Background: University dental students perceived a higher level of stress prior to the final exam associated with raised salivary cortisol levels which could be considered as a useful noninvasive biomarker for measuring acute stress. Using a Helkimo anamnestic and clinical dysfunction scoring for temporomandibular disorders can give a better insight about the association of this marker and temporomandibular joint disorders. The aim of this study was to evaluation level of salivary cortisol in stressor students with temporomandibular disorder and the relation between this marker in relation to temporomandibular disorder severity. This might give a better understanding to the role of psychological stress as an etiological factor for developing temporomandibular joint problems. Materials and methods: A total eighty participants age between 20 to 24 were recruited for this study. The participants were University dental students under graduate students at final examination period who were examined and gave saliva samples in final examination period. Salivary assay kits as cortisol was used to measure those variables and a Helkimo anamnestic and clinical dysfunction scoring for TMD. Results: The group of participants with stress and temporomandibular disorder showed significantly higher levels of salivary cortisol than the control group, the salivary cortisol has statistically significant correlation with Helkimo anamnestic categories (Di-I mild, Di-II moderate and Di-III severe. Salivary cortisol levels show significant but weak association with two categories of clinical dysfunction criteria in Helkimo index system, which are Muscle pain and TMJ pain on palpation. Conclusion: This study demonstrated that University students perceived a high level of stress before the final examination. Salivary cortisol is the stress biomarker that is most often used to measure acute stress. Helkimo anamnestic and clinical dysfunction scoring criteria for still the pioneer for measuring a temporomandibular disorder.
In this research, the electrical characteristics of glow discharge plasma were studied. Glow discharge plasma generated in a home-made DC magnetron sputtering system, and a DC-power supply of high voltage as input to the discharge electrodes were both utilized. The distance between two electrodes is 4cm. The gas used to produce plasma is argon gas which flows inside the chamber at a rate of 40 sccm. The influence of work function for different target materials (gold, copper, and silver), - 5cm in diameter and around 1mm thickness - different working pressures, and different applied voltages on electrical characteristics (discharge current, discharge potential, and Paschen’s curve) were studied. The results showed that the discharge cur
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Comparative Analysis of Economic Policy Stability between Monarchical and Republican Systems: A Theoretical Fundamental Research
During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
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Laser is a powerful device that has a wide range of applications in fields ranging from materials science and manufacturing to medicine and fibre optic communications. One remarkable