Saliva as a Potential Specimen to Monitor IL-6, TNF-α and IL-10 in COVID-19 Patients.

The SARS-CoV-2 instigated "cytokine storm" elicited upon infection is known to majorly cause lung injury and even mortality in severe cases. Early clinical prognosis to alleviate the exaggerated release of inflammatory cytokines is thus looked upon. Considering the recent attention and advantages of saliva as a clinical specimen, i.e. ease and painlessness of collection, which does not require trained staff and could allow self-sampling, the present study attempts to explore saliva for detection of IL-6, TNF-α and IL-10 which constitute major inflammatory genes that are elevated in COVID-19 using RT-PCR. Blood specimens of the same patients were also parallelly assessed to compare and validate the inflammatory marker expression. A total of 64 COVID-19 subjects who met the inclusion criteria were enrolled in this pilot study. Paired samples of blood and saliva from each patient were collected as per standard sampling protocols. RNA from all specimens were extracted using Qiagen RNA Blood Mini Kit and subjected to RT-PCR. IL-6, TNF-α and IL-10 expression were assessed in Ct (cycle threshold) values. It was observed that all 64 (100%) patients expressed IL-6 gene and TNF-α gene, whereas only 7 (5.19%) patients expressed IL-10 in both blood and saliva samples. The mean Ct values of IL-6 gene expressed in blood and saliva were 26.68 ± 2.26 and 28.53 ± 3.11 respectively. Similarly, the mean Ct values of TNF-α gene expressed in blood and saliva were 27.98 ± 2.45 and 28.92 ± 3.70 respectively. The observed mean Ct values of IL-10 gene expressed in blood and saliva were 31.26 ± 3.96 and 30.11 ± 4.12 respectively. Accordingly, the results indicate that inflammatory genes IL-6, TNF-α and IL-10 were detectable in both patient saliva as well as in blood. Moreover, mean Ct values of IL-6, TNF-α and IL-10 in both samples were found to be comparable. This finding thus suggests the possible use of saliva as an alternative specimen to blood for monitoring inflammation in COVID-19 patients.

Identification Of Semi-Solid Liquids Using Photodiode And RGB Sensor With Spectrophotometric Neural Network (S-NN) Method

Droplet or human saliva is a semi-solid liquid that Covid-19 can catch on its patch media. It is also one of the causes of the fastest spread of Covid-19, resulting in a pandemic nowadays. So in this preliminary study, we created a tool that uses spectrophotometry to identify semi-solid liquids, including saliva, yogurt, and yeast water. The non-monochromatic spectrophotometric output will be classified using the neural network (NN) method. NN identifies the type of liquid by calculating the weight of each absorption wavelength of each semi-solid liquid sample from a non-monochromatic spectrophotometer. This initial research reveals several types of wavelength spectrum that can be recognized by Photodiode and RGB sensors through non-monochrome spectrophotometric methods. From the test results, saliva samples on glass media have a very high error rate of 99.9098%. For the overall average of saliva samples in all media, the accuracy is 89.1036%, and the error is 10.8964%. For the yogurt sample, the accuracy is 99.3075%, and the error is 0.6925%. The accuracy of the media without liquid is 78.8809%, and the error is 21.1191%. Based on the results, we found that the device can work properly as its aims. © 2022 IEEE.

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

The United States Centers for Disease Control and Prevention considers saliva contact the lead transmission means of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19). Saliva droplets or aerosols expelled by heavy breathing, talking, sneezing, and coughing may carry this virus. People in close distance may be exposed directly or indirectly to these droplets, especially those droplets that fall on surrounding surfaces and people may end up contracting COVID-19 after touching the mucosa tissue on 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 that range 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 virus' presence. A classification of sensors by their working principle and the substance they detect is presented. This comparison lists their 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.