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Dalal Alhasan,1 Mohammad Fakhraldeen,2 Sara Alqabandi,3 Maryam Alajmi3 1Department of Emergency Medical Services, College of Health Sciences, Public Authority of Applied Education and Training, Shwaikh, Kuwait; 2Department of Emergency Medicine, Ministry of Health, Al-Sabah Health District, Kuwait Kuwait; 3Kuwait Board of Emergency Medicine, Ministry of Health, Sharq, KuwaitCorrespondence: Dalal Alhasan, Department of Emergency Medical Services, College of Health Sciences, Public Authority of Applied Education and Training, Shwaikh, Kuwait, Tel +965 97964699, Email dm.alhasan@paaet.edu.kwBackground: School cardiopulmonary resuscitation (CPR) training has not been previously implemented nor studied in the Arabian Peninsula, and this is due to the challenges that this training imposes. This study aims to determine high school teacher perspectives, willingness, and barriers as related to CPR student training in high schools.Methods: An anonymous, voluntary, cross-sectional electronic questionnaire, primarily based on the theory of planned behavior (TPB) was distributed to high school principals and teachers from 20 local (public and private) high schools between October and December 2021. The questionnaire was a 4-part (demographics, willingness, barriers, implementation approach), 23-variable tool.Results: Eighty-four out of 88 participants were included in the analysis from 20 high schools. The overall willingness to teach CPR to students was 4.2 ± 0.9, and this willingness was significantly associated with being a female (p = 0.019), being a teacher (p = 0.036), having a family history of cardiovascular disease (p = 0.007), previous school CPR campaigns (p = 0.02), and all TPB factors: attitude (p = 0.001), subjective norms (p = 0.011), and perceived behaviour control (p = 0.007). As for perceived barriers, there was moderate concern regarding the absence of the Good Samaritan law (3.8 ± 1.1) and CoVID-19 transmission (3.5 ± 1.3). High school teaching staff recommended formal legislation of CPR training from the Ministry of Education (MoE) and favoured CPR training delivery by healthcare professionals. However, they were willing to conduct CPR training themselves with regular training, material integration into the curriculum, and online teaching material access.Conclusion: High school teachers are willing to teach students CPR. They need MoE legislation, appropriate training, online material, and a standardized database. Teaching staff also recommend specific training session settings. CoVID-19 and Good Samaritan law are moderate barriers. A number of factors influence teaching staff willingness to conduct CPR training. From this analysis, we recommend piloting CPR training in Kuwait high schools with consideration to the identified influential factors and barriers.Keywords: high school, cardiopulmonary resuscitation, training, willingness, theory of planned behavior, Kuwait

Nasser G Alsaedi,1,2 Ruba M Alselaimy,3 Abdulaziz A Alshamrani,1 Muhammed AlAjmi,4 Rajiv Khandekar,1 Hassan Al-Dhibi,1 Abdulelah A Al-Abdullah1 1King Khaled Eye Specialist Hospital, Riyadh, Saudi Arabia; 2King Abdullah Medical City, Makkah, Saudi Arabia; 3College of Medicine, King Saud University, Riyadh, Saudi Arabia; 4Albahar Eye Centre, Kuwait City, KuwaitCorrespondence: Abdulelah A Al-AbdullahVitreoretinal and Uveitis consultant, Vitreoretinal division, King Khaled Eye Specialist Hospital, P. O. Box 7191, Riyadh, 11462, Saudi ArabiaTel +966114821234 extension 2655Email hasadoc@gmail.comPurpose: To compare the visual and anatomic outcomes of aflibercept versus ranibizumab as a second line treatment for persistent diabetic macular edema (DME) after initial bevacizumab injections.Methods: In this retrospective cohort study, patients with center-involved DME of ≥ 300 μm thickness after bevacizumab intravitreal injections in 2015– 2019 were included. Those treated with ranibizumab (R) and aflibercept (A) were grouped as group R and group A, respectively. The change in central macular thickness (CMT) measured by optical coherence tomography (OCT) and the best corrected distance visual acuity (BCVA) before and after three-monthly anti-VEGF injections (anti-VEGF) in group R and group A were compared and reviewed.Results: There were 80 eyes of 75 patients in group R and 80 eyes of 72 patients in group A. The initial bevacizumab injections in group R and group A varied significantly (p = 0.01). The median change of the CMT after the three injections was not significantly different in group R (80 μm) and group A (81.5μm) (p = 0.7). The improvement of BCVA in group R and group A was not significant (p = 0.5). Dry macula was noted in 1 vs 14 eyes in group R vs group A.Conclusion: After treating refractory DME with initial bevacizumab injections, 3 injections of either aflibercept or ranibizumab had similar anatomic and functional outcomes. Aflibercept achieved dry macula in more eyes with refractory DME compared to ranibizumab.Keywords: aflibercept, bevacizumab, persistent DME, ranibizumab, VEGF switch, vascular endothelial growth factor

OBJECTIVE: Addiction is a widespread public health problem despite all efforts to prevent and treat it. Over the past few years, tramadol abuse has been sharply increasing in Middle Eastern countries. This research aims to identify the tramadol-induced histological changes in rat kidneys and any potential protective effects of vitamin C on these changes. MATERIALS AND METHODS: This is an experimental study conducted at Prince Sattam bin Abdul Aziz University. Thirty-three adult albino rats were randomly divided into three groups. Control, Tramadol, and vitamin C groups. The Tramadol group received 25 mg/Kg a day of tramadol orally via gastric gavage for three weeks. In the vitamin C + tramadol treated group, 100 mg/Kg/b. wt of vitamin C was administered intravenously to the animals 30 minutes before receiving the same dose of tramadol. RESULTS: Specimens from the kidney of every rat were excised for histological examination by the light and electron microscope. Tramadol damage to the kidney's glomeruli and proximal and distal convoluted tubule hypertrophy were among its long-term harmful consequences. When vitamin C was added to tramadol, the distal and proximal convoluted tubules, and the renal glomeruli, improved. CONCLUSIONS: When vitamin C was given to the tramadol group, the drug's harmful effects on the kidney were reduced. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is any predictive algorithm, or a combination of algorithms, that learns from data (that learns from "experience"), and makes predictions without being explicitly coded with a deterministic model. The most immediate example are neural networks, which are trained with data to minimise a cost function and perform predictions. In this work, we present some ML methods, with simple examples to grasp the basic concepts of each algorithm, avoiding formal mathematical complexities. The techniques involved in ML include gradient methods, genetic algorithms, simulated annealing, neural networks, and the novel field of quantum computing as an aid to speed the algorithms. Geophysical examples are given to illustrate practical applications. [ABSTRACT FROM AUTHOR]

There is increasing evidence that diabetes mellitus and its complication diabetic ketoacidosis confer an increased risk of venous thromboembolism. Pulmonary embolism is the most serious venous thromboembolic event that is rarely reported in association with diabetic ketoacidosis. We report a case of saddle pulmonary embolism that presented simultaneously with diabetic ketoacidosis. Though radiologically massive, saddle pulmonary embolism does not confer any difference in management decisions or prognosis from other forms of pulmonary embolism. It is important to appreciate the relation between diabetic ketoacidosis and venous thromboembolism in order to further evaluate the need for prophylactic anticoagulation and to increase the suspicion and recognition of venous thromboembolic events in patients with diabetic ketoacidosis. [ABSTRACT FROM AUTHOR]

Protein and peptide misfolding is a central factor in the formation of pathological aggregates and fibrils linked to disorders like Alzheimer's and Parkinson's diseases. Therefore, it's essential to understand how food additives, particularly Azorubine, affect protein structures and their ability to induce aggregation. In this study, human serum albumin (HSA) was used as a model protein to investigate the binding and conformational changes caused by azorubine, a common food and drink colorant. The research revealed that azorubine destabilized the conformation of HSA at both physiological (pH 7.4) and acidic (pH 3.5) conditions. The loss of tryptophan fluorescence in HSA suggested significant structural alterations, particularly around aromatic residues. Far UV-CD analysis demonstrated disruptions in HSA's secondary structure, with a notable reduction in α-helical structures at pH 7.4. At pH 3.5, Azorubine induced even more extensive perturbations, resulting in a random coil conformation at higher azorubine concentrations. The study also investigated aggregation phenomena through turbidity measurements, RLS analysis, and TEM imaging. At pH 3.5, larger insoluble aggregates formed, while at pH 7.4, only conformational changes occurred without aggregate formation. Cytotoxicity assessments on neuroblastoma (SH-SY5Y) cells highlighted the concentration-dependent toxicity of albumin aggregates. Molecular dynamics simulations reaffirmed the stable interaction between azorubine and HSA. This research provides valuable insights into the mechanisms by which azorubine influences protein conformations. To further advance our understanding and contribute to the broader knowledge in this area, several future directions can be considered such as exploring other proteins, studying dose-response relationship, gaining mechanistic insights, biological relevance, toxicity assessment, identifying alternative food colorants, and mitigation strategies to prevent adverse effects of azorubine on serum proteins.Communicated by Ramaswamy H. Sarma.

Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. Differences between the two domains, such as significant variations in the scale of visual things and the higher granularity of pixels in images compared to words in the text, make it difficult to transfer Transformer from language to vision. Masking autoencoding is a promising self-supervised learning approach that greatly advances computer vision and natural language processing. For robust 2D representations, pre-training with large image data has become standard practice. On the other hand, the low availability of 3D datasets significantly impedes learning high-quality 3D features because of the high data processing cost. We present a strong multi-scale MAE prior training architecture that uses a trained ViT and a 3D representation model from 2D images to let 3D point clouds learn on their own. We employ the adept 2D information to direct a 3D masking-based autoencoder, which uses an encoder-decoder architecture to rebuild the masked point tokens through self-supervised pre-training. To acquire the input point cloud's multi-view visual characteristics, we first use pre-trained 2D models. Next, we present a two-dimensional masking method that preserves the visibility of semantically significant point tokens. Numerous tests demonstrate how effectively our method works with pre-trained models and how well it generalizes to a range of downstream tasks. In particular, our pre-trained model achieved 93.63% accuracy for linear SVM on ScanObjectNN and 91.31% accuracy on ModelNet40. Our approach demonstrates how a straightforward architecture solely based on conventional transformers may outperform specialized transformer models from supervised learning., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Background: Myocardial perfusion imaging (MPI) is a type of single-photon emission computed tomography (SPECT) used to evaluate patients with suspected or confirmed coronary artery disease (CAD). Detection and diagnosis of CAD are complex processes requiring precise and accurate image processing. Proper segmentation is critical for accurate diagnosis, but segmentation issues can pose significant challenges, leading to diagnostic difficulties. Machine learning (ML) algorithms have demonstrated superior performance in addressing segmentation problems., Methods: In this study, a deep learning (DL) algorithm, U-Net, was employed to enhance segmentation accuracy for image segmentation in MPI. Data were collected from 1100 patients who underwent MPI studies at Al-Jahra Hospital between 2015 and 2024. To train the U-Net model, 100 studies were segmented by nuclear medicine (NM) experts to create a ground truth (gold-standard coordinates). The dataset was divided into a training set ( n = 100 images) and a validation set ( n = 900 images). The performance of the U-Net model was evaluated using multiple cross-validation metrics, including accuracy, precision, intersection over union (IOU), recall, and F1 score., Result: A dataset of 4560 images and corresponding masks was generated. Both holdout and k-fold (k = 5) validation strategies were applied, utilizing cross-entropy and Dice score as evaluation metrics. The best results were achieved with the holdout split and cross-entropy loss function, yielding a test accuracy of 98.9%, a test IOU of 89.6%, and a test Dice coefficient of 94%. The k-fold validation scenario provided a more balanced true positive and false positive rate. The U-Net segmentation results were comparable to those produced by expert nuclear medicine technologists, with no significant difference ( p = 0.1)., Conclusions: The findings demonstrate that the U-Net model effectively addresses some segmentation challenges in MPI, facilitating improved diagnosis and analysis of mega data.

Federated unlearning (FUL) is a promising solution for removing negative influences from the global model. However, ensuring the reliability of local models in FL systems remains challenging. Existing FUL studies mainly focus on eliminating bad data influences and neglecting scenarios where other factors, such as adversarial attacks and communication constraints, also contribute to negative influences that require mitigation. In this paper, we introduce Local Model Refining (LMR), a FUL method designed to address the negative impacts of bad data as well as other factors on the global model. LMR consists of three components: (i) Identifying and categorizing unreliable local models into two classes based on their influence source: bad data or other factors. (ii) Bad Data Influence Unlearning (BDIU): BDIU is a client-side algorithm that identifies affected layers in unreliable models and employs gradient ascent to mitigate bad data influences. Boosting training is applied when necessary under specific conditions. (iii) Other Influence Unlearning (OIU): OIU is a server-side algorithm that identifies unaffected parameters in the unreliable local model and combines them with corresponding parameters of the previous global model to construct the updated local model. Finally, LMR aggregates updated local models with remaining local models to produce the unlearned global model. Extensive evaluation shows LMR enhances accuracy and accelerates average unlearning speed by 5x compared to comparison methods on MNIST, FMNIST, CIFAR-10, and CelebA datasets., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Vaccine acceptance is a crucial component of a viable immunization program in healthcare system, yet the disparities in new and existing vaccination adoption rates prevail across regions. Disparities in the rate of vaccine acceptance result in low immunization coverage and slow uptake of newly introduced vaccines. This research presents an innovative AI-driven predictive model, designed to accurately forecast vaccine acceptance within immunization programs, while providing high interpretability. Primarily, the contribution of this study is to classify vaccine acceptability into Low, Medium, Partial High, and High categories. Secondly, this study implements the Feature Importance method to make the model highly interpretable for healthcare providers. Thirdly, our findings highlight the impact of demographic and socio-demographic factors on vaccine acceptance, providing valuable insights for policymakers to improve immunization rates. A sample dataset containing 7150 data records with 31 demographic and socioeconomic attributes from PDHS (2017-2018) is used in this paper. Using the LightGBM algorithm, the proposed model constructed on the basis of different machine-learning procedures achieved 98% accuracy to accurately predict the acceptability of vaccines included in the immunization program. The association rules suggest that higher SES, region, parents' occupation, and mother's education have an association with vaccine acceptability., (© 2024. The Author(s).)

Oral lichen planus (OLP) is a potentially malignant disorder affecting the oral mucosa. Platelet concentrates, including platelet-rich plasma (PRP) and platelet-rich fibrin (PRF), have emerged as promising alternative treatments to corticosteroids. This study aims to comprehensively evaluate the effectiveness of PRP and PRF in the management of patients with OLP. We conducted a comprehensive search of PubMed, Scopus, Web of Science, and the Cochrane Library for randomized controlled trials (RCTs) involving patients with OLP comparing intralesional PRP or PRF with corticosteroids up to August 2024. The primary outcomes assessed were changes in lesion size, pain scores, and Thongprasom scores. Network meta-analysis (NMA) was used. Data were pooled using summary effect sizes with corresponding 95% confidence intervals (CIs) in a random-effects model based on the DerSimonian-Laird method. Eight studies comprising 157 patients and 250 lesions were included in the final analysis. Compared to corticosteroids, no significant differences were observed among PRF and PRP in terms of changes in lesion size, pain scores, clinical severity scores, and adverse events. NMA ranking showed that PRF was the best-ranking treatment in reducing lesion sizes (SUCRA values: 72.6%, 75.8%, 66.2%, 80.8%, and 77.5% at first, second, third, fourth, and eighth weeks of assessment), followed by corticosteroids, and PRP. Moreover, PRF was the best-ranking treatment in reducing pain score at the first, third, and eighth weeks of assessment (SUCRA values: 91.8%, 86%, and 85.9%), while PRP was the best intervention at the second and fourth weeks of assessment (SUCRA values: 61.3%, and 90.2%). Also, PRF was the best intervention in terms of Thongprasom scores at eight weeks of assessment (SUCRA value: 77.3%), while PRP was the best intervention at the fourth week of assessment with value of 78.1%. PRF and PRP showed comparable results with intralesional corticosteroids in all studied parameters. Considering treatments ranking, PRF was the best intervention. The optimal treatment modality for OLP varies on different clinical conditions., Competing Interests: Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Alshemmari et al.)

Vehicular Adhoc Network (VANET) suffers from the loss of perilous data packets and disruption of links due to the fast movement of vehicles and dynamic network topology. Moreover, the reliability of the vehicular network is also threatened by malicious vehicles and messages. The malicious vehicle can promulgate fake messages to the node to misguide it, which may result in the loss of precious lives. In this situation, maintaining efficient, reliable, and secure communication among automobiles is of extreme importance, especially for a densely populated network. One of the remedies is vehicular clustering, which can effectively perform in a high-density network. However, secure cluster formation and cluster optimization are important factors to consider during the clustering process because non-optimal clusters may incur high end-to-end communication delays and produce overhead on the network. In addition, malicious nodes and packets reduce passenger and driver safety, increase road accidents, and waste passenger and driver time. To this end, we employ Arithmetic Optimization Algorithm (AOA) to design a secure intelligent clustering named AOACNET. AOA is used to achieve optimality of vehicular clusters. During cluster formation, the algorithm prevents unauthentic nodes from becoming cluster members by taking into consideration the performance value of each automobile. The vehicle's performance value is based on the record of data transmission. If a vehicle transmits a fake message, it will receive a penalty of (-1), and in the case of transmitting a legitimate message, a reward of (+1) will be assigned to the vehicle. Initially, all the vehicles have equal performance value which either increase or decrease based on communication with their peers. The vehicles will become cluster members only if their performance value is greater than the threshold value (0). AOACNET is tested in MATLAB using various evaluation metrics (i.e., number of clusters, load balancing, computational time, network overhead and delay). The simulation results show that the proposed algorithm performs up to 25% better than the similar contenders in terms of designated optimization objectives., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Vitamin D deficiency is a serious health problem that is associated with many health consequences. The study aimed to assess the knowledge, attitudes, and practices of medical students at Qassim University, Saudi Arabia, regarding vitamin D deficiency., Methods: An observational cross-sectional study was conducted among students of Unaizah Colleges of Pharmacy (UCP) and Medicine (UCM) at Qassim University, Saudi Arabia, over a period of 3 months. A well-structured self-administered questionnaire was used for the data collection. Statistical analysis was applied by using SPSS version 22. A 0/1 scoring process was employed to assess the KAP of the participants. The midpoint is considered the cutoff point., Results: A total of 337 complete responses were received. The majority of participants (62.3%) were female. 291 (86.3%) were considered to have good knowledge, with a mean score of 6.326 out of 9. Moreover, 220 (65.2%) of participants had positive attitudes, with a mean score of 4.077 out of 7. Unfortunately, only 9 (2.6%) participants demonstrated satisfactory practices, with a mean score a 0.911 out of 6. Female participants were significantly better than male participants in terms of knowledge and attitudes, but worse in terms of practices. Sun exposure during safe daytime hours is considered the main cause of vitamin D deficiency. Nearly 60% confirmed that KSA indoor activities and the COVID-19 lockdown might maximise vitamin D deficiency., Conclusion: The study reveals a gap in vitamin D knowledge among participants influenced by gender and study year. It suggests educational initiatives for male students to enhance their knowledge regarding vitamin D, while female students are encouraged to prioritise safe sun exposure, minimising the use of umbrellas and sunscreen. It also highlights the role of educational institutions as reliable sources of information and the influence of the COVID-19 pandemic on participants' attitudes and practices towards vitamin D., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

This work presents a new framework for a competitive evolutionary game between monoclonal antibodies and signalling pathways in oesophageal cancer. The framework is based on a novel dynamical model that takes into account the dynamic progression of signalling pathways, resistance mechanisms and monoclonal antibody therapies. This game involves a scenario in which signalling pathways and monoclonal antibodies are the players competing against each other, where monoclonal antibodies use Brentuximab and Pembrolizumab dosages as strategies to counter the evolutionary resistance strategy implemented by the signalling pathways. Their interactions are described by the dynamical model, which serves as the game's playground. The analysis and computation of two game-theoretic strategies, Stackelberg and Nash equilibria, are conducted within this framework to ascertain the most favourable outcome for the patient. By comparing Stackelberg equilibria with Nash equilibria, numerical experiments show that the Stackelberg equilibria are superior for treating signalling pathways and are critical for the success of monoclonal antibodies in improving oesophageal cancer patient outcomes., Competing Interests: The authors have no competing interests or other interests that might be perceived to influence the results and/or discussion reported in this paper., (© 2024 The Authors.)

Purpose: Despite understanding the connection between obesity and fracture risk, there is limited research on the implications of lower limb fractures on subsequent changes in body mass index (BMI). Our study aimed to assess the impact of lower limb fractures on BMI alterations over an 18-month period., Methods: A multi-center, prospective cohort study was conducted between January 2021 to June 2023, involving 494 adults with lower limb fractures. Participants were recruited within 2 weeks post-injury and were assessed for demographics, injury details, and weight at seven distinct time points. By 18 months, the primary outcome was the mean weight gain., Results: The average age of the participants was 39 (± 12.7) with a baseline weight and BMI of 80.4 kg and 27.6, respectively. At the 18-month follow-up, 75% of patients experienced an average weight increase in 4 kg (± 5.39 kg), equating to a BMI rise of 1.39 (± 1.88). Most patients attributed weight changes to their injury, with nearly half expressing distress from their weight change. Only 37% believed that they had resumed their previous activity levels by the final follow-up. Approximately 31% of the patients sought some form of external weight management care in the form of nutritionist advice, training programs, medication and weight management procedures., Conclusions: Lower limb fractures significantly affect weight gain over an 18-month period, with substantial psychological and physical consequences. Healthcare providers should anticipate potential weight gain post-fracture and incorporate strategies addressing both physical and mental aspects of rehabilitation to enhance recovery outcomes. Early and even immediate weight bearing may play a pivotal role in mitigating weight changes and returning the patient to their previous level of activity. Further detailed studies focusing on different fractures and postoperative interventions are recommended., (© 2024. The Author(s).)

In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a blockchain-based decentralized facial recognition system (DFRS) that has been designed to overcome the complexities of technology. The DFRS takes a trailblazing approach, focusing on finding a critical balance between the benefits of facial recognition and the protection of individuals' private rights in an era of increasing monitoring. First, the facial traits are segmented into separate clusters which are maintained by the specialized node that maintains the data privacy and security. After that, the data obfuscation is done by using generative adversarial networks. To ensure the security and authenticity of the data, the facial data is encoded and stored in the blockchain. The proposed system achieves significant results on the CelebA dataset, which shows the effectiveness of the proposed approach. The proposed model has demonstrated enhanced efficacy over existing methods, attaining 99.80% accuracy on the dataset. The study's results emphasize the system's efficacy, especially in biometrics and privacy-focused applications, demonstrating outstanding precision and efficiency during its implementation. This research provides a complete and novel solution for secure facial recognition and data security for privacy protection.

This study explores the viability of using the avoided mode crossing phenomenon in the microwave regime to design microwave differential sensors. While the design concept can be applied to any type of planar electrically small resonators, here, it is implemented on split-ring resonators (SRRs). We use two coupled synchronous SRRs loaded onto a two-port microstrip line system to demonstrate the avoided mode crossing by varying the distance between the split of the resonators to control the coupling strength. As the coupling becomes stronger, the split in the resonance frequencies of the system increases. Alternatively, by controlling the strength of the coupling by materials under test (MUTs), we utilize the system as a microwave differential sensor. First, the avoided mode crossing is theoretically investigated using the classical microwave coupled resonator techniques. Then, the system is designed and simulated using a 3D full-wave numerical simulation. To validate the concept, a two-port microstrip line, which is magnetically coupled to two synchronous SRRs, is utilized as a sensor, where the inter-resonator coupling is chosen to be electric coupling controlled by the dielectric constant of MUTs. For the experimental validation, the sensor was fabricated using printed circuit board technology. Two solid slabs with dielectric constants of 2.33 and 9.2 were employed to demonstrate the potential of the system as a novel differential microwave sensor.

This study presents a new framework for obtaining personalized optimal treatment strategies targeting aberrant signaling pathways in esophageal cancer, such as the epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF) signaling pathways. A new pharmacokinetic model is developed taking into account specific heterogeneities of these signaling mechanisms. The optimal therapies are designed to be obtained using a three step process. First, a finite-dimensional constrained optimization problem is solved to obtain the parameters of the pharmacokinetic model, using discrete patient data measurements. Next, a sensitivity analysis is carried out to determine which of the parameters are sensitive to the evolution of the variants of EGF receptors and VEGF receptors. Finally, a second optimal control problem is solved based on the sensitivity analysis results, using a modified pharmacokinetic model that incorporates two representative drugs Trastuzumab and Bevacizumab, targeting EGF and VEGF, respectively. Numerical results with the combination of the two drugs demonstrate the efficiency of the proposed framework., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Chronic kidney disease (CKD) is a common comorbid condition in patients undergoing transcatheter aortic valve replacement (TAVR). Reported outcome studies on the association of baseline CKD and mortality is currently limited., Objectives: To determine the prevalence of chronic kidney disease in patients undergoing TAVR and analyse their overall procedural outcomes., Methods: This retrospective observational study was conducted at 43 publicly funded hospitals in Hong Kong. Severe aortic stenosis patients undergoing TAVR between the years 2010 and 2019 were enroled in the study. Two groups were identified according to the presence of baseline chronic kidney disease., Results: A total of 499 patients (228, 58.6% men) were enroled in the study. Baseline hypertension was more prevalent in patients with CKD (82.8%; P =0.003). As for primary end-points, mortality rates of CKD patients were significantly higher compared to non-CKD patients (10% vs. 4.1%; P =0.04%). Gout and hypertension were found to be significantly associated with CRF. Patients with gout were nearly six times more likely to have CRF than those without gout (odds ratio = 5.96, 95% CI = 3.12-11.29, P <0.001). Patients with hypertension had three times the likelihood of having CRF compared to those without hypertension (odds ratio=2.83, 95% CI=1.45-6.08, P =0.004)., Conclusion: In patients with severe aortic stenosis undergoing TAVR, baseline CKD significantly contributes to mortality outcomes at long-term follow up., Competing Interests: No conflict of interest exists for any author on this manuscript.Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Studies have reported differing factors associated with poor outcomes in patients with differentiated thyroid cancer (DTC). We aimed to describe our 20 years of experience in the management of thyroid cancer (TC) and identify predictors of treatment outcomes., Methods: We conducted a retrospective review of medical records of patients with TC seen in the Thyroid Center at King Saud University Medical City (KSUMC) in Riyadh, Saudi Arabia, between the years 2000 and 2020. Demographic and clinical data including pathological characteristics were collected. The American Thyroid Association (ATA) risk stratification was determined for all patients at the postoperative period as well as the response to therapy at the final follow-up visit., Results: A total of 674 patients (mean age: 47.21 years) with TC, 571 (84.7%) of which were women, were included. There were 404 (60.0%) patients with ATA low risk, 127 (18.8%) with intermediate risk, and 143 (21.2%) with high-risk histology. Overall, 461 patients (68.4%) had an excellent response to treatment, 65 (9.6%) had an indeterminate response, 83 (12.3%) had a biochemical incomplete response, and 65 (9.6%) had a structural incomplete response. Patients who had an excellent response were mostly ATA low risk ( n = 318 of 431, 68.1%), whereas 40 of 65 patients (61.5%) of those with ATA high-risk histology had a structural incomplete response to treatment. There were significantly more women who had an excellent response compared with men. Obesity, lymphovascular invasion, and size of the tumor were significant predictors of worse outcomes to therapy., Conclusion: Tumor size, lymphovascular invasion, and obesity are strong predictors of a worse response to therapy among patients with TC. Patients with obesity should be carefully followed up regardless of their risk stratification in light of the recent compelling evidence associating obesity with thyroid cancer and its higher risk of a worse disease outcome. ATA risk stratification is well correlated with patient long-term outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Jammah, AlSadhan, Alyusuf, Alajmi, Alhamoudi and Al-Sofiani.)

This study investigates the determinants of smart government systems that are used in public service organizations in Saudi Arabia. The world's developed nations have conducted studies on smart government systems, but little research has been done on the Middle East, particularly in Saudi Arabia. This study fills the lacuna in the literature. Based on a number of theories including the Technology, Organization, and Environment framework (TOE), Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Models (TAM), the study established an integrated conceptual research model. Online survey questionnaires were sent to 2060 employees in four ministries and after the second reminder a total of 427 completed answers were received, of which 419 (22% response rate) were deemed useable for the analysis. Multivariate statistical analysis was used to analyze the data and results indicated that 51% of the variance (R 2  = 0.51) of employees' perceptions of smart government systems is explained by independent determinants. Findings show that security concerns (t (419) = 2.051, p < 0.041), ICT strategy (t (419) = 4.215, p < 0.000), managerial support (t (419) = 5.027, p < 0.000), incentives (t (419) = 5.263, p < 0.000), and trust (t (419) = -1.957, p < 0.050) are significant predictors of smart government systems acceptance. Meanwhile cultural values (t (419) = 0.669, p < 0.504) and religious values (t (419) = 1.082, p < 0.280) have no significant effect on the attitude to smart system adoption. Perception was found to have a strong significant effect on adoption of smart government systems (t (419) = 8.411, p < 0.000). These results have significant implications for the Saudi government's drive to implement smart government systems in all its agencies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Published by Elsevier Ltd.)

Objective: Addiction is a widespread public health problem despite all efforts to prevent and treat it. Over the past few years, tramadol abuse has been sharply increasing in Middle Eastern countries. This research aims to identify the tramadol-induced histological changes in rat kidneys and any potential protective effects of vitamin C on these changes., Materials and Methods: This is an experimental study conducted at Prince Sattam bin Abdul Aziz University. Thirty-three adult albino rats were randomly divided into three groups. Control, Tramadol, and vitamin C groups. The Tramadol group received 25 mg/ Kg a day of tramadol orally via gastric gavage for three weeks. In the vitamin C + tramadol treated group, 100 mg/Kg/b.wt of vitamin C was administered intravenously to the animals 30 minutes before receiving the same dose of tramadol RESULTS: Specimens from the kidney of every rat were excised for histological examination by the light and electron microscope. Tramadol damage to the kidney's glomeruli and proximal and distal convoluted tubule hypertrophy were among its long-term harmful consequences. When vitamin C was added to tramadol, the distal and proximal convoluted tubules, and the renal glomeruli, improved., Conclusions: When vitamin C was given to the tramadol group, the drug's harmful effects on the kidney were reduced.

Introduction: Electronic cigarettes (e-cigarettes) have become one of the most common forms of nicotine delivery used by youth and young adults worldwide. Given the lack of epidemiologic data in Kuwait, this study sought to estimate the prevalence of e-cigarette use, assess perceptions of harm and addictiveness of e-cigarettes, measure the level of dependence on e-cigarettes and assess factors associated with dependence level among adults., Methods: A cross-sectional study enrolled adults (n=3032, aged ≥18 years) living in Kuwait using a web-based questionnaire. Participants self-reported ever and current (past 30-day) e-cigarette use and self-completed the 10-item Penn State E-cigarette Dependence Index. Associations were evaluated using multinomial logistic regression., Results: The prevalence of ever and current e-cigarette use was estimated to be 40.2% (1220/3032) and 29.4% (892/3032), respectively. The prevalence of current e-cigarette use was higher in males compared to females (47.6% vs 14.4%, p<0.001). Relative to cigarette smoking, 40.6% of participants reported that e-cigarettes are less harmful, and 41.8% indicated that e-cigarettes are equally addictive. Among current e-cigarette users (n=892), 84.8% were ascertained to have developed either low, medium, or high dependence. The use of pod-based devices compared to disposable devices was associated with a high dependence level (adjusted odds ratio, AOR=8.56; 95% CI: 4.52-16.22)., Conclusions: These findings suggest that a large proportion of adults in Kuwait use e-cigarettes, and a considerable proportion of current users have developed dependence. Therefore, such results should alert public health authorities and warrant the development of evidence-based awareness campaigns, policies, and prevention measures to protect and improve the health of people., Competing Interests: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none was reported., (© 2023 Alshaibani M. et al.)