Vitamin D, Cholesterol, and DXA Value Relationship with Bimaxillary Cone Beam CT Values.

We evaluated the correlation that Vitamin D (Vit D), cholesterol levels, and T- and Z-scores of dual-energy X-ray absorptiometry (DXA) scans have with cone beam computed tomography values assessed in the anterior and posterior regions of maxillary and mandibular jaws. In total, 187 patients were recruited for this clinical study. Patients' ages ranged between 45 and 65 years. Patients with valid DXA results, serum Vit D and cholesterol levels, and no evidence of bone disorders in the maxilla or mandibular region were included in the study and grouped in the control (non-osteoporosis) and case (osteoporosis) groups. Patients with a history of medical or dental disease that might complicate the dental implant therapy, chronic alcohol users, and patients who took calcium or Vit D supplements were excluded. The outcome variables assessed in the investigation were Vit D, cholesterol, Z-values, and cone beam computed tomography values. Regarding the case group, a significant (p < 0.05) inverse relationship was observed between Vit D and cholesterol. Although insignificant (p > 0.05), a positive relationship was found between Vit D and the cone beam computed tomography values in all regions of the jaws, except the mandibular posterior region (p < 0.05). Pearson correlation analysis was carried out. Vit D and cholesterol showed a statistically insignificant (p > 0.05) negative association with the cone beam computed tomography values in all regions of the jaws. However, the Z-values were highly correlated with the cone beam computed tomography values in all regions of the jaws (r > 7, p < 0.05). Vit D, cholesterol levels, and Z-values in women and men from young adulthood to middle age (45-65) were related with the cone beam computed tomography values of the jaws.

Towards a Smart Environment: Optimization of WLAN Technologies to Enable Concurrent Smart Services.

In this research paper, the spatial distributions of five different services-Voice over Internet Protocol (VoIP), Video Conferencing (VC), Hypertext Transfer Protocol (HTTP), and Electronic Mail-are investigated using three different approaches: circular, random, and uniform approaches. The amount of each service varies from one to another. In certain distinct settings, which are collectively referred to as mixed applications, a variety of services are activated and configured at predetermined percentages. These services run simultaneously. Furthermore, this paper has established a new algorithm to assess both the real-time and best-effort services of the various IEEE 802.11 technologies, describing the best networking architecture as either a Basic Service Set (BSS), an Extended Service Set (ESS), or an Independent Basic Service Set (IBSS). Due to this fact, the purpose of our research is to provide the user or client with an analysis that suggests a suitable technology and network configuration without wasting resources on unnecessary technologies or requiring a complete re-setup. In this context, this paper presents a network prioritization framework for enabling smart environments to determine an appropriate WLAN standard or a combination of standards that best supports a specific set of smart network applications in a specified environment. A network QoS modeling technique for smart services has been derived for assessing best-effort HTTP and FTP, and the real-time performance of VoIP and VC services enabled via IEEE 802.11 protocols in order to discover more optimal network architecture. A number of IEEE 802.11 technologies have been ranked by using the proposed network optimization technique with separate case studies for the circular, random, and uniform geographical distributions of smart services. The performance of the proposed framework is validated using a realistic smart environment simulation setting, considering both real-time and best-effort services as case studies with a range of metrics related to smart environments.

Quantitative Evaluation of Dental Students' Perceptions of the Roleplay-Video Teaching Modality in Clinical Courses of Dentistry: A Pilot Study.

In the modern era of dentistry, role modeling/roleplaying is one of the most prevalent and recommended methods of dental education. Working on video production projects and using student-centred learning also help students create feelings of ownership and self-esteem. This study aimed to compare students' perceptions of roleplay videos among genders, different disciplines of dentistry, and different levels of dental students. This study included 180 third- and fourth-year dental students registered in courses such as 'Introduction to Dental Practice' and 'Surgical management of oral and maxillofacial diseases', respectively, at the College of Dentistry at Jouf University. Four groups of recruited participants were pre-tested using a questionnaire about their clinical and communication skills. The students were tested again using the same questionnaire at the end of the workshop to evaluate improvements in their skills. The students were then assigned to create roleplay videos with respect to demonstrated skills related to all three disciplines (Periodontics, Oral Surgery, and Oral Radiology) in a week's time. Students' perceptions of the roleplay video assignments were collected through a questionnaire survey. The Kruskal-Wallis test was used to compare responses for each section of the questionnaire (p < 0.05). Improvements in problem-solving and project management skills during video production were reported by 90% of the participants. No significant difference (p > 0.05) in the mean scores of the responses was found with respect to the type of discipline involved in the process. There was a significant difference in the mean scores of the responses between male and female students (p < 0.05). The fourth year participants demonstrated increased mean scores and significantly higher (p < 0.05) mean scores than third-year participants. Students' perceptions of roleplay videos differed by gender and the level of the students, but not by the type of discipline.

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification.

Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease. MetS is also linked to numerous cancers and chronic kidney disease. All of these variables raise medical costs. Developing a prediction model that can quickly identify persons at high risk of MetS and offer them a treatment plan is crucial. Early prediction of metabolic syndrome will highly impact the quality of life of patients as it gives them a chance for making a change to the bad habit and preventing a serious illness in the future. In this paper, we aimed to assess the performance of various algorithms of machine learning in order to decrease the cost of predictive diagnoses of metabolic syndrome. We employed ten machine learning algorithms along with different metaheuristics for feature selection. Moreover, we examined the effects of data augmentation in the prediction accuracy. The statistics show that the augmentation of data after applying feature selection on the data highly improves the performance of the classifiers.