IMPROVING THE EFFECTIVENESS OF THE TREATMENT OF XEROSTOMIA IN PATIENTS CONFRONTED COVID-19

COVID-19 caused by an emerging pathogen SARS-CoV-2 is able to lead to various pathological conditions in the oral cavity. Of huge importance is the risk of xerostomia which can occur in both during the height of the disease and after recovery from this novel coronavirus infection. A possible risk factor for xerostomia in patients with COVID-19 may be the impact of SARS-CoV-2 on the expression of angiotensin-converting enzyme 2 by salivary gland cells. Efforts are under way in many countries to treat and minimize the so called post-COVID syndrome. Our clinical and epidemiological study was aimed at studying the effectiveness of the treatment of xerostomia in patients with COVID-19. The research question required an observational, prospective, sampling, controlled open before-after intervention study design. This paper describes the potential of using modern moisturizing polycomponent toothpastes in patients with signs of xerostomia who have undergone COVID-19. The study included 40 patients of both sexes, aged 32 to 44 years. 78 +/- 6.6% were female participants. Research methods such as medical and dental examination, sialometry test for measuring saliva flow, as well as statistical analysis and interpretation were used. Our research has shown that the application of moisturizing toothpaste can improve salivation and subjective sensations of this group of patients. In addition, we have outlined approaches to the construction of further clinical and epidemiological studies of effectiveness of interventions similar in nature. In turn, this will allow us to make more accurate and maximally unbiased judgments about prophylactic and therapeutic effects of such interventions.

Modernization of Regression Models to Predict the Number of Deaths from the New Coronavirus Infection.

The aim of the study was to modernize the existing prognostic regression models in the context of expanding knowledge about the new coronavirus infection. Materials and Methods: The modification of models and the increase in their predictive ability are based on collecting the available data from international and Russian databases. We calculated the traditional descriptive statistics and used the linear regression analysis for modeling. The work was performed using the IBM SPSS Statistics 26.0 and the R 3.6.0 (RStudio) software. Results: Manifestations of the COVID-19 epidemic process in several countries were studied; special attention was put to the number of deaths associated with the infection. A significant proportion of severe cases were noted among patients both in Russia and elsewhere. Considering that the disease incidence has reached its peak in China and Italy, we were able to improve the previously published (Sovremennye tehnologii v medicine 2020, Vol. 12, No.2) regression models and to compare their performance. The first modified model is based on the absolute increase in new cases of the infection: its regression coefficient is 0.16 (95% CI 0.137-0.181). In the extended version of the updated model, we additionally considered cases of aggravated COVID-19: the regression coefficients were 0.128 (95% CI 0.103-0.153) for model 2 and 0.053 (95% CI 0.029-0.077) for model 1.1; p=0.0001. Conclusion: Based on the most recent data (from January to May 2020) on the incidence of COVID-19 in the world, we have developed more specific versions of the basic and extended regression models of lethal outcomes. The resulting models are optimized and extrapolated to the current epidemiological situation; they will allow us to improve our analytical approach. For that purpose, data collection is currently ongoing.

Comparative characteristics of a subsequent morbidity wave COVID-19 in various regions of the world

Relevance. The COVID-19 pandemic is characterized by a long undulating course. One of the directions of the dynamic assessment of the incidence of this infection is, as is known, the characterization of the determinants of the epidemic process and the study of the actual effectiveness of various measures. Aims. Were to study the features of the COVID-19 morbidity in the European, American and Asian regions of the world on the example of individual countries with an assessment of the possible impact of regime-restrictive measures on the daily increase in cases. Materials & methods. A descriptive epidemiological study involved the use of the following data on COVID-19: daily increase in new infections in absolute numbers and relative indicators during 1 June 2020 till 30 November 2020 in five countries (France, Italy, USA, Brazil, India), description and timing of various restrictive measures. Information obtained from open sources (situation reports from WHO, CDC, ECDC, national ministries of health, etc.). Time series characterized, defining sharply differing values, timing and duration of ups and downs, the rate of average daily growth (decline). Statistical analysis was carried out using the IBM SPSS Statistics 26. Results. On average, for the analyzed period of time, 1303 were registered in Italy, 4897, France – 52799, Brazil – 31853, India –50507new cases. The average incidence rate in the compared countries ranged from 500.98 ± 417.06 per 100,000 in India to 4399.43 ± 2390.77 per 100,000 in the US. After the passage of the «first wave» of the incidence of COVID-19, regardless of the region of the world, there was an increase in the daily increase in new cases of SARS-CoV-2 in the summer-autumn period of 2020. Furthermore, with the differences in the morbidity rates in the different countries, there were also characteristics the formation of similar to the region. For the European region (Italy, France), there was a simultaneous beginning of an increase in the incidence in August-September 2020, a similar trend towards exponential growth and synchronous fluctuations in the daily increase in absolute cases of diseases. For the countries of the American region (USA and Brazil), a similar sinusoidal nature of the dynamics of the average daily increase in infection cases and its synchronicity until October 2020 was revealed. The Asian region, on the example of India, had significant differences in the dynamics of the analyzed indicators in comparison with the countries of the European and American regions. Differences in the formation of morbidity in the summer-autumn period were more pronounced between the regions and related to the level of average daily growth, the incidence rate, the month of the maximum rise in the incidence in this period, and trend differences. Comparison of the ongoing isolation measures with the daily increase in cases revealed their discrepancy. This could create the preconditions for the activation of the epidemic process of infection and the ineffectiveness of measures. Conclusions. We found that in the five countries examined, the situation developed according to a similar scenario. Nevertheless, in different regions of the world there was a specificity in the involvement of the territory in the epidemic process. A more in-depth study of the timeliness and completeness of regime-restrictive measures against SOCID-19 should include a comparison with the patterns of formation and manifestations of the epidemic process. In turn, this is important for scientifically based implementation and increasing their effectiveness. © 2021, Numikom. All rights reserved.

Regression Models Predicting the Number of Deaths from the New Coronavirus Infection.

Predicting the development of epidemic infection caused by the COVID-19 coronavirus is a matter of the utmost urgency for health care and effective anti-epidemic measures. Given the rapidly changing initial information and the ambiguous quality of data coming from various sources, it is important to quickly optimize the existing prognostic models by using more sophisticated algorithms. The aim of the study is to test the originally developed mathematical algorithms for predicting the development of the COVID-19 epidemic process. MATERIALS AND METHODS: To assess the situation in China, Italy, and the USA, we used the information from Russian- and English-language sources available in official websites. The generally accepted descriptive statistics were used; mathematical modeling was based on linear regression. Statistical data processing was performed using the IBM SPSS Statistics 24.0 and R (RStudio) 3.6.0. RESULTS: We found significant differences not only in the incidence rate of COVID-19 in the countries in question, but also in the death rate. The risk of death associated with COVID-19 is high due to the high number of severe clinical cases of the disease reported from these countries.Two preliminary regression models were created. The first, initial model was based on the increase in new cases of infection - this factor was significantly associated with the outcome; the regression coefficient was 0.02 (95% CI 0.01-0.03). In the second, expanded model, in addition to the increase in new cases, the increase in the number of severe forms of infection was also considered; the regression coefficients were 0.017 (95% CI 0.012-0.022) and 0.01 (95% CI 0.008-0.011), respectively. Adding the second variable contributed to a more accurate description of the available data by the model. CONCLUSION: The developed regression models for infection control and predicting the number of lethal outcomes can be successfully used under conditions of spreading diseases from the group of "new infections" when primary data received from various sourced are changing rapidly and updates of the information are continually required. In addition, our initial model can produce a preliminary assessment of the situation, and the expanded model can increase the accuracy and improve the analytic algorithm.