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.
Re-use of the byproduct wastes resulting from different municipal and industrial activities in the reclamation of contaminated water is real application for green projects and sustainability concepts. In this direction, the synthesis of composite sorbent from the mixing of waterworks and sewage sludge coated with new nanoparticles named “siderite” (WSSS) is the novelty of this study. These particles can be precipitated from the iron(II) nitrate using waterworks sludge as alkaline agent and source of carbonate. Characterization tests using X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) mapping revealed that the coating process was c
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In this study, novel Schiff base complexes with Zn(II) and Co(II) ions were successfully synthesized. The malonic acid dihydrazide was converted into the Schiff base ligand by combining it with 1-hydroxy-2-naphthaldehyde, and the last step required reacting it with the appropriate metal(II) chloride to produce pure target complexes. The generated complexes were thoroughly characterized using FTIR, 1H-NMR, 13C-NMR, GC-mass, and UV-Vis spectroscopies. In order to photo-stabilize polystyrene (PS) and reduce the photodegradation of its polymeric chains, these chemicals have been used in this work. The efficiency of the generated complexes as photo-stabilizers was evaluated using a variety of techniques, including FTIR, weight loss, visc
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Autoría: Muwafaq Obayes Khudhair. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 6, 2022. Artículo de Revista en Dialnet.
Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a
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Complex-valued regular functions that are normalized in the open unit disk are vastly studied. The current study introduces a new fractional integrodifferential (non-linear) operator. Based on the pre-Schwarzian derivative, certain appropriate stipulations on the parameters included in this con-structed operator to be univalent and bounded are investigated and determined.
This work involves theoretical and experimental studies for seven compounds to calculate the electrons spectrum and NLO properties. The theoretical study is done by employing the Time Depending Density Functional Theory TD-DFT and B3LYP/high basis set 6-311++G (2d,2p), using Gaussian program 09. Experimental study by UV/VIS spectrophotometer device to prove the theoretical study. Theoretical and experimental results were applicable in spectrum and energy gap values, in addition to convergence theoretically the energy gap results from ΔEHOMO-LUMO and UV/VIS. spectrum. Consider the theoretical method very appropriate to compounds that absorb in vacuum UV.
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