Background: The COVID-19 virus outbreak had a massive effect on many parts of people's lives, as they were advised to quarantine and lockdown to prevent the virus from spreading, which had a big impact on people's mental health, anxiety, and stress. Many internal and external factors lead to stress. This negatively influences the body's homeostasis. As a result, stress may affect the body's capacity to use energy to defend against pathogens. Many recent investigations have found substantial links between human mental stress and the production of hormones, prohormones, and/or immunological chemicals. some of these researches have verified the link between stress and salivary cortisol levels. The aim of this study is to measure salivary cortisol as a stress biomarker as well as a total viable count of salivary bacterial microbiome among COVID-19 patients. Materials and methods: a sample of 84 adults patients was collected who were divided into two groups: the COVID-19 group consists of 42 patients and the COVID-19 free group which consists of 42 subjects. All subjects undergo a PCR test to confirm their health status. The collection of Un-stimulated saliva was done. Laboratory investigations were carried out to measure the total viable count of the salivary bacterial microbiome by culturing on Brain Heart Infusion Agar and to evaluate the salivary cortisol level using cortisol kit (Elecsys Cortisol II). Results: SPSS version 21 was used for statistical analysis. According to the statistical analysis, the salivary cortisol and total viable count of salivary bacterial microbiome values were substantially greater in the COVID-19 group than in the COVID-19 free group. Conclusion: A positive association was found between salivary cortisol and the total viable count of the salivary bacterial microbiome. So, when the concentration of salivary cortisol is elevated in the COVID-19 group, the level of the total viable count of the salivary bacterial microbiome is also elevated.
Decolorization of red azo dye (Cibacron Red FN-R) from synthetic wastewater has been investigated as a function of solar advanced oxidation process. The photocatalytic activity using ZnO as a photocatalysis has been estimated. Different parameters affected the removal efficiency, including pH of the solution, initial dye concentration and H2O2 concentration were evaluated to find out the optimum value of these parameters. The results proved that the optimal pH value was 8 and the most efficient H2O2 concentration was 100mg/L. Toxicity reduction percent for effluent solution was also monitored to assess the degradation process. This treatment method was able to strongly reduce the color and toxicity of reactive red dye-238 to about (99 an
... Show MoreMassive multiple-input multiple-output (MaMi) systems have attracted much research attention during the last few years. This is because MaMi systems are able to achieve a remarkable improvement in data rate and thus meet the immensely ongoing traffic demands required by the future wireless networks. To date, the downlink training sequence (DTS) for the frequency division duplex (FDD) MaMi communications systems have been designed based on the idealistic assumption of white noise environments. However, it is essential and more practical to consider the colored noise environments when designing an efficient DTS for channel estimation. To this end, this paper proposes a new DTS design by exploring the joint use of spatial channel and n
... Show MoreFlexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of eac
... Show MoreThis study was done to determine the concentration of several heavy metals in the water of Al-Saddah agricultural drainage in Al-Saddah District in Babylon Province/Iraq. The concentrations of six heavy metals were measured (Pb, Cd, Cu, Hg, Fe, Zn). It was found that Pb concentration ranged from 0.06 mg/L at St.2 in autumn to 0.13 mg/L at St.2 in winter. Fe concentrations ranged from 0.04 mg/L at St.2 in autumn and winter to 0.41 at St.2 in Summer. Cd concentrations ranged from 0.008 mg/L at St.2 in summer to 0.05 mg/L at St.2 in winter. Cu concentrations ranged from 0.01 mg/L at St.1 in both autumn and winter to 0.63 mg/L at St.2 in winter. Hg concentrations was ranged from 0.002 mg/

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2, 0, 0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlat
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