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.

The PentaFOLD 3.0 Algorithm for the Selection of Stable Elements of Secondary Structure to be Included in Vaccine Peptides.

AIMS: The aim of this study was to create a new version of the PentaFOLD algorithm and to test its performance experimentally in several proteins and peptides. BACKGROUND: Synthetic vaccines can cause production of neutralizing antibodies only in case if short peptides form the same secondary structure as fragments of full-length proteins. The Penta- FOLD 3.0 algorithm was designed to check stability of alpha helices, beta strands, and random coils using several propensity scales obtained during analysis of 1730 3D structures of proteins. OBJECTIVE: The algorithm has been tested in the three peptides known to keep the secondary structure of the corresponding fragments of full-length proteins: the NY25 peptide from the Influenza H1N1 hemagglutinin, the SF23 peptide from the diphtheria toxin, the NQ21 peptide from the HIV1 gp120; as well as in the CC36 peptide from the human major prion protein. METHODS: Affine chromatography for antibodies against peptides accompanied by circular dichroism and fluorescence spectroscopy were used to check the predictions of the algorithm. RESULTS: Immunological experiments showed that all abovementioned peptides are more or less immunogenic in rabbits. The fact that antibodies against the NY25, the SF23, and the NQ21 form stable complexes with corresponding full-length proteins has been confirmed by affine chromatography. The surface of SARS CoV-2 spike receptor-binding domain interacting with hACE2 has been shown to be unstable according to the results of the PentaFOLD 3.0. CONCLUSION: The PentaFOLD 3.0 algorithm (http://chemres.bsmu.by/PentaFOLD30.htm) can be used with the aim to design vaccine peptides with stable secondary structure elements.