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Background The Central Sensitization Inventory (CSI) is a patient-reported screening instrument that can be used to identify and assess central sensitization (CS)/Central Sensitization Syndrome (CSS)-related symptoms. Objective The aim was to translate the CSI into Arabic (CSI-Ar) and to subsequently validate its psychometric properties. Design Cross-sectional. Methods The CSI was translated and cross-culturally adapted into Arabic, and validated following international standardized guidelines. This study included patients with chronic musculoskeletal pain (n = 264) and healthy control participants (n = 56). Patients completed the CSI-Ar, Pain Catastrophizing Scale (PCS), Depression, Anxiety, and Stress scale (DASS-21), Tampa Scale of Kinesiophobia (TSK), and 5-level EuroQol-5D (EQ-5D). Patients completed the CSI-Ar twice to assess test–retest reliability. To evaluate discriminative validity, healthy controls participants completed the CSI-Ar. Statistical analyses were conducted to test the internal consistency, reliability, and structural, construct and discriminant validity of CSI-Ar. Results The CSI-Ar showed acceptable internal consistency (Cronbach’s alpha = 0.919) and excellent test–retest reliability (intraclass correlation coefficient = 0.874). The CSI-Ar scale had significant correlations (P < 0.001) with all PCS subscales and total score (Spearman’s rho = 0.459–0.563, P < 0.001), all DASS-21 subscales and total score (Spearman’s rho = 0.599–0.685, P < 0.001), the TSK (Spearman’s rho = 0.395, P < 0.001), and the EQ-5D (Spearman’s rho = −0.396, P < 0.001). The Mann-Whitney U-test showed a statistically significant difference between the patient group and the healthy control group (P < 0.001), with the healthy controls displaying a lower average CSI-Ar score (12.27 ± 11.50) when compared to the patient group (27.97 ± 16.08). Factor analysis indicated that the CSI-Ar is a unidimensional tool. Conclusion The CSI-Ar is a reliable and valid screening tool that can be used to assess CS/CSS-related symptoms in Arabic-speaking people with chronic musculoskeletal pain.

الملخص: أهداف البحث: سعت هذه الدراسه ألي تحديد الأرتباط والفرق، إن وجد، بين الجانب المماثل وبين الجانبين المتقابلين أثناء تمرين التوازن والتحمل الثابت. طرق البحث: شارك ١١٢ طالب جامعي يتمتع بصحه جيده (٥٥ إناث، ٥٧ ذكور) في هذه الدراسه. أجري كل مشارك تمرين Quadruped bird dog ( رفع ذراع واحده والساق في الجانب المقابل) والحفاظ علي التوازن في حاله ثابته. كما أجري كل مشارك تمرين ال side bridge من وضع الأستلقاء علي الجانب ورفع الجذع والوركين والحفاظ علي التوازن لأطول فتره ممكنه. النتائج: أظهر أختبار Wilcoxon فرق ذو دلاله أحصائيه لتمرين Quadruped bird dog وليس لتمرين Side bridge. كما أظهرت الدراسه معامل أرتباط قوي مقداره 0.85 و 0.75 لتمرين Quadruped bird dog وتمرين Side bridge علي التوالي. الأستنتاجات: قد تكون هذه النتائج مفيده لأخصائي التأهيل للتقييم الموضوعي لقدرات التوازن والتحمل الثابت ومتابعة التقدم في العلاج. كما أنه يساعد في تحديد التمرين المناسب والمتدرج لثبات الجذع. Abstract: Objectives: This study sought to determine the association and the difference, if any, between the levels of the contralateral and between the levels of the ipsilateral sides during static endurance-balance exercise. Methods: One hundred twelve healthy active- college students (55 females and 57 males) participated in this cross-sectional study. Each participant performed the contralateral (raising one arm and opposite-side leg) quadruped bird dog exercise and balanced in static condition. Side bridge exercise was performed from lying on your side then engaging your core muscles and lifting your upper body and hips off the ground, maintaining a straight line and holding this position as long as tolerated. Results: Wilcoxon signed rank test showed significant difference (p = 0.004) between the contralateral right and left quadruped bird dog but insignificant difference (p = 0.059) between the ipsilateral right and left side bridge endurance-balance exercises. Mann–Whitney U test showed that the holding time was significant across gender for the contralateral but was insignificant for the ipsilateral endurance-balance exercise. Mann–Whitney U test was insignificant (p > 0.05) between those being recreationally active or inactive. Kruskal–Wallis test revealed insignificant difference between body mass index categories. Spearman's rho correlation coefficient showed strong positive correlation equals 0.85 and 0.75 (p

AbstractProtein S (PS) is a vital endogenous anticoagulant. It plays a crucial role in regulating coagulation by acting as a cofactor for the activated protein C (APC) and tissue factor pathway inhibitor (TFPI) pathways. Additionally, it possesses direct anticoagulant properties by impeding the intrinsic tenase and prothrombinase complexes. Protein S oversees the coagulation process in both the initiation and propagation stages through these roles. The significance of protein S in regulating blood clotting can be inferred from the significant correlation between deficits in protein S and an elevated susceptibility to venous thrombosis. This is likely because activated protein C and tissue factor pathway inhibitor exhibit low efficacy as anticoagulants when no cofactors exist. The precise biochemical mechanisms underlying the roles of protein S cofactors have yet to be fully elucidated. Nevertheless, recent scientific breakthroughs have significantly enhanced comprehension findings for these functions. The diagnosis of protein S deficiency, both from a technical and genetic standpoint, is still a subject of debate due to the complex structural characteristics of the condition. This paper will provide an in-depth review of the molecular structure of protein S and its hemostatic effects. Furthermore, we shall address the insufficiency of protein S and its methods of diagnosis and treatment.

Recent advances in generative artificial intelligence (GenAI), particularly large language models (LLMs), are profoundly impacting many fields. In chemical engineering, GenAI plays a pivotal role in the design, scale-up, and optimization of chemical and biochemical processes. The natural language understanding capabilities of LLMs enable the interpretation of complex chemical and biological data. Given the rapid developments of GenAI, this paper explores the extensive applications of GenAI in multiscale chemical engineering, spanning from quantum mechanics to macro-level optimization. At quantum and molecular levels, GenAI accelerates the discovery of novel products and enhances the understanding of fundamental phenomena. At larger scales, GenAI improves process design and operational efficiency, contributing to sustainable practices. We present several examples to demonstrate the role of GenAI, including its impact on nanomaterial hardness enhancement, novel catalyst generation, protein design, and the development of autonomous experimental platforms. This multiscale integration demonstrates the potential of GenAI to address complex challenges, drive innovation, and foster advancements in chemical engineering.

Many people still struggle with quitting smoking despite available treatment options, making it one of the most significant public health challenges that our society faces. The use of electronic cigarettes (E-cigarettes) has become increasingly popular among people who are seeking to quit smoking. The objective of this review paper is to present a comprehensive analysis of the mechanisms, several types, and impact of E-cigarettes, along with supporting evidence indicating their efficacy in aiding smokers to quit tobacco usage. Additionally, the review discusses recent developments in the treatment of smoking cessation, which include conventional smoking cessation methods. Also, the review discusses the challenges, potential risks, ethical considerations, and controversies surrounding the use of E-cigarettes. The present review presents a comprehensive examination of the existing methods and approaches employed in smoking cessation, including the emerging utilization of E-cigarettes as an effective option in smoking cessation. It explores their efficacy as a valuable instrument in promoting smoking cessation.

Brain tumors pose a significant threat, especially when not detected early. The Inception v3 machine learning model has found extensive applications in computer vision and related fields. This study aims to develop a robust transfer learning model for classification, adaptable to various data modalities through neural networks. However, the training process for these neural networks is complex, being both demanding and computationally intensive. To tackle this challenge, we introduce an innovative training approach for Inception v3 referred to as ‘PSO-INCEPT’ (Particle Swarm Optimization-based Inception v3 training). In this method, the weight vectors for each Inception v3 model are analogized to particle positions in a phase space. The PSO cooperates with the ADAM optimizer in achieving the purpose of training with the best performance and generalization. This research is composed of two main parts, the first stage is being performed by the model independently using the ADAM optimizer. In the following stage, PSO-INCEPT models share the latest weight vectors or particle coordinates and loss function approximations via training. The optimization function then uses them to improve the validation accuracy. The effectiveness of PSO-INCEPT was evaluated through experiments that were conducted on Kaggle datasets that provide a common base ground by having four distinct classes. Experimental studies have proven the extraordinary ability of the proposed model by providing 99.33% validation accuracy and 99.95% training accuracy which shows exceptional performance.

A growing kind of communication, the Internet of Things (IoT), links all network-capable devices worldwide. The most recent phase of the Internet of Things (IoT), known as the Internet of Medical Things (IoMT), is one that is rapidly catching researchers’ interest. The centralized storage system houses the substantial amount of medical data created by IoMT. However, centralizing sensitive patient data creates a single point of failure and raises privacy and security issues. To assure honesty, reliability, and safety while determining an entity’s trustworthiness, trust information is required to be safely transmitted and preserved. We propose a safe trust management solution based on blockchain technology. The proposed solution gathers node trust data using both time- and event-driven methods that are used to compute the trust of a node based on the threshold value. We also confirm the validity of data by varying the threshold value during transmission and authentication and employing a membership mechanism for authentication. The trust scores for each node are securely stored in an array, and the Maximum subarray (Kadane) Algorithm is used to compute the threshold value. The blockchain network receives and stores the IoMT score in the trusted list. Due to its distributed structure, capacity to maintain safeguards, and resiliency against a variety of threats with low overhead. Our approach is viable, deployable, and appropriate and also includes safety features like tamper-proofing, attack resistance, dependability, and low capabilities for IoT in smart hospitals.

Parkinson’s disease affects bodily functions and there is a growing need for advanced solutions to offer therapeutic advice to patients. A framework using artificial intelligence and machine learning techniques has been proposed to address this. The proposed system employs a combination of RGB, inertial, and depth sensors data. The inertial signals have been filtered using a notch filter to obtain the optimal wearable sensor data by examining the upper and lower cutoff frequencies. Multiple features have been calculated, including mel frequency cepstral coefficients (MFCC), statistical features and Gaussian mixture model (GMM) features. On the other hand, silhouettes have been extracted from RGB and depth images, and four crucial parameters have been employed to gauge the level of accuracy with which patients with neurological disorders performed their activities, including the angle formation between the hands and lower/upper half of the body and center of the body, and the angle between the hands of the silhouette. The resulting features have been fused and classified using principle component analysis and a reweighted genetic algorithm. Evaluation using cross-validation on mRI (multi-modal 3d human pose estimation dataset using mmwave, RGB-D, and inertial sensors) and MHEALTH (mobile health) datasets showed a recognition accuracy rate of 97.29% and a 97.94%. The study highlights the need for more datasets to address challenges in rehabilitation using human activity recognition with multi-modal sensors.

The Industrial Internet of Things (IIoT) comprises a variety of systems, smart devices, and an extensive range of communication protocols. Hence, these systems face susceptibility to privacy and security challenges, making them prime targets for malicious attacks that can result in harm to the overall system. Privacy breach issues are a notable concern within the realm of IIoT. Various intrusion detection systems based on machine learning (ML) and deep learning (DL) have been introduced to detect malicious activities within these networks and identify attacks. However, traditional ML and DL models encounter significant hurdles when faced with highly imbalanced training data and repetitive patterns within network datasets, hampering their performance in distinguishing between various classes of attacks. To overcome the challenges inherent in existing systems, this paper presents a self-attention-based deep convolutional neural network (SA-DCNN) model designed for monitoring the IIoT networks and detecting malicious activities. The SA mechanism computes the significance value for each input feature, and the DCNN processes these parameters to detect IIoT network behavior. Additionally, a two-step cleaning method has been implemented to eliminate redundancy within the training data, considering both intra-class and cross-class samples. Furthermore, to tackle the issue of underfitting, we have employed a mutual information-based feature filtering method. This method ranks all the features in descending order based on their mutual information and subsequently removes the features with negative impact from the dataset. The performance of the SA-DCNN model is assessed using IoTID20 and Edge-IIoTset datasets. Moreover, the proposed study is demonstrated through a comprehensive comparison with other ML and DL models, as well as against relevant studies, showcasing the superior performance and efficacy of the proposed model.

Cervical cancer, the second most prevalent cancer among women worldwide, is primarily attributed to the human papillomavirus (HPV). Despite advances in healthcare, it remains a significant cause of mortality among women across diverse regions, surpassing other hereditary cancers. Early detection is pivotal, as survival rates exceed 90% when the disease is identified in its early stages. In response to this critical need, we introduce WFC2DS (Web Framework for Cervical Cancer Detection System), a novel expert web system specifically designed to revolutionize cervical cancer diagnosis. WFC2DS integrates a sophisticated ensemble of machine learning classification algorithms, including Artificial Neural Network (ANN), AdaBoost, K-Nearest Neighbor (KNN), Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree (DT). This ensemble approach enables a comprehensive analysis of a large dataset comprising information from 858 patients with 36 attributes, with the primary objective being the early detection of cervical cancer, using the last attribute, Biopsy, as the target variable. Our evaluation criteria encompass accuracy, specificity, sensitivity, and the F1 score. Among the algorithms, RFC and DT emerge as the most promising, demonstrating exceptional performance with an accuracy of 98.1% and an F1 score of 0.98. AdaBoost shows an accuracy of 97.4% and an F1 score of 0.98, ANN attains an accuracy of 97.7% and an F1 score of 0.96, SVM achieves an accuracy of 96.2% and an F1 score of 0.96, and KNN reaches an accuracy of 90.6% with an F1 score of 0.91. This research significantly contributes to reducing the global burden of cervical cancer, emphasizing transformative advancements in women’s healthcare. WFC2DS, with its cutting-edge machine learning techniques, not only improves the accuracy of cervical cancer diagnosis but also enhances the overall healthcare landscape for women worldwide.

The rise of Internet of Things (IoT) has led to increased security risks, particularly from botnet attacks that exploit IoT device vulnerabilities. This situation necessitates effective Intrusion Detection Systems (IDS), that are accurate, lightweight, and fast (having less inference time), designed particularly to detect botnet attacks in resource constrained IoT devices. This paper proposes SkipGateNet, a novel deep learning model designed for detecting Mirai and Bashlite botnet attacks in resource constrained IoT and fog computing environments. SkipGateNet is a lightweight, fast model combining 1D-Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) layers. The novelty of this model lies in the integration of ‘Learnable Skip Connections’. These connections feature gating mechanisms that enhance detection by focusing on relevant features and ignoring irrelevant ones. They add adaptability to the architecture, performing feature selection and propagating only essential features to deeper layers. Tested on the N-BaIoT dataset, SkipGateNet efficiently detects ten types of botnet attacks, with a remarkable test accuracy of 99.91%. It is also compact (2596.87 KB) and demonstrates a quick inference time of 8.0 milliseconds, suitable for real-time implementation in resource-limited settings. While evaluating its performance, parameters like precision, recall, accuracy, and F1 score were considered, along with statistical reliability measures like Cohen’s Kappa Coefficient and Matthews Correlation Coefficient. These highlight its reliability and effectiveness in IoT security challenges. The paper also compares SkipGateNet to existing models and four other deep learning architectures, including two sequential CNN architectures, a simple CNN+LSTM architecture, and a CNN+LSTM with standard skip connections. SkipGateNet surpasses all in accuracy and inference time, demonstrating its superiority in addressing IoT security issues.

Background: Doxorubicin (DOX), an anthracycline antibiotic, is a powerful chemotherapeutic agent effective against multiple types of cancer, particularly lung, breast, bladder and hematologic neoplasia (lymphomas and leukemia). However, its therapeutic usage is restricted by its known cardiotoxicity, which is associated with the production of oxidative stress. Enhancing antioxidant capacity represents a promising approach to mitigate DOX-induced cardiotoxicity. Hesperidin (HES), a citrus bioflavonoid, possesses several pharmacological effects, such as anti-inflammatory and antioxidant characteristics. Aim: This study was designed to evaluate the cardiotoxicity of DOX and assess the possible cardioprotective role of HES. Methods: Groups of Wistar rats were either treated with doxorubicin (4 mg/kg. bw., once a week for 5 consecutive weeks, intraperitoneally) or received co-treatment with hesperidin (100 mg/kg. bw./day in distilled water, 5 days in a week for 5 consecutive weeks, administered orally). Heart and blood samples were obtained for histological, immunohistochemical, and biochemical assessments. Results: DOX administration resulted in severe cardiotoxicity, as evidenced by significant elevations in cardiac biomarkers, including Troponin I (CTnI), Creatine kinase (CK-Total), Creatine kinase isoenzyme-MB (CK-MB), lactate dehydrogenase (LDH), and Aspartate aminotransferase (AST). DOX also elevated pro-inflammatory cytokines, such as Interferon γ (IFN-γ), Interleukin 1β (IL-1β), and Tumor necrosis factor α (TNF-α). Furthermore, DOX induced oxidative stress and substantially reduced the levels of antioxidant enzymes, including Glutathione peroxidase (GPX), Superoxide dismutase (SOD), and Catalase (CAT). Histopathologically, DOX caused severe Zenker's necrosis, cardiomyocyte disarray, sarcoplasmic vacuolizations, cardiomyocyte congestion, and inflammatory cell infiltration. Immunohistochemically, DOX exhibited extensive apoptosis, as indicated by strong positive immuno-localization against anti-caspase-3 antibody. In contrast, co-treatment with HES protected cardiac tissues against cardiotoxicity of DOX, as indicated by the amelioration of histological abnormalities and the normalization of biochemical values. Conclusion: We can conclude that DOX induces severe cardiotoxicity characterized by oxidative stress, inflammation, pathological alterations, and apoptosis. Co-treatment with HES demonstrates significant cardioprotective effects by virtue of its potent anti-inflammatory, antioxidant, cytoprotective, and antiapoptotic characteristics. [Open Vet J 2023; 13(12.000): 1718-1728]

Background: This study assessed the beliefs and knowledge of physical therapists in Saudi Arabia regarding the management of patellofemoral pain (PFP) and their alignment with current Clinical Practice Guidelines (CPGs). Methods: A cross-sectional survey was conducted, involving 111 licensed physical therapists actively treating PFP patients. The survey included questions about PFP risk factors, prognosis, diagnosis, and treatment, using a Likert scale to measure confidence and knowledge. Results: Readers have significantly greater confidence in the knowledge of managing patients with PFP following current CPGs (p < 0.01). No significant differences were observed between groups in the other items (p > 0.01). However, over 70% of respondents, irrespective of guideline familiarity, held beliefs about risk factors, prognosis, diagnosis, and treatment that were inconsistent with CPGs. Conclusions: These discrepancies highlight a significant knowledge gap that may affect patient care quality. Enhancing education and dissemination efforts regarding CPGs is essential to improve adherence to evidence-based practices among physical therapists in Saudi Arabia. To change practitioners’ preferences, attitudes, and beliefs, more targeted programs and interventions for knowledge dissemination and implementation should be provided.

Background:Melatonin is responsible for regulating the sleep-wake cycle and circadian rhythms in mammals. Tramadol, a synthetic opioid analgesic, is used to manage moderate to severe pain but has a high potential for abuse and dependence. Studies have shown that melatonin could be a potential modulator to reduce tramadol addiction.Methods:Male Wistar rats were used to investigate the effect of melatonin on tramadol-induced place preference. The rats were divided into four groups: control, tramadol, tramadol + melatonin (single dose), and tramadol + melatonin (repeated doses). Tramadol was administered intraperitoneally at 40 mg/kg, while melatonin was administered at 50 mg/kg for both the single dose and repeated-dose groups. The study consisted of two phases: habituation and acquisition.Results:Tramadol administration produced conditioned place preference (CPP) in rats, indicating rewarding effects. However, melatonin administration blocked tramadol-induced CPP. Surprisingly, repeated doses of melatonin were ineffective and did not reduce the expression of CPP compared to that of the single dose administration.Conclusion:The study suggests that melatonin may be a potential therapeutic option for treating tramadol addiction. The results indicate that melatonin attenuates the expression of tramadol-induced CPP, supporting its uses as an adjunct therapy for managing tramadol addiction. However, further studies are needed to investigate its effectiveness in humans.

Abstract Wireless body area networks (WBAN) provide remote services for patient monitoring which allows healthcare practitioners to diagnose, monitor, and prescribe them without their physical presence. To address the shortcomings of WBAN, software‐defined networking (SDN) is regarded as an effective approach in this prototype. However, integrating SDN into WBAN presents several challenges in terms of safe data exchange, architectural framework, and resource efficiency. Because energy expenses account for a considerable portion of network expenditures, energy efficiency has to turn out to be a crucial design criterion for modern networking methods. However, creating energy‐efficient systems is difficult because they must balance energy efficiency with network performance. In this article, the energy efficiency features are discussed that can widely be used in the software‐defined wireless body area network (SDWBAN). A comprehensive survey has been carried out for various modern energy efficiency models based on routing algorithms, optimization models, secure data delivery, and traffic management. A comparative assessment of all the models has also been carried out for various parameters. Furthermore, we explore important concerns and future work in SDWBAN energy efficiency.

BackgroundPrevious studies have highlighted instances where pharmacists lacked knowledge regarding women’s health issues related to epilepsy.ObjectivesTo assess UAE community pharmacists’ knowledge, toward women’s issues in epilepsy.Methodsa cross-sectional research method was employed. A team of seven pharmacy students in their final year visited a randomly selected sample of community pharmacies in the UAE and face-to-face interviews were conducted with the pharmacists using a structured questionnaire. The questionnaire includes two parts; Eight questions designed to elicit data about the demographics of the study participants and 12 questions eliciting insights into the participants’ knowledge of women’s issues in epilepsy.ResultsA total of 412 community pharmacist were recruited in the study. The overall level of knowledge about women’s issues in epilepsy was good and the average knowledge score was 81% with a 95% confidence interval (CI) [79.1, 82.7%]. The results of multivariate analysis showed higher knowledge scores in chain pharmacies (OR 1.37; 95% CI 1.12–1.67), Chief pharmacists (OR 1.44; 95% CI 1.01–2.06), Pharmacists in charge (OR 3.46; 95% CI 2.7–4.45), pharmacists with 1–5 Years of experience (OR 2.87; 95% CI 1.71–4.82), pharmacists with 6–10 Years (OR 2.63; 95% CI 1.58–4.38), pharmacists with >10 years (OR 3.13; 95% CI 2.03–4.83), graduation form regional universities (OR 1.37; 95% CI 1.12–1.67), graduation form international universities (OR 1.73; 95% CI 1.36–2.20) and receiving a training on epilepsy (OR 1.36; 95% CI 1.12–1.67).ConclusionWhile the findings reveal an overall promising level of knowledge among community pharmacists regarding the issues faced by women with epilepsy, pinpointing which clinical and demographic factors have the most significant impact on this knowledge would permit the implementation of tailored educational interventions. Workshops and modules targeting the issues faced by women with epilepsy would further raise the knowledge and competence among community pharmacists in this area, ensuring better pharmaceutical care for this population.

Abstract Background Lower back pain (LBP) is a common musculoskeletal disorder that may affect students’ daily lives. Recent psychological research showed a relevant connection between LBP and multidimensional health. However, the association between LBP and lifestyle behavior has not been established, and improving knowledge in this area may help develop preventive strategies and optimize college students’ quality of life. Methods A cross-sectional study of 1420 college students in Saudi Arabia was conducted, and participants who attended Saudi Universities were recruited from May 2021 to November 2021. An established validated online survey assessed LBP, sleep quality, time spent sedentary (sedentary duration), health responsibility, physical activity, nutrition, spiritual growth, interpersonal relationships, and stress management. Generalized Linear Regression was used to assess the associations between LBP severity and lifestyle behaviors after controlling for covariates. Results LBP was prevalent among college students from Saudi Arabia. Most of the sample were young (23.81 ± 6.02), and female (83.7%). There were significant differences between students with and without LBP regarding age, BMI, sex, marital status, pain severity, overall lifestyle behavior, health responsibility, physical activity, nutrition, stress management, and global sleep quality. After controlling for age, BMI, sex, and marital status, there were significant associations between pain severity and global sleep quality (ß=0.2, p

The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential applications, but the security of IoT networks remains a major concern. The existing system needs improvement in detecting intrusions in IoT networks. Several researchers have focused on intrusion detection systems (IDS) that address only one layer of the three-layered IoT architecture, which limits their effectiveness in detecting attacks across the entire network. To address these limitations, this paper proposes an intelligent IDS for IoT networks based on deep learning algorithms. The proposed model consists of a recurrent neural network and gated recurrent units (RNN-GRU), which can classify attacks across the physical, network, and application layers. The proposed model is trained and tested using the ToN-IoT dataset, specifically collected for a three-layered IoT system, and includes new types of attacks compared to other publicly available datasets. The performance analysis of the proposed model was carried out by a number of evaluation metrics such as accuracy, precision, recall, and F1-measure. Two optimization techniques, Adam and Adamax, were applied in the evaluation process of the model, and the Adam performance was found to be optimal. Moreover, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. The results show that the proposed system achieves an accuracy of 99% for network flow datasets and 98% for application layer datasets, demonstrating its superiority over previous IDS models.

In recent years, the industrial network has seen a number of high-impact attacks. To counter these threats, several security systems have been implemented to detect attacks on industrial networks. However, these systems solely address issues once they have already transpired and do not proactively prevent them from occurring in the first place. The identification of malicious attacks is crucial for industrial networks, as these attacks can lead to system malfunctions, network disruptions, data corruption, and the theft of sensitive information. To ensure the effectiveness of detection in industrial networks, which necessitate continuous operation and undergo changes over time, intrusion detection algorithms should possess the capability to automatically adapt to these changes. Several researchers have focused on the automatic detection of these attacks, in which deep learning (DL) and machine learning algorithms play a prominent role. This study proposes a hybrid model that combines two DL algorithms, namely convolutional neural networks (CNN) and deep belief networks (DBN), for intrusion detection in industrial networks. To evaluate the effectiveness of the proposed model, we utilized the Multi-Step Cyber Attack (MSCAD) dataset and employed various evaluation metrics.

This study was conducted to evaluate how sterubin affects rotenone-induced Parkinson's disease (PD) in rats. A total of 24 rats were distributed into 4 equal groups: normal saline control and rotenone control were administered saline or rotenone (ROT), respectively, orally; sterubin 10 received ROT + sterubin 10 mg/kg po; and sterubin alone was administered to the test group (10 mg/kg). Rats of the normal saline and sterubin alone groups received sunflower oil injection (sc) daily, 1 h after receiving the treatments cited above, while rats of the other groups received rotenone injection (0.5 mg/kg, sc). The treatment was continued over the course of 28 days daily. On the 29th day, catalepsy and akinesia were assessed. The rats were then euthanized, and the brain was extracted for estimation of endogenous antioxidants (MDA: malondialdehyde, GSH: reduced glutathione, CAT: catalase, SOD: superoxide dismutase), nitrative (nitrite) stress markers, neuroinflammatory cytokines, and neurotransmitter levels and their metabolites (3,4-dihydroxyphenylacetic acid (DOPAC), dopamine (DA), norepinephrine (NE), serotonin (5-HT), 5-hydroxyindoleacetic acid (5-HIAA), and homovanillic acid (HVA)). Akinesia and catatonia caused by ROT reduced the levels of endogenous antioxidants (GSH, CAT, and SOD), elevated the MDA level, and altered the levels of nitrites, neurotransmitters, and their metabolites. Sterubin restored the neurobehavioral deficits, oxidative stress, and metabolites of altered neurotransmitters caused by ROT. Results demonstrated the anti-Parkinson's activities of sterubin in ROT-treated rats.

Peripheral nerve sheath tumors (PNSTs), while uncommon, can have a significant impact on appearance and quality of life, especially when they form in prominent areas such as the nose dorsum. We discuss a case of a 29-year-old woman who developed a benign PNST on the right side of her nasal ala. This tumor gradually grew, impairing her face appearance. Diagnostic tests, such as computed tomography (CT) and magnetic resonance imaging (MRI), revealed the tumor as a slowly growing, well-defined mass. The tumor was removed via open rhinoplasty under general anesthesia, and pathological investigation verified its benign nature. After surgery, the patient’s quality of life improved significantly, and there were no evidence of tumor recurrence after eight months. This case emphasizes the need of including PNST in the differential diagnosis of nasal tumors.

ObjectiveThis study aims to assess the knowledge and perceptions of the public toward migraine in Saudi Arabia.MethodsThis cross-sectional survey assessed the knowledge and perceptions of migraine among Saudi Arabian individuals. The study was conducted over three months in 2023 (1st of June 2023 to 31st of August 2023) using a prevalidated online questionnaire divided into four sections.ResultsA total of 1,975 adults aged between 18 and 64 completed the web-based survey. Of these, over half were male (n = 1,268; 64.2%). The main causes of migraine identified by the participants were genetic disease (n = 540, 27.3%), followed by physical disease (n = 341, 17.3%), head trauma (n = 274, 13.9%), and psychiatric disease (n = 157, 7.9%). The main symptoms identified by the participants were photophobia (21%), followed by inability to control urine (14.1%), vomiting and nausea (13.8%), and vision loss (8.3%). The majority of the participants in this study had a good knowledge of migraines, while 49% had poor knowledge. The migraine knowledge score was significantly associated with the participants' gender (p = 0.002), age (p = 0.0001), educational level (p = 0.001), employment status (p = 0.001), monthly income (p = 0.0001), region (p = 0.0001), and history of migraine (p = 0.0001).ConclusionAlthough one-third of the participants exhibiting good knowledge, deficiencies existed in certain clinical aspects, emphasizing the need for targeted educational interventions to enhance public awareness and understanding of migraines.

In this era of advanced information technology, the exploration and development of novel mechanisms to ensure information confidentiality have consistently captivated the attention of upcoming researchers. In this article, we present a pioneering approach that combines DNA sequencing with a four-dimensional (4D) hyperchaotic map to bolster the security of digital information. Our primary focus is on the design of a robust and secure scheme for encrypting color images, leveraging DNA cryptography and hyperchaos. By extracting three distinct DNA sequences, we generate encryption keys through the integration of DNA computing and 4D hyperchaotic maps. Notably, these keys are intricately linked to the plaintext and vary with any alterations in the input. Consequently, the proposed encryption method stands resilient against an array of potential cryptographic attacks. To gauge the algorithm's security, we subject it to rigorous standard statistical analysis. Our findings underscore the efficiency and robustness of the proposed framework, establishing its potential for facilitating secure communication.

الملخص: أهداف البحث: يعد اجراء الاختزال المفتوح مع التثبيت الداخلي بمثابة العلاج القياسي للكسور الحقية. تثبيت الكسور الحقية بالمسمار عن طريق الجلد هو اجراء جديد مرغوب فيه بسبب التشريح المعقد للحوض. في هذه الدراسة، هدفنا تقييم النتائج الوظيفية والحركية ومعدل الشفاء والتشوهات الوعائية العصبية البعيدة في المرضى الذين خضعوا لتثبيت الكسور باستخدام المسمار بالطريق الراجع عن طريق الجلد طرق البحث: شملت دراستنا ۳٦ مريضا يعانون من الكسور الحقية ممن خضعوا للعلاج بطريقة تثبيت الكسور بالمسمار عن طريق الجلد وذلك خلال الفترة من يناير ٢٠۱٦ الى يونيو٢٠٢۱, منهم ۱٨ حالة مصابة بالكسور الحقية في منطقة العمود الأمامي و٧ مصابين بالكسور الحقية على المستوى المستعرض و ۱۱ مصابين بالكسور الحقية المصاحبة منهم ٦ مصابين بكسور حقية على شكل حرف ت. تم وصف الخصائص والنتائج السريرية بالتكرار والنسب المئوية للمتغيرات الفئوية، والمتغيرات المستمرة كقيمة متوسط وانحراف معياري، وتم استخدام برنامج الحزمة الإحصائية للعلوم الاجتماعية للتحليل الإحصائي النتائج: بلغ متوسط الوقت لاستعادة القدرة على الحركة الكاملة مع تحمل الوزن الكامل ۱٢٫٩±٫٤ ٥ أسبوعا، بالإضافة الى حوالي ۱۱٫۱±٫٨ ٢ لاختفاء الألم مع التئام الكسور بشكل مرض. ظهرت اقلية من المرضى بلغت ٨٫۳٪ تعاني من تشوهات تؤثر على الجهاز الوعائي العصبي القاصي بالإضافة الى۱۱٫۱ ٪ من المرضى الذين عانوا من اختلال وظيفي جنسي. تم تقييم شدة الألم أيضا باستخدام مقياس تناظري بصري، وكان متوسط شدة الألم في اليومين الأول والثالث بعد الجراحة ٤±٫٤ ٢و ۳٫٨ ±٫٦ ٢ على التوالي بينما بلغ متوسط شدة الألم قبل الخروج من المستشفى ۱٫٧ ±٢٫٦ الاستنتاجات: يعد التثبيت بالمسمار عن طريق الجلد بديلا جراحيا آمنا وفعالا لمعظم الكسور الحقية Abstract: Objective: Open reduction with internal fixation is the surgical intervention of choice for acetabular fractures (AFs). Percutaneous screw fixation for AFs is a new procedure that is desirable because of the complex anatomy of the pelvis. In this study, we aimed to assess the functional outcomes, mobility, healing, and distal neurovascular abnormalities in patients who underwent percutaneous retrograde screw fixation. Methods: Our study included 36 patients with AFs treated with percutaneous screw fixation between January 2016 and June 2021. There were 18 cases with anterior column AF, 7 cases with transverse AF, and 11 cases with associated AF, 6 of which had a T-shaped AF. Frequencies and percentages were used to describe characteristics and clinical outcomes. Mean and standard deviation were used for continuous variables. SPSS version 23 (IBM Corporation, Armonk, NY, USA) was used for statistical analysis. Results: The average time to regain full mobility with full weight bearing was 12.9 ± 5.4 weeks, and approximately 11.1 ± 2.8 weeks was required for patients to be pain-free with satisfactory fracture healing. Only a minority (8.3%) of patients had abnormalities affecting the distal neurovascular system, and 11.1% experienced sexual dysfunction. Pain severity was assessed with a visual analogue scale. The average pain severity on the first and third post-operative days was 4 ± 2.4 and 3.8 ± 2.6, respectively. However, the average pain intensity before discharge was 1.7 ± 2.6. Conclusion: Percutaneous screw fixation is the most efficient surgical choice for most pelvic/AFs.

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.

IntroductionRecently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. MethodThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications.ResultsThe proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively.DiscussionThe experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.

Background: Globally, the use of amphetamines as therapeutic agents in pediatric medicine is a crucial area of concern, especially given the population’s vulnerability.Methods: On 6 August 2023, a search was conducted on ClinicalTrials.gov using “amphetamine” as the keyword. Two independent examiners screened trials against set criteria, including a focus on amphetamine, completion status, an interventional approach, and included children. Ongoing or observational studies were excluded. Data extracted from the qualified trials encompassed primary objectives, participant counts, study duration, and outcomes, with the aim of analyzing children disorders treated by amphetamine.Results: On 6 August 2023, a search of the ClinicalTrials.gov database with the term “amphetamines” identified 179 clinical trials. After extensive exclusion criteria, 19 trials were ultimately selected for analysis. The predominant condition under investigation was attention deficit hyperactivity disorder (ADHD), present in 84.2% of studies. Key study characteristics included: phase 4 trials (36.8%), randomized allocation (63.2%), and the parallel intervention model (42.1%). Masking techniques varied, with no masking in 42.1% of studies, and double and quadruple masking both accounting for 21.1%. Geographically, 78.9% of the studies’ participants were from the United States.Conclusion: This study highlights the notable therapeutic potential of amphetamines in pediatric ADHD populations and emphasizes the importance of recognizing potential side effects and addiction risks. As pharmacogenomics offers the prospect of personalized treatments, there is potential to increase therapeutic efficacy and decrease adverse reactions. It is vital to balance these benefits against the inherent risks, understanding the need for continued research to optimize the use of amphetamines in medicine.

Background: Community pharmacists play an intermediary role between prescribing physicians and patients in the United Arab Emirates (UAE) and thus are responsible for ensuring that patients receive optimal cardiovascular disease (CVD) pharmaceutical care.Methods: we used a cross-sectional design to assess the perceptions and practices of community pharmacists concerning pharmaceutical care for patients with CVD. A trained researcher visited randomly selected community pharmacies and used a structured questionnaire to conduct in-person interviews with pharmacists. The questionnaire collected demographic data and information on perceptions and practices regarding CVD pharmaceutical care.Results: Five hundred and fifty-one participants were recruited. The average participant age (mean ± SD) was 35 ± 2.7 years. The average perception score regarding CVD prevention and management was 75.6% (95% confidence interval [CI] 77.1%–74.2%), and the average practice score for CVD prevention and management was 87.1% (95% CI 76.5%–79.6%). Bivariate analysis revealed that gender (p = 0.001), education level (p < 0.001), pharmacy position (p = 0.004), work experience (p < 0.001), number of patients served per day (p < 0.001) and being trained on CVD prevention and management (p < 0.001) were significantly associated with perceptions about the prevention and management of CVD. Better practice scores were seen among older participants (OR 1.01; 95% CI 1–1.019), postgraduates (OR 1.77; 95% CI 1.66–1.89), workers at chain pharmacies (OR 1.24; 95% CI 1.11–1.39), pharmacists in charge (OR 1.22; 95% CI 1.01–1.47), pharmacists with >10 years of experience (OR 11.3; 95% CI 6.01–15.62), pharmacists with 6–10 years of experience (OR 4.42; 95% CI 3.90–5) and pharmacists trained on CVD prevention and management (OR 1.29; 95% CI 1.15–1.46).Conclusion: Pharmacy practitioners working in community pharmacies in the UAE actively engage in delivering pharmaceutical care to patients, playing a role in CVD management and prevention. However, they showed low levels of involvement in other healthcare services, specifically in screening and measuring patients’ weight, glucose levels, and blood pressure, monitoring treatment responses, maintaining medical records, and reviewing medication refill histories. Activities such as educating patients, providing medication counseling, offering support for treatment adherence, and fostering collaborative relationships with other healthcare providers should be encouraged among UAE community pharmacists to ensure the provision of high-quality patient care.

The emergence of two new technologies, namely software defined networking (SDN) and 5G networks, has greatly changed the development of network functions and network topologies. These two technologies provide cost benefits for mobile operators, a more flexible and scalable network, and a shorter time to market for new services and applications. Scalability and effectiveness are increased when 5G and SDN are used together. SDN increases the reliability of the 5G network by separating the control plane from the data plane. Incorrect load balancing, a lack of knowledge of network traffic, and other issues make it difficult to provide Quality of Service (QoS) with SDN. This research proposes a unique load-balancing method to resolve these concerns using Hierarchical Agglomerative Clustering (HAC) and Back Propagation Neural Network (BPNN) algorithms. The proposed method segments the network services into several groups after normalizing data requirements. It consists of two phases: in first phase, there is clustering of bandwidth for different services (e.g., social media, automated homes, and automated cars) inside the network for different data requirements. The agglomerative hierarchical clustering (single link technique) is implemented to make the clusters of bandwidth inside the SDN work based on minimum distance. After clustering, we allotted bandwidths to the respective clusters. In the second phase, the BPNN technique trains the network to choose the optimal path and check the error faults. The proposed approach evaluates the network delay, packet loss, throughput, latency rate, and bandwidth usage to evaluate the performance with Multiple Regression-based Searching (MRBS) and Software-defined Sensor Network Load Balancing (SDSNLB) algorithms. The experimental results of the proposed model are promising as the performance increased by 15%, 23%, 27%, 21%, and 30%, respectively, compared with existing approaches. In addition we compared the computational time complexity with increased the no of nodes and services, when the rate of nodes and services is varying, the proposed solution’s efficiency remains constant.

Background: Medication errors can result in adverse drug events (ADEs) and cause considerable patient harm. Limited data are available from Saudi Arabia and the Middle East regarding the prevalence of preventable adverse drug events (pADEs) in primary care settings. Objectives: To estimate the period prevalence of pADEs and assess the medication error severity in primary care setting in Saudi Arabia. Methods: This retrospective study is a continuation of a previous study where 117 of 2000 adult patients managed at the Family Medicine clinics of King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia, were identified to have had least one medication error in the past 15 months. The electronic health records of these 117 patients were analyzed for a 3-month post-medication error period to explore the presence of pADE. Medication errors were categorized according to the National Coordinating Council for Medication Error Reporting and Prevention index (NCC MERP) and the occurrence of pADE was assessed using the NCC MERP scheme. Results: Of the included 117 patients, 9 (7.7% [95% confidence interval (CI): 2.79–12.59]) experienced pADE (Category E), while 108 (92.3% [95% CI: 87.97–98.35]) did not (Category C). All patients who experienced pADE were using over-the-counter medications and were on polypharmacy. Outcomes 2a and 2b (asthma and β-blocker) accounted for two and four cases, respectively, while Outcomes 6 (warfarin and international normalized ratio), 7 (lithium and lithium level), 16 (new oral anti-coagulant or warfarin and antiplatelet), and 17 (acetylsalicylic acid [aspirin] and antiplatelet) each accounted for one case. Conclusions: This study provides the period prevalence of patients with pADEs from Family Medicine clinics at a major tertiary hospital of Saudi Arabia, and highlights the need for a multicenter study of clinically important medication errors at the prescribing and monitoring stages for the development of quality improvement programs.

Antibiotic resistance is a major risk to human health worldwide due to antibiotic- and multidrug-resistant bacteria, especially in the case of serious infections, which limits the availability of antimicrobial treatment options. Focusing on the bacterial resistance mechanisms against antibiotics and the conventional strategies used to combat antimicrobial resistance, this review highlights the history of antibiotics and their target mechanisms, mentions the strategy limitations, provides the most recent novel alternative therapies to combat resistance, and illustrates their mode of action and applications that may treat several infectious diseases caused by bacterial resistance. Finally, this paper mentions future prospects that we believe would make a considerable difference in the microbial resistance battle. Novel antibiotic alternative therapies, including nanomaterial therapy, antimicrobial photodynamic therapy, hybrid antimicrobial therapy, and phage therapy, are covered in this review.

Nowadays, antibiotic resistance is a serious problem, especially in countries that have unregulated use of antibiotics. We aimed to prepare and characterize Sily-Nano from silymarin to present its antimicrobial activities in comparison to silymarin and gentamycin and provide natural antibacterial from natural sources. Silymarin NPs (Sily-Nano) were prepared using a high-pressure homogenization and characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), ultraviolet-visible (UV-visible) spectrum, and scanning electron microscopy (SEM). The antibacterial monitoring of Sily-Nano and silymarin was compared against Escherichia coli UN75 (E.coli-UN75), Enterobacter aerogenes SL3 (E.aerogenes-SL3), Staphylococcus aureus-PS41 (S.aureus-PS41), and Klebsiella pneumoniae-BD29 (K.pneumoniae-BD29). Sily-Nano was effectively prepared and confirmed using FTIR. SEM, with even distribution, and significant bacterial zone inhibition, compared with silymarin. In conclusion, Sily-Nano preparation is an efficient agent, and effective against certain clinical antibiotic-resistant bacterial strains. Sily-Nano has a novel significant inhibitory effect against the antibiotic-resistant strain of K. pneumonia-BD29, Ent. Aerogenes-SL3 and E. coli UN75 when compared with silymarin and the common antibiotic gentamycin. Therefore, sily-Nano is potentially used as an antibacterial and natural sanitizer agent.

Background: The use of drugs containing fiscalized substances is essential in different medical areas, including pain management, obstetric emergencies, and the treatment of mental disorders. However, due to their potential for abuse and negative health effects, the dispensing of these substances demands pharmacists with the requisite skills and practice.Objective: This study assesses the skills and practices of pharmacy personnel in the United Arab Emirates (UAE) regarding the dispensing of tramadol, a medication containing fiscalized substances, in community pharmacies.Methodology: A cross-sectional study was conducted. Community Pharmacies were chosen via random sampling, and seven well-trained final year pharmacy students visited them and conducted face-to-face interviews. The survey tool covered items highlighting the demographic data of the subjects, and items on the practice and skills regarding dispensing the fiscalized substances. The content validity ratio values of all tool questions were more than 0.78, suggesting acceptable validity and the Cronbach’s α of 0.75 showed as acceptable internal reliability. The primary outcome measures of interest were the skills and practice regarding dispensing Fiscalized substances.Results: A total of 612 pharmacists were recruited in the study. The average practice score was 80%. There was a statistically significant association (p < 0.05) between practices about dispensing fiscalized substances and gender, age group, pharmacy type, work experience, university of graduation, and receiving training on epilepsy and antiepileptic drugs.Conclusion: The results implied that competency and experience are vital factors for the dispensing of tramadol. Contextually, the majority of the pharmacists evidently have the requisite competencies to provide high-quality and proper medical care, with regards to dispensing tramadol, which will minimize drug abuse and medication errors, and assist outpatients to manage their drugs containing fiscalized substances.

A new series of thieno[2,3-d][1,2,4]triazolo[1,5-a]pyrimidines was designed and synthesized using readily available starting materials, specifically, β-enaminoester. Their cytotoxicity was screened against three cancer cell lines, namely, MCF-7, HCT-116, and PC-3. 2-(4-bromophenyl)triazole 10b and 2-(anthracen-9-yl)triazole 10e afforded excellent potency against MCF-7 cell lines (IC50 = 19.4 ± 0.22 and 14.5 ± 0.30 μM, respectively) compared with doxorubicin (IC50 = 40.0 ± 3.9 μM). The latter derivatives 10b and 10e were further subjected to in silico ADME and docking simulation studies against EGFR and PI3K and could serve as ideal leads for additional modification in the field of anticancer research.

BackgroundMonkeypox (mpox), a zoonotic viral infection, poses a global threat that is being acknowledged at the national and international levels. This systematic review aims to identify and characterize interventional clinical trials for mpox.MethodAll interventional clinical trials registered at ClinicalTrials.gov for mpox were searched up to January 6, 2023. We described the characteristics of interventional clinical trials, and drug interventions (including drugs and vaccines).ResultsAs of January 6, 2023, there were 10 clinical trials in the ClinicalTrials.gov registry that met our criteria. Most of the interventional clinical trials were focused on the treatment (N = 4, 40%) and prevention (N = 4, 40%) of mpox. From the 10 trials, 50% used random treatment allocation, and six (60%) chose the parallel assignment intervention model. All 10 studies were blinded, and six were open-label blinded. The largest proportion of the clinical trials (N = 4, 40%) were registered in Europe, followed by America (N = 3, 30%) and Africa and others (N = 3, 30%). The JYNNEOS vaccine (40%), followed by Tecovirimat (30%) were the most frequently studied drugs used against mpox.ConclusionA limited number of clinical trials have been registered on ClinicalTrials.gov since the first case of mpox was reported. Therefore, there is an urgent need to conduct large-scale randomized clinical trials to assess the safety and efficacy of the drugs and vaccines being used against the mpox virus.

Background: This review aimed to conduct an indirect comparison using a Bayesian network meta-analysis of randomized controlled trials (RCTs) to compare the efficacy and safety of delafloxacin versus other single antibiotic regimens for the empiric treatment of Acute Bacterial Skin and Skin Structure Infections. Method: A systematic search with no start date restrictions was conducted. The Cochrane Risk of Bias tool was used to assess the quality of included RCTs. Results: Of the 577 studies initially identified, nine RCTs were included in the review. The network meta-analysis showed that ceftaroline, ceftobiprole, delafloxacin and tigecycline had similar efficacy in the indirect comparisons [Ceftaroline Odds Ratio (OR) = 1.2, 95% Crl = 0.46–3.6), ceftobiprole (OR = 1.3, 95% Crl = 0.34–3.0) and tigecycline (OR = 0.96, 95% Crl = 0.30–2.9)]. However, the ranking plot for the intention to treat (ITT) population showed that delafloxacin had a probability of 80.8% to be ranked first followed by ceftobiprole (13.1%). The analysis of the overall adverse events showed that ceftaroline (OR = 0.88, 95% Crl = 0.65–1.2), ceftobiprole (OR = 1.1, 95% Crl = 0.69–2.0), delafloxacin (OR = 0.88, 95% Crl = 0.57–1.3) and tigecycline (OR = 1.4, 95% Crl = 0.88–2.2) had similar safety profiles. Conclusion: Delafloxacin did not show any statistically significant differences when compared to ceftaroline, ceftobiprole, and tigecycline in terms of efficacy and safety. However, the surface under the cumulative ranking curve (SUCRA) probability ranked delafloxacin as the first option for the ITT population.

Background: The Central Sensitization Inventory (CSI) is a patient-reported screening instrument that can be used to identify and assess central sensitization (CS)/Central Sensitization Syndrome (CSS)-related symptoms. Objective: The aim was to translate the CSI into Arabic (CSI-Ar) and to subsequently validate its psychometric properties. Design: Cross-sectional. Methods: The CSI was translated and cross-culturally adapted into Arabic, and validated following international standardized guidelines. This study included patients with chronic musculoskeletal pain (n = 264) and healthy control participants (n = 56). Patients completed the CSI-Ar, Pain Catastrophizing Scale (PCS), Depression, Anxiety, and Stress scale (DASS-21), Tampa Scale of Kinesiophobia (TSK), and 5-level EuroQol-5D (EQ-5D). Patients completed the CSI-Ar twice to assess test–retest reliability. To evaluate discriminative validity, healthy controls participants completed the CSI-Ar. Statistical analyses were conducted to test the internal consistency, reliability, and structural, construct and discriminant validity of CSI-Ar. Results: The CSI-Ar showed acceptable internal consistency (Cronbach's alpha = 0.919) and excellent test–retest reliability (intraclass correlation coefficient = 0.874). The CSI-Ar scale had significant correlations (P < 0.001) with all PCS subscales and total score (Spearman's rho = 0.459–0.563, P < 0.001), all DASS-21 subscales and total score (Spearman's rho = 0.599–0.685, P < 0.001), the TSK (Spearman's rho = 0.395, P < 0.001), and the EQ-5D (Spearman's rho = −0.396, P < 0.001). The Mann-Whitney U-test showed a statistically significant difference between the patient group and the healthy control group (P < 0.001), with the healthy controls displaying a lower average CSI-Ar score (12.27 ± 11.50) when compared to the patient group (27.97 ± 16.08). Factor analysis indicated that the CSI-Ar is a unidimensional tool. Conclusion: The CSI-Ar is a reliable and valid screening tool that can be used to assess CS/CSS-related symptoms in Arabic-speaking people with chronic musculoskeletal pain. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the dose–response relationship between physical activity and health-related quality of life (HRQoL) in a large population-based sample of people with chronic disease. We analysed the data of 29,271 adults (15,315 women) who were diagnosed with chronic diseases and participated in the Welsh Health Survey (Wales, UK; data collection 2011–2015). Participants were classified, based on their weekly minutes of moderate-to-vigorous physical activity (MVPA), into four groups as follows: inactive (no MVPA), insufficiently active (

Introduction: Knee injuries largely vary including cartilaginous, ligamentous, bony, and tendinous injuries. Greatest of the reported knee injuries associated with in a non-contact method includes mainly the anterior cruciate ligament (ACL). Additionally, medial and lateral menisci act as shock absorbers with an additional role in stabilizing the joint which can be or partially or totally torn. The current study aimed to assess athletes' knowledge and attitude concerning the meniscus, meniscal injury, and management. Methodology: A descriptive cross-sectional study was conducted to achieve the objectives. A pre-structured electronic questionnaire was used to collect the data, covered participants socio-demographic data, personal and family history of meniscus injury and surgery, physical activity during past year, and also knowledge regarding the meniscal injury and management. Results: A total of 448 athletes fulfilling the inclusion criteria completed the study questionnaire. Participants 'age ranged from 18 to 60 years with mean age of 26.7 ± 7.7 years old. A total of 256 (57.1%) participants were males. Exact of 21 participants had meniscus surgery. As for family history, 75 (16.7%) had family history of meniscus injury. Exact of 95 (21.2%) athletes had good knowledge level, while vast majority (78.8%; 353) had poor knowledge level. Conclusions: In conclusion, the study showed that the estimated rate of meniscus injury and surgery was not high (within the international range). The participants' knowledge regarding meniscus injury and meniscus surgery with its related management was unsatisfactory, where 1 out of each 5 was knowledgeable.

Introduction: The use of face mask in these COVID-19 times has increased manifolds. This has led to various skin problems due to the adverse reaction caused by the mask. This study aimed to observe the skin effects of the face mask during the COVID-19 pandemic among individuals in Saudi Arabia. Materials and Methods: Cross-sectional study in different regions of Saudi Arabia with data collection was done using a self-administered online questionnaire in the adults above 18 years of age of both sexes. Results: The study involved 516 participants who were mostly young population with male predominance. In general, 37% of face skin reactions related to mask use, whereas 63% of participants did not notice any skin reaction from face mask wearing. Most of them wear surgical masks (85.5%), of which 36.7% of them reported adverse skin reactions (ASRs) related to surgical masks. Furthermore, using the mask for >4 h is approximately double the risk of skin changes, and a significant association has been found between the duration of mask use and ASRs. Finally, no significant association was observed between prolonged face mask use and aggravation of pre-existing skin diseases by face masks. Conclusion: The overall prevalence of skin reactions associated with face masks was 37%. The most frequent skin reaction was acne and itching symptoms, followed by acne alone. Regarding mask type, both surgical and cloth masks were associated with a significantly high incidence of ASRs, which was statistically significant.

There are many problems that procedural algorithms can solve efficiently. However, these algorithms are sometimes too slow to abide by the time available for performing the solution; other times, it is impossible to get a solution using procedural algorithms. A heuristic method is a practical approach that can reach an approximation of an efficient solution where the optimum is not guaranteed. Heuristic techniques are applied in many real-world problems, including crowd management; using heuristic-based models helped to comprehend crowd behavior better and increase simulation reliability. This paper reviews many heuristic-related articles to gather the aspects of the topic in one place and clear the fuzziness to make it easy to comprehend. The paper covers some of the previous works with similar approaches and presents state-of-the-art heuristic solutions for real-world problems. These techniques are discussed under three classifications: simple heuristics, meta-heuristics, and hyper-heuristics. Most importantly, the paper explores the heuristic role in crowd field problems concluding that heuristics are primarily applied in modeling when it comes to the Crowd field. It investigates different heuristics for crowd management. The main intent of this review is to establish a comprehensive understanding of heuristics-related operations in the crowd management field. Moreover, it aims to support other researchers’ future work and fill research gaps by highlighting the absence of crowd problems from heuristics literature and the limitations of each heuristics approach.

Background: Vancomycin is a glycopeptide antibiotic with a high risk of acute liver injury. Resveratrol is believed to protect the liver against toxicity.Aim: To investigate the ability of resveratrol to attenuate vancomycin-induced liver toxicity in rats injected with vancomycin.Method: Twenty-four adult male Wistar rats were distributed into three groups. The control group received only a vehicle, while the treated group received either vancomycin 200 (mg/kg, i. p.) only or vancomycin (200 mg/kg, i. p.) with resveratrol (20 mg/kg, oral gavage). All groups received their dose once daily for 7 days. Hepatic damage was assessed by measuring biochemical parameter levels in serum, aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), and lactate dehydrogenase (LDH). Also, antioxidants and inflammation biomarkers such as Interleukin-6 (IL-6), malondialdehyde (MDA), nitric oxide (NO), and glutathione (GSH) were measured. Furthermore, the vancomycin-induced pathological changes in the liver were evaluated by histopathological studies.Results: In the vancomycin-treated group, hepatic serum biomarkers such as AST, ALT, ALP, IL-6, and MDA were elevated, while NO and GSH were depleted. However, resveratrol co-treatment with vancomycin prevented the elevation of AST, ALT, ALP, IL-6, and MDA and it protected the liver from NO and GSH depletion. Also, regarding vancomycin-induced degeneration of hepatocytes, resveratrol co-treatment with vancomycin prevented such degeneration and improved mononuclear cells in the liver.Conclusion: The results showed that oral administration of resveratrol has a significant hepatoprotective effect against vancomycin-induced hepatotoxicity.

Objective. This study provides a comprehensive analysis of the characteristics of clinical trials related to alcohol dependence that are registered on ClinicalTrials.gov. Methods. All ClinicalTrials.gov trials registered up to 1 January 2023 were examined, focusing on trials that involved alcohol dependence. All 1295 trials were summarized by presenting their characteristics and results and reviewed most intervention drugs used in the treatment of alcohol dependence. Results. The study analysis identified a total of 1295 clinical trials registered on ClinicalTrials.gov that were focused on alcohol dependence. Of these, 766 trials had been completed, representing 59.15% of the total, while 230 trials were currently recruiting participants, accounting for 17.76% of the total. None of the trials had yet been approved for marketing. The majority of the studies included in this analysis were interventional studies (1145 trials, or 88.41%), which accounted for most of the patients enrolled in the trials. In contrast, observational studies represented only a small portion of the trials (150 studies, or 11.58%) and involved a smaller number of patients. In terms of geographic distribution, the majority of registered studies were located in North America (876 studies, or 67.64%), while only a small number of studies were registered in South America (7 studies, or 0.54%). Conclusions. The purpose of this review is to provide a basis for the treatment of alcohol dependence and prevention of its onset through an overview of clinical trials registered at ClinicalTrials.gov. It also offers essential information for future research to guide future studies.

Western diet style (fast food), which includes fatty frozen junk food, lard, processed meats, whole-fat dairy foods, cream, mayonnaise, butter, snacks, and fructose, is a primary etiological determinant for developing nonalcoholic steatohepatitis (NASH) worldwide. Here the primary focus is to see the impact of naturally identified essential oil on disease mechanisms developed in an animal model using the same ingredients. Currently, symptomatic therapies are recommended for the management of NASH due to non-availability of specific treatments. Therefore, the present study was designed to evaluate the potential anti-NASH effect of nerolidol in a rat model fed with a purpose-built diet. The diet substantially induced insulin resistance, hepatic steatosis, dyslipidemia, and elevation of liver enzymes in the experimental animals. The levels of liver oxidative stress markers, nitrites (NO2–), serum pro-inflammatory cytokine (TNF-α) and hepatic collagen were increased in disease control rats. Nerolidol oral treatment in ascending dose order of 250 and 500 mg/kg substantially reduced the steatosis (macrovesicular and microvesicular), degeneration of hepatocytes, and inflammatory cells infiltration. The amounts of circulatory TNF-α and tissue collagen were also reduced at 500 mg/kg dose of nerolidol, expressing its anti-fibrotic effect. The current study described the multiple-hit pathophysiology of NASH as enhanced steatosis, pro-inflammatory markers, and oxidative stress in rats, which resulted in the development of vicious insulin resistance. Nerolidol treatment significantly reduced hepatic lipid accumulation and halted disease progression induced by a hypercaloric diet.

Abstract There has been an increase in cases of drug addiction and prescription drug abuse worldwide. Recently, pregabalin abuse has been a focus for many healthcare agencies, as highlighted by epidemiological studies. We previously evaluated the possibility of pregabalin abuse using the conditioned place preference (CPP) paradigm. We observed that a 60 mg/kg dose could induce CPP in mice and that pregabalin-rewarding properties were mediated through glutamate neurotransmission. Notably, the dopaminergic reward circuitry is also known to play a crucial role in medication-seeking behavior. Therefore, this study aimed to explore the possible involvement of dopaminergic receptor-1 in pregabalin-induced CPP. Mice were randomly allocated to receive saline or the dopamine-1 receptor antagonist SKF-83566 (0.03 mg/kg, intraperitoneal). After 30 min, the mice received either saline or pregabalin (60 mg/kg) during the conditioning phase. Among the control groups that received saline or SKF-83566, the time spent in the two conditioning chambers was not significantly altered. However, among the pregabalin-treated group, there was a marked increase in the time spent in the drug-paired chamber compared to the time spent in the vehicle-paired chamber. Notably, blocking dopamine-1 receptors with SKF-83566 completely prevented pregabalin-induced place preference, thus demonstrating the engagement of the dopaminergic system in pregabalin-induced reward-related behavior.

Abstract Objectives Behaviour management strategies involving pharmacological or non-pharmacological interventions during dental procedures should be considered to attain safe and successful treatment outcomes. This study compared the frequencies of use and the completeness of treatment with these interventions. Methods A total of 1725 dental records of patients up to 18 years old, who were treated in the King Abdulaziz Medical City in Jeddah City from October 2018 to June 2019, were used in this retrospective, cross-sectional study. Inferential analysis, Chi-square test, Kruskal–Wallis test, and regression model were used in the data analysis. Results About two-thirds of the patients were treated with attendant non-pharmacological interventions, while one-third, with pharmacological interventions. The application of General Anesthesia (GA) was the most frequently used intervention. Restorative procedures and extractions were done in higher frequencies with pharmacological interventions. Treatments with space maintainers and orthodontic appliances were carried out in higher frequencies with non-pharmacological strategies. The choice of intervention was significantly influenced by the systemic conditions of the patients. Patients treated with non-pharmacological intervention comprised the dominant type of patients, because they required treatments with less pain. Those treated with GA needed restorative treatments and extractions, or treatments that involve pain, but these treatments had higher frequencies of being completed. Conclusions The treatments with pharmacological intervention through GA have higher frequencies of being completed, compared to those with non-pharmacological interventions. Factors, such as age, potential to complete the treatment, and the type of dental treatment applied, influence the choice of treatment intervention.

Sleep improves the cognitive and physical performance of athletes. A detailed questionnaire that detects sleep disruptions is required to identify sleep-deprived athletes. This study evaluates the translated Athlete Sleep Screening Questionnaire (ASSQ), a tool suggested by the International Olympic Committee, among Arabic-speaking athletes. The ASSQ was translated into Arabic and examined for floor or ceiling effects, internal consistency, and validity among Arabic-speaking athletes. The Arabic Pittsburgh Sleep Quality Index (PSQI) was employed to assess convergent validity. Ninety athletes (28.9% women) participated and completed this study’s questionnaires. The Cronbach’s alpha for the ASSQ-Sleep Difficulty Score (SDS) was 0.435, and that of the ASSQ-chronotype was 0.632. The SDS and chronotype subset of the ASSQ demonstrated excellent test-retest reliability, with intraclass correlation coefficients of 0.84 and 0.938, respectively. The ASSQ-SDS correlated positively with the PSQI (0.734, p = 0.001). The ASSQ-chronotype was inversely associated with the PSQI (p = 0.001). This study’s findings can assist clinicians in assessing sleep disorders in sports. The Arabic version of the ASSQ has satisfactory psychometric qualities and can identify clinically relevant sleep problems in athletes.

Background and Objectives: Epilepsy is a chronic disease that causes substantial morbidity and mortality. Pharmacists represent an integral role in managing patients with epilepsy. The aim of this study was to evaluate the level of knowledge about the pharmacology and pathophysiology of epilepsy among senior pharmacy students. Materials and Methods: Cross-sectional study using a designed questionnaire to measure the pharmacological and physiological knowledge of senior pharmacy students regarding epilepsy who are studying at Umm Al-Qura University, Makkah, Saudi Arabia, from August to October 2022. Results: A total of 211 senior clinical pharmacy students responded to the questionnaire. The majority of the respondents were 4th year pharmacy students. The numbers of female and male participants were equal (106 and 105 students, respectively). The participants represented an acceptable level of knowledge about the pathophysiology aspects of epilepsy, with a mean total score of 6.22 ± 1.9 out of a maximum score of 10. The respondents reported that epilepsy could be due to genetic predisposition combined with environmental conditions (80.1%) or brain stroke (17.1%). Regarding the respondent knowledge about the pharmacology of epilepsy, the total score was 4.6 ± 2.1 (maximum attainable score: 9). Conclusions: The majority of pharmacy students had knowledge about the pathophysiology concept of the disease; however, low knowledge was shown by the respondents regarding the pharmacology of epilepsy. Thus, there is a need to identify better strategies to improve students’ education.