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.

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.

Design of highly sensitive biosensors using hollow-core microstructured fibers for plasma sensing in aids with human metabolism.

Detection of low index liquid analytes in real-time, in-situ, and with high accuracy is of great importance in various scientific fields, particularly in medicine and biology. Accurate detection of plasma concentration in blood samples is one of the most significant usages of biosensors in medicine. In this paper, we report a highly sensitive biosensor using hollow core microstructure optical fibers (HC-MOFs) to detect low index liquid analytes with a particular focus on detection of plasma concentration in blood samples. We demonstrate how variations in plasma concentration in blood can change transmission spectra of the HC-MOF due to the photonic bandgap mechanism. We use the finite element approach to explore how the biosensor's performance depends on the number of capillary rings encircling the hollow core of the fibre. An average spectral and amplitude sensitivity of 8928.57 nm/RIU and 1.21 dB/RIU is reported for the optimized design of HC-MOF for five capillary rings with a refractive index detection range of 1.333 to 1.3385 for different ratios of plasma in blood serum. The proposed biosensor can have potential application in liquid analyte detection in medicine, chemistry, and biology where real-time and accurate data about liquid analytes are necessary for human metabolism.

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.

An Analytic Model for Reducing Authentication Signaling Traffic in an End-to-End Authentication Scheme.

In an end-to-end authentication (E2EA) scheme, the physician, patient, and sensor nodes authenticate each other through the healthcare service provider in three phases: the long-term authentication phase (LAP), short-term authentication phase (SAP), and sensor authentication phase (WAP). Once the LAP is executed between all communication nodes, the SAP is executed (m) times between the physician and patient by deriving a new key from the PSij key generated by healthcare service provider during the LAP. In addition, the WAP is executed between the connected sensor and patient (m + 1) times without going back to the service provider. Thus, it is critical to determine an appropriate (m) value to maintain a specific security level and to minimize the cost of E2EA. Therefore, we proposed an analytic model in which the authentication signaling traffic is represented by a Poisson process to derive an authentication signaling traffic cost function for the (m) value. wherein the residence time of authentication has three distributions: gamma, hypo-exponential, and exponential. Finally, using the numerical analysis of the derived cost function, an optimal value (m) that minimizes the authentication signaling traffic cost of the E2EA scheme was determined.