Caries detection analysis in human saliva alpha amylase

Yazid, Farinawati; Zain, Mohd Norzaliman Mohd; Yusof, Zalhan Md; Ghazali, F. S.; Zulkifli, Syed; Nadri, N. M.; Ariffin, Shahrul Hisham Zainal; Wahab, Rohaya Megat Abdul

doi:10.1063/1.5142106

Cited by 8 publications

(18 citation statements)

References 14 publications

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“…The absorption spectra of saliva in health and in pathologies of the oral cavity have been well studied by molecular absorption spectroscopy and fluorescence [ 18 ].…”

Section: Resultsmentioning

confidence: 99%

Spectra of Photobiological Inactivation of SARS-CoV-2 by Solar UVB Radiation (280–320 nm)

Prokop'ev

2021

Atmos Ocean Opt

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Primary photoacceptors and spectra of photobiological inactivation of the SARS-CoV-2 molecular structures (genome and S-spike protein) by UVB radiation were determined for the first time based on experimental ground-based observations of the intensity and spectrum of solar UVB radiation in Tomsk (56°29′ N, 84°56.89′ E) from March 26, 2020, to March 26, 2021, and the analysis of works on the structure and chemical molecular composition of SARS-CoV-2.

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“…The absorption spectra of saliva in health and in pathologies of the oral cavity have been well studied by molecular absorption spectroscopy and fluorescence [ 18 ].…”

Section: Resultsmentioning

confidence: 99%

Spectra of Photobiological Inactivation of SARS-CoV-2 by Solar UVB Radiation (280–320 nm)

Prokop'ev

2021

Atmos Ocean Opt

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“…Optical sensors either uses colorimetric optical absorption or measure a change in wavelength when there is light absorption by the component [ 39 , 40 ].…”

Section: Sensors For Organic and Inorganic Components Of Human Salmentioning

confidence: 99%

“…Yazid et al [ 40 ] proposed an optical sensor to detect dental caries by detecting the amount of amylase in saliva. Amylase can be detected on the basis of optical absorption using an ultraviolet-visible (UV-VIS) spectrometer.…”

Section: Sensors For Organic and Inorganic Components Of Human Salmentioning

confidence: 99%

Survey of Saliva Components and Virus Sensors for Prevention of COVID-19 and Infectious Diseases

et al. 2020

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The United States Centers for Disease Control and Prevention considers saliva contact the lead transmission mean of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19). Saliva droplets or aerosols expelled by sneezing, coughing, breathing, and talking may carry this virus. People in close distance may be exposed directly to these droplets or indirectly when touching the droplets that fall on surrounding surfaces and ending up contracting COVID-19 after touching the mucosa tissue of their faces. It is of great interest to quickly and effectively detect the presence of SARS-CoV-2 in an environment, but the existing methods only work in laboratory settings, to the best of our knowledge. However, it may be possible to detect the presence of saliva in the environment and proceed with prevention measures. However, detecting saliva itself has not been documented in the literature. On the other hand, many sensors that detect different organic components in saliva to monitor a person’s health and diagnose different diseases, ranging from diabetes to dental health, have been proposed and they may be used to detect the presence of saliva. This paper surveys sensors that detect organic and inorganic components of human saliva. Humidity sensors are also considered in the detection of saliva because a large portion of saliva is water. Moreover, sensors that detect infectious viruses are also included as they may also be embedded into saliva sensors for a confirmation of the presence of the virus. A classification of sensors by their working principles and the substances they detect is presented, including the sensors’ specifications, sample size, and sensitivity. Indications of which sensors are portable and suitable for field application are presented. This paper also discusses future research and challenges that must be resolved to realize practical saliva sensors. Such sensors may help minimize the spread of not only COVID-19 but also other infectious diseases.

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“…However, detection using ELISA lacks of accuracy and specificity in detecting the sample at trace level and depends only on an absorption of a single wavelength. Therefore, the purpose of this study is to prove that multi wavelength analysis of biological markers spectrum can give a better conclusive result [14,15]. The objective of this study is to investigate the univariate and multivariate analysis of the ELISA spectrum trace samples.…”

Section: Introductionmentioning

confidence: 99%

Multivariate versus univariate spectrum analysis of dentine sialophosphoprotein (DSPP) for root resorption prediction: a clinical trial

et al. 2022

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Background A force applied during orthodontic treatment induces inflammation to root area and lead to root resorption known as orthodontically induced inflammatory root resorption (OIIRR). Dentine sialophosphoprotein (DSPP) is one of the most abundant non-collagenous proteins in dentine that was released into gingival crevicular fluid (GCF) during OIIRR. The aim of this research is to compare DSPP detection using the univariate and multivariate analysis in predicting classification level of root resorption. Methods The subjects for this study consisted of 30 patients in 3 group classified as normal, mild, and severe groups of OIIRR. The GCF samples were taken from upper permanent central incisors in the normal and mild group while the upper primary second molars in the severe group. The DSPP qualitative detection limit was determined by analyzing the whole absorption spectrum utilizing multivariate analysis embedded with different preprocessing method. The multivariate analysis represents the multi-wavelength spectrum while univariate analyzes the absorption of a single wavelength. Results The results showed that the multivariate analysis technique using partial least square-discriminate analysis (PLS-DA) with the preprocess method has successfully improved in classification prediction for the normal and mild group at 0.88 percent accuracy. The multivariate using PLS-DA algorithm with Mean Center preprocess method was able to predict normal and mild tooth resorption classes better than the univariate analysis. The classification parameters have improved in term of the specificity, precision and accuracy. Conclusion Therefore, the multivariate analysis helps to predict an early detection of tooth resorption complimenting the sensitivity of the univariate analysis. Trial registration NCT 05077878 (14/10/2021).

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Caries detection analysis in human saliva alpha amylase

Cited by 8 publications

References 14 publications

Spectra of Photobiological Inactivation of SARS-CoV-2 by Solar UVB Radiation (280–320 nm)

Spectra of Photobiological Inactivation of SARS-CoV-2 by Solar UVB Radiation (280–320 nm)

Survey of Saliva Components and Virus Sensors for Prevention of COVID-19 and Infectious Diseases

Multivariate versus univariate spectrum analysis of dentine sialophosphoprotein (DSPP) for root resorption prediction: a clinical trial

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