Impact of temporomandibular disorders on oral health-related quality of life: A systematic review and meta-analysis.

BACKGROUND: The management of temporomandibular disorders (TMDs) requires a comprehensive approach that considers multiple factors, including the impact of oral health-related quality of life (OHRQoL). Through this investigation we aim to assess the impact of OHRQoL played in a TMD-afflicted individual. METHODS: Using keywords relevant to our research, such as "Oral health related quality of life," "Oral hygiene," "Temporomandibular joint" and "Temporomandibular disorders," a comprehensive search across multiple online databases was carried out, yielding a total of 632 studies at the preliminary stage of the review. Modified New Castle Ottawa scale was used to assess the quality of studies included. RESULTS: Eight studies were included in the review, out of which six were eligible for further meta-analysis. The studies included in this review employed various OHRQoL measures, including the Oral Health Impact Profile-14 (OHIP-14), the Short-Form 36 Health Survey (SF-36) and the OHIP- 49. All the studies demonstrated significant effect of TMDs on the OHRQoL of the target population under study. CONCLUSION: The impact of OHRQoL on the management of TMD was deemed to be significant. The comprehensive management of TMD should consider the impact of the condition on the individual's daily life and incorporate interventions that address both the physical and psychological aspects of the condition. By improving OqL, individuals with TMD can experience improved overall well-being and quality of life.

An architecture for COVID-19 analysis and detection using big data, AI, and data architectures.

The COVID-19 epidemic is affecting individuals in many ways and continues to spread all over the world. Vaccines and traditional medical techniques are still being researched. In diagnosis and therapy, biological and digital technology is used to overcome the fear of this disease. Despite recovery in many patients, COVID-19 does not have a definite cure or a vaccine that provides permanent protection for a large number of people. Current methods focus on prevention, monitoring, and management of the spread of the disease. As a result, new technologies for combating COVID-19 are being developed. Though unreliable due to a lack of sufficient COVID-19 datasets, inconsistencies in the datasets availability, non-aggregation of the database because of conflicting data formats, incomplete information, and distortion, they are a step in the right direction. Furthermore, the privacy and confidentiality of people's medical data are only partially ensured. As a result, this research study proposes a novel, cooperative approach that combines big data analytics with relevant Artificial Intelligence (AI) techniques and blockchain to create a system for analyzing and detecting COVID-19 instances. Based on these technologies, the reliability, affordability, and prominence of dealing with the above problems required time. The architecture of the proposed model will analyze different data sources for preliminary diagnosis, detect the affected area, and localize the abnormalities. Furthermore, the blockchain approach supports the decentralization of the central repository so that it is accessible to every stakeholder. The model proposed in this study describes the four-layered architecture. The purpose of the proposed architecture is to utilize the latest technologies to provide a reliable solution during the pandemic; the proposed architecture was sufficient to cover all the current issues, including data security. The layers are unique and individually responsible for handling steps required for data acquisition, storage, analysis, and reporting using blockchain principles in a decentralized P2P network. A systematic review of the technologies to use in the pandemic covers all possible solutions that can cover the issue best and provide a secure solution to the pandemic.

AMDDLmodel: Android smartphones malware detection using deep learning model.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.

In Vitro Evaluation of Lithium Disilicate Endocrowns and Post and Core Crowns-A Systematic Review.

The aim of this systematic review was to summarize the results of the studies that have compared the physical and mechanical properties of lithium disilicate (LDS) endocrowns constructed for posterior teeth to those retained by post-and-core retention systems. The review was conducted following the PRISMA guidelines. The electronic search process was conducted on PubMed-Medline, Scopus, Embase and ISI Web of Knowledge (WoS) from the earliest available date till 31 January 2023. Additionally, the studies were assessed for their overall quality and risk of bias using the Quality Assessment Tool For In Vitro Studies (the QUIN). The initial search resulted in 291 articles, out of which, only 10 studies met the eligibility criteria. In all studies LDS endocrowns were compared with various kinds of endodontic posts and crowns made from other materials. There were no definite pattern or trends observed in the fracture strengths of tested specimens. There was no predilection observed in failure patters among the experimental specimens. No predilection was observed in the fracture strengths of LDS endocrowns when compared to post-and-core crowns. Furthermore, no differences in failure patterns could be observed when both types of restorations were compared. The authors propose standardized testing of endocrowns against post-and-core crowns in future studies. In conclusion, long-term clinical trials are advocated to compare the survival, failure and complication rates of LDS endocrowns and post-and-core restorations.

Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features.

The human brain, primarily composed of white blood cells, is centered on the neurological system. Incorrectly positioned cells in the immune system, blood vessels, endocrine, glial, axon, and other cancer-causing tissues, can assemble to create a brain tumor. It is currently impossible to find cancer physically and make a diagnosis. The tumor can be found and recognized using the MRI-programmed division method. It takes a powerful segmentation technique to produce accurate output. This study examines a brain MRI scan and uses a technique to obtain a more precise image of the tumor-affected area. The critical aspects of the proposed method are the utilization of noisy MRI brain images, anisotropic noise removal filtering, segmentation with an SVM classifier, and isolation of the adjacent region from the normal morphological processes. Accurate brain MRI imaging is the primary goal of this strategy. The divided section of the cancer is placed on the actual image of a particular culture, but that is by no means the last step. The tumor is located by categorizing the pixel brightness in the filtered image. According to test findings, the SVM could partition data with 98% accuracy.

Prevalence and Risk Factors of Home Accidents Among Children Under Five Years of Age in Al-Baha, Saudi Arabia.

INTRODUCTION: Internationally, home accidents are the main cause of preventable debilities and death among children and young persons. Many times, children survive accidents with physical or mental damage that curtails their activities in the long term. The most commonly reported accidental injuries include head injuries, open wounds, and poisoning. This study aims to assess the prevalence and factors associated with home accidents among children under five years old in the Al-Baha region, Saudi Arabia. METHODS: A descriptive cross-sectional study was conducted among the community population in the Al-Baha region, Saudi Arabia, targeting all accessible parents who have children under five years old. A convenience sampling technique was used for sample collection during the period of three months (May 2023 to July 2023), where all accessible parents who fulfilled the inclusion criteria and agreed to participate were invited to fill out the received online study questionnaire. Section 1 covered the participants' demographic data. The second section covered the children's data and the third section included questions about home accident types, frequency, severity, and causes.  Results: The findings showed that 205 (58.2%) study parents reported a history of home accidents among their children. As for accident data, about 122 (59.5%) of the injured children were males. The most reported home accidents among children were fall/impact with hard objects (58.2%), burn (30.7%), asphyxia (27.6%), and poisoning (24.4%). Families with more than seven members and those with four or more siblings significantly experienced higher home accidents than others (p<0.001). CONCLUSION: In conclusion, the current study showed that home accidents among children under five years of age were mainly falls and burns; they were mainly found among male children and children in families with highly educated mothers and many kids. A majority of the reported cases of home accidents were less severe and the hospitalization rates with complications were very few.