Saliva is a useful biomarker for diagnosing oral health conditions, including periodontal disease (PD). Smoking is a risk factor for PD. The aim of this systematic review was to summarize the salivary biomarkers associated with PD based on smoking status. A comprehensive search of the MEDLINE (via PubMed), EMBASE, Cochrane, SCOPUS, and Web of Sciences databases was conducted up to 1 January 2021 using key terms relevant to the topic of our research and Cochrane methodology and improved with searching a gray literature resource. The methodological quality of all included studies was assessed with the revised Quality Assessment of Diagnostic Accuracy Studies-2. Seven studies were included. Smokers had increased levels of malondialdehyde, sialic acid, salivary cortisol, salivary interleukin 1β, albumin, tissue inhibitor of matrix metalloproteinase (TIMP), and the pyridinoline cross-linked carboxyterminal telopeptide of type I collagen (ICTP), as well as decreased levels of superoxide dismutase, activity of lactate dehydrogenase, activity of enzyme activity of β-glucuronidase, uric acid, matrix metalloproteinase-8 (MMP-8)/TIMP-1 ratio, and combinations of MMP-8 and ICTP. However, mixed results were observed some studies in detecting glutathione peroxidase, MMP-8, and MMP-14. The results were interpreted with caution because of limitations in the number of included studies and the study design. Some salivary biomarkers are potentially useful in combination or alone for diagnosing PD. Methodological and systematic studies are needed to develop more effective biomarkers.
Objective: Currently, non-face-to-face classes are dramatically on the increase due to COVID-19. This study was conducted to investigate the preferred lecture types in non-face-to-face classes and factors affecting class satisfaction among health students and to understand their correlation. Method: In this study, a self-report survey was performed on a total of 238 health students attending three universities in Chungcheong Province. An online survey was conducted on them using Naver Form from May 3 to 30, 2021. The questionnaire consisted of 14 questions about the class quality and type of non-face-to-face lectures such as preferred class content, learning environments, and interaction, 4 questions about perceived learning achievements, and 4 questions about class satisfaction with the students. Statistical analysis was performed using one-way ANOVA and Pearson correlation analysis. Result: The survey results showed that satisfaction with non-face-to-face classes was 14.06 out of 25. As for the scores for each sub-factor, the quality of class was in the order of learning environments (22.27 points), learning content (19.01 points), and interaction (10.33 points). Learning achievements were 28.27 points and learning sentiments were 16.59 points. As for the learning sentiments in a non-face-to-face class, 'Simple Class Materials' showed the highest score with 19.00 points, followed by 'Video Lectures Using PPT' with 17.18 points (p=0.001). In terms of learning achievements, 'Video Lecture Using PPT' was the highest with 29.32 points (p=0.014). Lecture types showed little significant differences in the quality of class (P > 0.05). It was found that there was a significant positive correlation between class quality, learning achievements, and learning sentiments. Conclusion:In the study, the class type with the highest satisfaction with the students was found to be an LMS (e-learning) recorded lecture, while the class type with the lowest satisfaction was a real-time video lecture in a non-face-to-face class for health students. Considering the findings showing that there is a positive correlation between the quality of non-face-to-face classes, learning achievements, and learning sentiments, it was suggested that non-face-to-face classes should reflect the class content as well as the curriculum and educational environments in order to improve students' satisfaction with the classes.
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