BackgroundObesity is a growing health-related problem worldwide. Both obesity and dental caries are important health issues with multifactorial aspects. Some studies have shown an association between body mass index (BMI) and caries in childhood/adolescence but limited data about such an association are available in adults. The primary goal of this study was to assess the prevalence of dental caries and its relationship to BMI.MethodsWe conducted a cross-sectional study at Taif University Outpatient Clinic, for adults who had a visit to the dental clinic. Baseline characteristics were obtained by the participating physician. The decayed, missing, and filled teeth (DMFT) index was used to determine the prevalence of dental caries. Information about healthy eating, smoking, exercise, sleep patterns, media consumption, and brushing habits were collected.ResultsA total of 385 patients were enrolled with a mean age of 28.39 years, 72.8% were male, mean DMFT index score was 6.55, and 85.5% reported brushing their teeth at least once daily. Of the participants, 55.3% were either overweight or obese, and 42.2% demonstrated a high prevalence of dental caries with no significant difference in BMI when compared to the low dental caries group.ConclusionsA high prevalence of overweight/obesity and dental caries was observed among the participants. After controlling for potential confounders like smoking and brushing habits, significant positive correlation between BMI and DMFT was observed.

The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients’ confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers.

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Objective: This study aimed to identify and compare the factors that contribute to patient satisfaction towards the medical care services between governmental and private healthcare clinics. Methods:A self-administrated (Arabic/English) questionnaire was used to conduct the all participants. Participants were selected using convenience sampling for both the governmental and private clinics.Results: Participated were 141 patients from the government clinics and 215 patients from private clinics. 75% of the patients were Saudi, 49.1% were male patients. Opinion about level of care provided was significantly higher in AL-Ameen Hospital outpatient clinic (AHOC) (77%) than Taif University outpatient clinic (TUOC) (59%), the physician explain way of taking medication was (42%) in TUOC compared to (55%) in AHOC. Waiting time >30 Min was (7.09%) in TUOC while it was (30.70%) in AHOC. The time spent during examination >30 Min was (3.55%) in TUOC while it was (7.91%) in AHOC. Satisfaction about working hours was significantly less in TUOC (29%) compared to (44%) in AHOC. Conclusion:Although patients at the AHOC were more satisfied than those at TUOC with the health care they received, eight of the predictors of patient satisfaction in this study were common to both settings.