Dimensional stability of 3D-printed edentulous and fully dentate hollowed maxillary models over periods of time.

BACKGROUND: Dental casts made utilising digital workflow are becoming more common because to their speed and cost savings. However, studies on their dimensional accuracy over time with diverse designs are missing. OBJECTIVE: The aim of this in vitro study was to assess the dimensional stability of 3D-printed edentulous and fully dentate hollowed maxillary models with 50-micrometer resolution over 1 day, 14 days, and 28 days using surface matching software. METHODS: Scanned edentulous and fully dentate maxillary typodont models were used as references. The models were scanned by a desktop lab scanner of 15-micrometer accuracy (D900, 3Shape). Then, the files were used in designing software (Meshmixer, Autodesk) to create hollowed maxillary casts. Fifteen edentulous and 15 fully dentate (total of 30) models were printed using a DLP lab printer (Cara print 4.0, Kulzer). The 3D-printed models were scanned using the same desktop lab scanner of 15-micrometer accuracy at intervals of baseline days, 1 day, 14 days, and 28 days to assess the effect of aging (n = 120). The dimensional changes were quantified and compared using the root mean square (RMS) method, expressed in micrometres (µm). The study employed repeated measures analysis of variance (ANOVA) to assess and compare the root mean square (RMS) values across the variables. The data was analysed using SPSS (26, Chicago, Illinois, USA). RESULTS: The RMS of the edentulous models rapidly increased from a mean value of 0.257 at the beginning of the study to 0.384 after twenty-eight days. However, the mean RMS values for the dentate models did not change much over the four intervals. It varied only from 0.355 to 0.347. The mean values for edentulous patients increased from 0.014 to 0.029 during the period from baseline to twenty-eight days. However, the mean average values decreased for the dentate models from 0.033 to 0.014 during this period. By utilizing ANOVA, mean RMS values increased insignificantly till one day but significantly to fourteen and twenty-eight days. Dentate model mean values differed insignificantly across four intervals. Repeated measures ANOVA for combined and separated data showed no significant differences across edentulous, dentate, and total models over times. CONCLUSION: The study revealed changes in the dimensions of 3D-printed edentulous models over a span of 3 and 4 weeks. Caution should be applied when using 3D-printed dental master models for constructing definitive prostheses on edentulous models over a period of 3 to 4 weeks.

Risk factors predicting disease severity and mortality in coronavirus disease 2019 Saudi Arabian patients.

CONTEXT: Coronavirus disease 2019 (COVID-19) became a global pandemic that may be associated with significant associated risk factors. AIMS: The aim of this study was to evaluate the factors predisposing risk to death in COVID-19 patients. SETTINGS AND DESIGN: This is a retrospective study that presents the demographic, clinical presentation, and laboratory findings on our patients to determine risk factors contributing to their COVID-19 outcome. METHODS: We used logistic regression (odds ratios) to examine associations between clinical findings and risk of death in COVID-19 patients. All analyses were done using STATA 15. RESULTS: A total of 206 COVID-19 patients were investigated, 28 of them died, and 178 survived. Expired patients were older (74.04 ± 14.45 vs. 55.56 ± 18.41 in those who survived) and mainly of male gender (75% vs. 42% in those who survived). The following factors were strong predictors of death: hypertension (OR: 5.48, 95% CI: 2.10-13.59, P < 0.001), cardiac disease (OR: 5.08, 95% CI: 1.88-13.74, P = 0.001), and hospital admission (OR: 39.75, 95% CI: 5.28-299.12, P < 0.001). In addition, blood group B was more frequent in expired patients (OR: 2.27, 95% CI: 0.78-5.95, P = 0.065). CONCLUSIONS: Our work adds to the current knowledge about the factors predisposing to death in COVID-19 patient. In our cohort, expired patients were of older age and male gender plus they were more likely to have hypertension, cardiac disease, and hospital severe disease. These factors might be used to evaluate risk of death in patients recently diagnosed of COVID-19.

Intelligent Transportation Using Wireless Sensor Networks Blockchain and License Plate Recognition.

License Plate Recognition (LPR) is essential for the Internet of Vehicles (IoV) since license plates are a necessary characteristic for distinguishing vehicles for traffic management. As the number of vehicles on the road continues to grow, managing and controlling traffic has become increasingly complex. Large cities in particular face significant challenges, including concerns around privacy and the consumption of resources. To address these issues, the development of automatic LPR technology within the IoV has emerged as a critical area of research. By detecting and recognizing license plates on roadways, LPR can significantly enhance management and control of the transportation system. However, implementing LPR within automated transportation systems requires careful consideration of privacy and trust issues, particularly in relation to the collection and use of sensitive data. This study recommends a blockchain-based approach for IoV privacy security that makes use of LPR. A system handles the registration of a user's license plate directly on the blockchain, avoiding the gateway. The database controller may crash as the number of vehicles in the system rises. This paper proposes a privacy protection system for the IoV using license plate recognition based on blockchain. When a license plate is captured by the LPR system, the captured image is sent to the gateway responsible for managing all communications. When the user requires the license plate, the registration is done by a system connected directly to the blockchain, without going through the gateway. Moreover, in the traditional IoV system, the central authority has full authority to manage the binding of vehicle identity and public key. As the number of vehicles increases in the system, it may cause the central server to crash. Key revocation is the process in which the blockchain system analyses the behaviour of vehicles to judge malicious users and revoke their public keys.

A Questionnaire-Based Study to Assess Knowledge and Awareness Regarding Cheiloscopy as a Forensic Odontology Diagnostic Tool Among Dental Professionals.

Background: The lips are covered with grooves and wrinkles, which form a characteristic pattern called a "lip print. The study of lip prints is called cheiloscopy. Searching for lip prints in the crime scene investigation helps in personnel identification and establishment of the true nature of the crime. Objective: This study aimed to assess the knowledge and awareness of cheiloscopy among dental undergraduates, postgraduate students, and general dental practitioners. Materials and methods : This cross-sectional observational, descriptive, survey-based study was conducted among 320 dental professionals, which included undergraduates, graduates, postgraduate dental students, and general dental practitioners aged between 18 and 32 years. A self-administered structured questionnaire written in English and Arabic was distributed to all willing participants. The questionnaire included knowledge and awareness-based questions along with demographic details of the participants. The Chi-square and Fisher's exact tests were applied to find out the association between the characteristics of the study participants and their knowledge and awareness of forensic odontology. A p-value of 0.05 was considered significant for all the statistical tests using IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp. Results: A total of 320 dental professionals completed the survey. The majority of participants (55.3%) were males (and 14.4% were females) between the ages of 23 and 27. Most of the participants were general dental practitioners (36.9%), followed by undergraduates (26.3%), graduates (8.8%), and postgraduates (18.1%). Cheiloscopy, the study of lip prints, was known to 36.6% of the participants. Whereas the majority of the participants (63.4%) were not aware of it. Postgraduate (46.7%) students had more knowledge as compared to undergraduates, graduates, and general dental practitioners. About 81.6% of the participants were not aware of the classification of lip prints by Tsuchihashi and Suzuki. Conclusion:Overall, there was a lack of knowledge and awareness of cheiloscopy among all study participants, although they had good knowledge of forensic odontology. Compared to undergraduates and graduates, postgraduate dentistry students showed a greater level of cheiloscopy knowledge and awareness. Comparatively to students, general dentists, however, lacked understanding and awareness of cheiloscopy. This condition, however, can be improved if necessary steps are taken to make forensic odontology a part of the dental curriculum in Saudi Arabia.

Novel Prediction Model for COVID-19 in Saudi Arabia Based on an LSTM Algorithm.

The rapid emergence of the novel SARS-CoV-2 poses a challenge and has attracted worldwide attention. Artificial intelligence (AI) can be used to combat this pandemic and control the spread of the virus. In particular, deep learning-based time-series techniques are used to predict worldwide COVID-19 cases for short-term and medium-term dependencies using adaptive learning. This study aimed to predict daily COVID-19 cases and investigate the critical factors that increase the transmission rate of this outbreak by examining different influential factors. Furthermore, the study analyzed the effectiveness of COVID-19 prevention measures. A fully connected deep neural network, long short-term memory (LSTM), and transformer model were used as the AI models for the prediction of new COVID-19 cases. Initially, data preprocessing and feature extraction were performed using COVID-19 datasets from Saudi Arabia. The performance metrics for all models were computed, and the results were subjected to comparative analysis to detect the most reliable model. Additionally, statistical hypothesis analysis and correlation analysis were performed on the COVID-19 datasets by including features such as daily mobility, total cases, people fully vaccinated per hundred, weekly hospital admissions per million, intensive care unit patients, and new deaths per million. The results show that the LSTM algorithm had the highest accuracy of all the algorithms and an error of less than 2%. The findings of this study contribute to our understanding of COVID-19 containment. This study also provides insights into the prevention of future outbreaks.

Delay Analysis in IoT Sensor Networks.

Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other applications include underwater communication with high attenuation of radio waves, disaster relief networks, rural networking, environmental monitoring networks, and vehicular networks. These networks all share the same characteristics, including link latency, latency variation (jitter), and tail latency. Achieving a predictable performance is critical for many interactive and latency-sensitive applications. In this paper, a two-stage tandem queuing model is developed to estimate the average end-to-end latency and predict the latency variation in closed forms. This model also provides a feedback mechanism to investigate other major performance metrics, such as utilization, and the optimal number of computing units needed in a single cluster. The model is applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs). While the proposed model is theoretically derived from a queuing-based model, the simulation results of various network topologies and under different traffic conditions prove the accuracy of our model.