506
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a George, Haritha, $e author. $3 3825080
245 1 0 $a Intelligent Salivary Biosensors for Systemic Disease Risk Prediction / $c Haritha George.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (259 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
500 $a Advisors: Mathew, Mathew T. Committee members: Pesavento, Russell; Takoudis, Christos; Gajendrareddy, Praveen; Yan, Yan; Li, Xue-Jun; Veeravalli, Krishna Kumar.
502 $b Ph.D. $c University of Illinois at Chicago $d 2025.
520 $a 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.
546 $a English
590 $a School code: 0799
650 4 $a Biomedical engineering. $3 535387
650 4 $a Engineering. $3 586835
650 4 $a Bioengineering. $3 657580
653 $a Electrochemical biosensing
653 $a Machine learning
653 $a Disease risk prediction
653 $a Point of care device development
653 $a Salivary biomarker
690 $a 0541
690 $a 0202
690 $a 0537
690 $a 0769
710 2 $a University of Illinois at Chicago. $b Biomedical Engineering. $e degree granting institution. $3 3825081
720 1 $a Mathew, Mathew T. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-12B.
790 $a 0799
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32154506