Magnetic resonance imaging of the pulsing brain: a systematic review.

OBJECTIVE: To perform a systematic review of the literature exploring magnetic resonance imaging (MRI) methods for measuring natural brain tissue pulsations (BTPs) in humans. METHODS: A prospective systematic search of MEDLINE, SCOPUS and OpenGrey databases was conducted by two independent reviewers using a pre-determined strategy. The search focused on identifying reported measurements of naturally occurring BTP motion in humans. Studies involving non-human participants, MRI in combination with other modalities, MRI during invasive procedures and MRI studies involving externally applied tests were excluded. Data from the retrieved records were combined to create Forest plots comparing brain tissue displacement between Chiari-malformation type 1 (CM-I) patients and healthy controls using an independent samples t-test. RESULTS: The search retrieved 22 eligible articles. Articles described 5 main MRI techniques for visualisation or quantification of intrinsic brain motion. MRI techniques generally agreed that the amplitude of BTPs varies regionally from 0.04 mm to ~ 0.80 mm, with larger tissue displacements occurring closer to the centre and base of the brain compared to peripheral regions. Studies of brain pathology using MRI BTP measurements are currently limited to tumour characterisation, idiopathic intracranial hypertension (IIH), and CM-I. A pooled analysis confirmed that displacement of tissue in the cerebellar tonsillar region of CM-I patients was + 0.31 mm [95% CI 0.23, 0.38, p < 0.0001] higher than in healthy controls. DISCUSSION: MRI techniques used for measurements of brain motion are at an early stage of development with high heterogeneity across the methods used. Further work is required to provide normative data to support systematic BTPs characterisation in health and disease.

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach.

In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study's objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.

Evaluating the operational resilience of small and medium-sized enterprises to flooding using a computational modelling and simulation approach: a case study of the 2007 flood in Tewkesbury.

The resilience of small and medium-sized enterprises (SMEs) to disruptive events is significant as this highly prevalent category of business forms the economic backbone in developed countries. This article provides an overview of the application of a computational modelling and simulation approach to evaluate SMEs' operational resilience to flooding based on combinations of structural and procedural mitigation measures that may be implemented to improve their premises' resistance to flooding and safeguard their business continuity. The approach integrates flood modelling and simulation with agent-based modelling and simulation (ABMS) within a modelled geographical environment. SMEs are modelled as agents based on findings of semi-structured interviews with SMEs that have experienced flooding or are at risk of flooding. In this paper, the ABMS has been applied to a new case study of the major flood event of 2007 in Tewkesbury. Furthermore, to enable an evaluation of the operational resilience of manufacturing SMEs in terms of the relative effectiveness of flood mitigation measures, a new coefficient based on production loss is introduced. Results indicate structural mitigation measures are more effective than procedural measures. While this result is intuitive, the approach provides a means of evaluating the relative effectiveness of combinations of mitigation measures that SMEs may implement to enhance their operational resilience to flooding. This article is part of the theme issue 'Urban flood resilience'.

Fruit and vegetable leaf disease recognition based on a novel custom convolutional neural network and shallow classifier.

Fruits and vegetables are among the most nutrient-dense cash crops worldwide. Diagnosing diseases in fruits and vegetables is a key challenge in maintaining agricultural products. Due to the similarity in disease colour, texture, and shape, it is difficult to recognize manually. Also, this process is time-consuming and requires an expert person. We proposed a novel deep learning and optimization framework for apple and cucumber leaf disease classification to consider the above challenges. In the proposed framework, a hybrid contrast enhancement technique is proposed based on the Bi-LSTM and Haze reduction to highlight the diseased part in the image. After that, two custom models named Bottleneck Residual with Self-Attention (BRwSA) and Inverted Bottleneck Residual with Self-Attention (IBRwSA) are proposed and trained on the selected datasets. After the training, testing images are employed, and deep features are extracted from the self-attention layer. Deep extracted features are fused using a concatenation approach that is further optimized in the next step using an improved human learning optimization algorithm. The purpose of this algorithm was to improve the classification accuracy and reduce the testing time. The selected features are finally classified using a shallow wide neural network (SWNN) classifier. In addition to that, both trained models are interpreted using an explainable AI technique such as LIME. Based on this approach, it is easy to interpret the inside strength of both models for apple and cucumber leaf disease classification and identification. A detailed experimental process was conducted on both datasets, Apple and Cucumber. On both datasets, the proposed framework obtained an accuracy of 94.8% and 94.9%, respectively. A comparison was also conducted using a few state-of-the-art techniques, and the proposed framework showed improved performance.

Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.

This review article offers a comprehensive analysis of current developments in the application of machine learning for cancer diagnostic systems. The effectiveness of machine learning approaches has become evident in improving the accuracy and speed of cancer detection, addressing the complexities of large and intricate medical datasets. This review aims to evaluate modern machine learning techniques employed in cancer diagnostics, covering various algorithms, including supervised and unsupervised learning, as well as deep learning and federated learning methodologies. Data acquisition and preprocessing methods for different types of data, such as imaging, genomics, and clinical records, are discussed. The paper also examines feature extraction and selection techniques specific to cancer diagnosis. Model training, evaluation metrics, and performance comparison methods are explored. Additionally, the review provides insights into the applications of machine learning in various cancer types and discusses challenges related to dataset limitations, model interpretability, multi-omics integration, and ethical considerations. The emerging field of explainable artificial intelligence (XAI) in cancer diagnosis is highlighted, emphasizing specific XAI techniques proposed to improve cancer diagnostics. These techniques include interactive visualization of model decisions and feature importance analysis tailored for enhanced clinical interpretation, aiming to enhance both diagnostic accuracy and transparency in medical decision-making. The paper concludes by outlining future directions, including personalized medicine, federated learning, deep learning advancements, and ethical considerations. This review aims to guide researchers, clinicians, and policymakers in the development of efficient and interpretable machine learning-based cancer diagnostic systems.

DNA encoding schemes herald a new age in cybersecurity for safeguarding digital assets.

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation.

Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0.

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning models without accessing the train data at a single manufacturing facility. We provide a self-adaptive framework for federated machine learning of healthcare intelligent systems in this research. Our method takes into account the participating parties at various levels of healthcare ecosystem abstraction. Each hospital trains its local model internally in a self-adaptive style and transmits it to the centralized server for universal model optimization and communication cycle reduction. To represent a multi-task optimization issue, we split the dataset into as many subsets as devices. Each device selects the most advantageous subset for every local iteration of the model. On a training dataset, our initial study demonstrates the algorithm's ability to converge various hospital and device counts. By merging a federated machine-learning approach with advanced deep machine-learning models, we can simply and accurately predict multidisciplinary cancer diseases in the human body. Furthermore, in the smart healthcare industry 5.0, the results of federated machine learning approaches are used to validate multidisciplinary cancer disease prediction. The proposed adaptive federated machine learning methodology achieved 90.0%, while the conventional federated learning approach achieved 87.30%, both of which were higher than the previous state-of-the-art methodologies for cancer disease prediction in the smart healthcare industry 5.0.

Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence.

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.

The awareness of public about the Emergency Medical Services in the Eastern region of Saudi Arabia.

Emergency Medical Services (EMS) are crucial for immediate medical assistance during life-threatening situations. However, insufficient public awareness about EMS services can impede their effectiveness. This study aimed to assess EMS knowledge and trust among the population of Eastern Saudi Arabia while identifying factors contributing to low awareness. A cross-sectional study was conducted in Eastern Saudi Arabia from September 2022 to September 2023. The study included participants aged 18 to 60 from diverse backgrounds. Using a convenience sampling approach, data was collected using a validated questionnaire covering demographics, hypothetical scenarios, EMS knowledge, and trust in EMS. We conducted the Chi-square tests and logistic regression using Jamovi software, with significance levels set at p < 0.05. Our study yielded 435 participants; 55% were males. Gender-based analysis showed significant differences in responses regarding first aid provision and EMS services (P < 0.001). Expectations for EMS response times also varied by gender (P = 0.01). Knowledge-based analysis revealed that age and education significantly influenced EMS knowledge (P < 0.001). Respondents with EMS knowledge were more likely to know how to provide first aid, understand the importance of emergency number 112, and trust EMS (P < 0.001). Trust-based analysis showed age and education-related differences in EMS trust (P < 0.001). Respondents with EMS knowledge and awareness of emergency numbers displayed higher trust in EMS (P < 0.001). This study underscores the need for enhanced public awareness of EMS services in Eastern Saudi Arabia. Age, education, and gender emerged as critical factors affecting EMS knowledge and trust. Bridging this awareness gap necessitates tailored educational campaigns and continuous monitoring. Policymakers should prioritise EMS awareness within broader healthcare strategies, contributing to improved public health outcomes and community well-being.

Epidemiology and outcomes of out of hospital cardiac arrest in Saudi Arabia: Findings from the Saudi Out of Hospital cardiac Arrest Registry (SOHAR).

Aim: The Saudi Out-of-Hospital Cardiac Arrest Registry (SOHAR) is the first out-of-hospital cardiac arrest (OHCA) registry in Saudi Arabia. This study aimed to describe the epidemiology and outcomes of OHCA in Saudi Arabia. Methods: The SOHAR is a prospective data collection system. Data were collected monthly from defined regions, and registry measured variables were adopted from the Utstein recommendations. Results: During the period from 01/01/2019 to 31/12/2022, 3671 patients were included in the registry. The mean age was 62 years, and 6.5% (240) of patients were under the age of 18 years. The most common cause of OHCA was medical 3439 (93.6%). A total of 641 (17.4%) and 129 (3.9%) had presumed cardiac and respiratory causes. Additionally, most OHCA in Saudi Arabia (3034, 82.6%) occurred at home. Prehospital Return Of Spontaneous Circulation (ROSC) was achieved in 275 (7.4%) cases, and 491 (13.3%) patients were pronounced dead upon arrival at the hospital. Survival to hospital discharge was achieved in 107 (2.9%) of the cases, and good neurological outcomes, defined as a Cerebral Performance Category (CPC) of 1-3, occurred in < 0.5% of patients. Conclusion: The Saudi out-of-hospital ROSC was 7.4%. The survival to hospital discharge rate was 2.9%, and less than 1% of patients were discharged with good neurological outcomes. Further research and the continuation of registry data collection is highly recommended. Additionally, a national-level out-of-hospital cardiac arrest system is recommended to ensure the standardization of medical care provided to patients with OHCA.

Lipid-Lowering Medications for Managing Dyslipidemia: A Narrative Review.

Dyslipidemia refers to the change in the normal levels of one or more lipid components in the bloodstream, which include triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Dyslipidemia represents a substantial source of danger for cardiovascular disease (CVD). Effectively managing dyslipidemia involves a thorough strategy that includes changing one's lifestyle and using medications that are specifically designed to target the complex processes involved in lipid metabolism. Lipid-lowering treatments play a crucial role in this approach, providing a wide range of medications that are developed to specifically target different components of dyslipidemia. Statins are the main drug among these medications. Other drugs that are used with statin or as monotherapy include fibrates, omega-3 fatty acids (OM3FAs), ezetimibe, bile acid sequestrants, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, and bempedoic acid. Using the PubMed database, we reviewed the literature about dyslipidemia, drugs used for treating dyslipidemia, their efficacy parameters, and common adverse events. We also reviewed the international guidelines for treating dyslipidemia and discussed the future of lipid-lowering medications. More trials and experiments are still required to verify the effectiveness of many lipid-lowering drugs and to know their common adverse events to be able to manage them properly.

The Effect of Adjunctive Use of Hyaluronic Acid on Prevalence of Porphyromonas gingivalis in Subgingival Biofilm in Patients with Chronic Periodontitis: A Systematic Review.

Porphyromonas gingivalis (P. gingivalis) is a Gram-negative anaerobic bacterium that plays an important role in the development and progression of periodontitis. Hyaluronic acid (HA) is a naturally occurring glycosaminoglycan that has previously demonstrated antibacterial potential in vitro against multiple bacterial species, including P. gingivalis. The purpose of this systematic review is to evaluate the effectiveness of HA as an adjunctive topical antibacterial agent to non-surgical mechanical therapy of periodontitis in reducing the prevalence of P. gingivalis in subgingival biofilms. Five clinical studies were identified that satisfied the eligibility criteria. Only three trials were suitable for the meta-analysis as they provided data at three and six months. Data on the prevalence of P. gingivalis in each study were collected. The odds ratio (OR) for measuring the effect size with a 95% confidence interval (CI) was applied to the available data. The results did not favor the use of HA during non-surgical mechanical therapy to reduce the prevalence of P. gingivalis in subgingival biofilm (odd ratio = 0.95 and 1.11 at three and six months, consecutively). Within their limitations, the current data do not indicate an advantage for using HA during mechanical periodontal therapy to reduce the prevalence of P. gingivalis.

Frailty Identification in Prehospital Care: A Scoping Review of the Literature.

The proportion of older adults is increasing worldwide. Frailty assessment in prehospital care was suggested to improve triage decisions and paramedics' judgment. This study aimed to assess the scope and nature of available evidence around frailty identification in prehospital care. A systematic search of the literature was performed using MEDLINE, SCOPUS, CINHAL, and Web of Science to identify relevant articles published from January 2022 downwards. A list of indexed terms and their associated alternatives were pre-determined. Of the 71 identified and reviewed articles after removing duplicates, six articles were included in the review. Due to the heterogeneity of the included articles, the findings were described narratively. The findings of this review showed that the available evidence is limited and heterogenic. Two themes emerged from the findings of the included articles: 1) Paramedics' Perceptions about Frailty Assessment in Prehospital Care and 2) Frailty Scores for Application in Prehospital Care. Paramedics recognised frailty assessment in pre-hospital care to be feasible and important. They highlighted the need for a simple and clear frailty score that could be used and mentioned to other healthcare professionals when handing over patients. Six frailty scores were reported to be used in prehospital care. The evidence around each frailty score is very limited. Overall, frailty assessment in prehospital care was shown to be important and feasible. Different frailty scores have been assessed for use in prehospital care. Further research investigating frailty identification in prehospital care is needed.

Artificial Intelligence Model for Alzheimer's Disease Detection with Convolution Neural Network for Magnetic Resonance Images.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease that drastically affects brain cells. Early detection of this disease can reduce the brain cell damage rate and improve the prognosis of the patient to a great extent. The patients affected with AD tend to depend on their children and relatives for their daily chores. OBJECTIVE: This research study utilizes the latest technologies of artificial intelligence and computation power to aid the medical industry. The study aims at early detection of AD to enable doctors to treat patients with the appropriate medication in the early stages of the disease condition. METHODS: In this research study, convolutional neural networks, an advanced deep learning technique, are adopted to classify AD patients with their MRI images. Deep learning models with customized architecture are precise in the early detection of diseases with images retrieved by neuroimaging techniques. RESULTS: The convolution neural network model classifies the patients as diagnosed with AD or cognitively normal. Standard metrics evaluate the model performance to compare with the state-of-the-art methodologies. The experimental study of the proposed model shows promising results with an accuracy of 97%, precision of 94%, recall rate of 94%, and f1-score of 94%. CONCLUSION: This study leverages powerful technologies like deep learning to aid medical practitioners in diagnosing AD. It is crucial to detect AD early to control and slow down the rate at which the disease progresses.

SHMAD: A Smart Health Care System to Monitor Alzheimer's Disease Patients.

BACKGROUND: In the digital era monitoring the patient's health status is more effective and consistent with smart healthcare systems. Smart health care facilitates secure and reliable maintenance of patient data. Sensors, machine learning algorithms, Internet of things, and wireless technology has led to the development of Artificial Intelligence-driven Internet of Things models. OBJECTIVE: This research study proposes an Artificial Intelligence driven Internet of Things model to monitor Alzheimer's disease patient condition. The proposed Smart health care system to monitor and alert caregivers of Alzheimer's disease patients includes different modules to monitor the health parameters of the patients. This study implements the detection of fall episodes using an artificial intelligence model in Python. METHODS: The fall detection model is implemented with data acquired from the IMU open dataset. The ensemble machine learning algorithm AdaBoost performs classification of the fall episode and daily life activity using the feature set of each data sample. The common machine learning classification algorithms are compared for their performance on the IMU fall dataset. RESULTS: AdaBoost ensemble classifier exhibits high performance compared to the other machine learning algorithms. The AdaBoost classifier shows 100% accuracy for the IMU dataset. This high accuracy is achieved as multiple weak learners in the ensemble model classify the data samples in the test data accurately. CONCLUSIONS: This study proposes a smart healthcare system for monitoring Alzheimer's disease patients. The proposed model can alert the caregiver in case of fall detection via mobile applications installed in smart devices.

Evaluation of the psychological distress and mental well-being of pre-hospital care providers in Saudi Arabia during COVID-19.

Background: Pre-hospital care providers are the first line of contact when emergencies occur. They are at high risk of mental health disorders associated with trauma and stress. The magnitude of their stress could increase during difficult times such as the COVID-19 pandemic. Objectives: This study reports on the state of mental well-being and the degree of psychological distress among pre-hospital care workers (paramedics, emergency medical technicians, doctors, paramedic interns and other healthcare practitioners) during the COVID-19 pandemic in Saudi Arabia. Methods: The study was a cross-sectional survey study in Saudi Arabia. A questionnaire was distributed among pre-hospital care workers in Saudi Arabia during the first wave of the COVID-19 pandemic. The questionnaire was based on the Kessler Psychological Distress Scale (K10) and the World Health Organization Well-Being Index (WHO-5). Results: In total, 427 pre-hospital care providers completed the questionnaire; 60% of the respondents had scores of more than 30 in the K10 and were likely to have a severe disorder. The WHO-5 showed a similar percentage of respondents with a score of more than 50 and coded as having poor well-being. Conclusions: The findings of this study provide evidence around mental health and well-being for pre-hospital care workers. They also highlight the need to better understand the quality of mental health and well-being for this population and to provide appropriate interventions to improve their quality of life.

Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model.

The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.

High molecular weight hyaluronic acid reduces the growth and biofilm formation of the oral pathogen Porphyromonas gingivalis.

Background: Porphyromonas gingivalis (P. gingivalis) is viewed as a keystone microorganism in the pathogenesis of periodontal and peri-implant diseases. Hyaluronic acid (HA) is believed to exert antimicrobial activity. The aim of this study is to assess the in-vitro growth and biofilm formation of P. gingivalis under HA and compare the effect of HA to that of azithromycin (AZM) and chlorhexidine (CHX). Materials and methods: In each material, the minimum inhibitory concentration (MIC), 50% MIC, 25% MIC, and 12.5% MIC were tested. The growth of P. gingivalis was evaluated by absorbance spectrophotometry after 48 h. A biofilm inhibition assay was performed on a 72-hour culture by washing planktonic bacterial cells, fixing and staining adherent cells, and measuring the variation in stain concentrations relative to the untreated control using absorbance spectrophotometry. Results: The results show that the overall growth of P. gingivalis after 48 h was 0.048 ± 0.030, 0.008 ± 0.013, and 0.073 ± 0.071 under HA, AZM, and CHX, respectively, while the untreated control reached 0.236 ± 0.039. HA was also able to significantly reduce the biofilm formation of P. gingivalis by 64.30 % ± 22.39, while AZM and CHX reduced biofilm formation by 91.16 %±12.58 and 88.35 %±17.11, respectively. Conclusions: High molecular-weight HA significantly inhibited the growth of P. gingivalis. The overall effect of HA on the growth of P. gingivalis was similar to that of CHX but less than that of AZM. HA was also able to significantly reduce the biofilm formation of P. gingivalis. However, the ability of HA to prevent the biofilm formation of P. gingivalis was generally less than that of both AZM and CHX.

Knowledge and Attitudes Regarding Clinical Trial Participation: A Cross-Sectional Study in the Eastern Province, Saudi Arabia.

Objective This study aims to assess the knowledge and attitudes toward clinical trial (CT) participation among the adult population in the Eastern Province of Saudi Arabia. Material and methods This cross-sectional study was conducted among the population of the Eastern Province of Saudi Arabia. A self-administered questionnaire was distributed among the general population using an online survey. Results A total of 334 participants completed the questionnaire. Participants' ages ranged from 18 to 65 years, with a mean age of 31.2 ± 13.9 years, 56.6% of whom were males, 42.2% were employed, 29.6% were students, and 23.1% were unemployed. Surprisingly, only a small percentage of respondents (7.5%) were requested to participate in a randomized controlled trial (RCT), of which the majority did partake. Additionally, 25.4% of participants believe CTs are used to evaluate new drugs; others believe that CTs are used to understand diseases and human behavior. The data show that most participants believe that CTs improve patient care, welfare, and society. Also, participants were more likely to take part if they were aware of the study's purpose and findings and were given more time to consider their options. Conclusion Participants believed that the biggest obstacle was a lack of knowledge of CTs. It is crucial to educate patients more about CTs. Multimodal strategies such as improved patient-provider communication and online information for trial information sharing may be effective in boosting knowledge and CT recruitment.

COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach.

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%.