Princess Nourah bint Abdulrahman University

Jiao X., Zhou Y., Fujita H.

Cheng M.P., Gonzalez-Bocco I.H., Arbonna-Haddad E., Aleissa M., Chen K., Zhou E., Beluch K., Cho A., Burchett S., Moulton E., Desjardins M., Baden L.R., Koo S., Letourneau A.R., Hammond S.P., et. al.

Introduction: Refractory or resistant cytomegalovirus (CMV) infection and disease pose a significant challenge in immunocompromised patients, including solid organ transplant (SOT) and allogeneic hematopoietic cell transplant (allo-HCT) recipients. This study aimed to evaluate letermovir as a treatment for patients with CMV infection or disease. Methods: We performed an open-label, phase II non-randomized clinical trial. Adult and paediatric (≥12 years of age) patients who had undergone an SOT or allo-HCT and who required antiviral treatment for refractory or resistant CMV or who had CMV with concurrent organ dysfunction were eligible. Patients received letermovir treatment daily for up to 12 weeks with an optional additional 12 weeks of therapy for secondary prophylaxis if clinically indicated. The primary objectives were to evaluate the safety and efficacy of letermovir treatment based upon virological and clinical responses. Results: Ten patients were enrolled in the study, and seven patients completed the study treatment and follow-up period. The overall virological response (defined as a complete virological response at the end of the study period) rate was 60% in the study population. The study drug was well tolerated, as only two patients experienced study drug-related toxicity and only one grade 3 toxicity (elevated ALT) was observed. Letermovir was not associated with acute kidney injury, hepatotoxicity, cardiac arrhythmias, or bone marrow suppression. Conclusion: In this limited sample, letermovir for CMV treatment was safe and well tolerated. Further research is needed to determine if letermovir can be used for the treatment of refractory or resistant CMV infection or disease.

Mohammad H.H., Binhimd S.M., EL-Helbawy A.A., AL-Dayian G.R., Abd EL-Maksoud F.G., Abd Elaal M.K.

In this paper, two different families are mixed: the exponentiated and new Topp–Leone-G families. This yields a new family, which we named the mixture of the exponentiated and new Topp–Leone-G family. Some statistical properties of the proposed family are obtained. Then, the mixture of two exponentiated new Topp–Leone inverse Weibull distribution is introduced as a sub-model from the mixture of exponentiated and new Topp–Leone-G family. Some related properties are studied, such as the quantile function, moments, moment generating function, and order statistics. Furthermore, the maximum likelihood and Bayes approaches are employed to estimate the unknown parameters, reliability and hazard rate functions of the mixture of exponentiated and new Topp–Leone inverse Weibull distribution. Bayes estimators are derived under both the symmetric squared error loss function and the asymmetric linear exponential loss function. The performance of maximum likelihood and Bayes estimators is evaluated through a Monte Carlo simulation. The applicability and flexibility of the MENTL-IW distribution are demonstrated by well-fitting two real-world engineering datasets. The results demonstrate the superior performance of the MENTL-IW distribution compared to other competing models.

Selim I.M., Abdelrehem N.S., Alayed W.M., Elbadawy H.M., Sadek R.A.

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks characterized by dynamic topologies and the absence of fixed infrastructure. These unique features make MANETs critical for applications such as disaster recovery, military operations, and IoT systems. However, they also pose significant challenges for efficient and effective routing. This study evaluates the performance of eight MANET routing protocols: Optimized Link State Routing (OLSR), Destination-Sequenced Distance Vector (DSDV), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV), Temporally Ordered Routing Algorithm (TORA), Zone Routing Protocol (ZRP), and Geographic Routing Protocol (GRP). Using a custom simulation environment in OMNeT++ 6.0.1 with INET-4.5.0, the protocols were tested under four scenarios with varying node densities (20, 80, 200, and 500 nodes). The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. The results reveal that proactive protocols like OLSR are ideal for stable, low-density environments, while reactive protocols such as AOMDV and TORA excel in dynamic, high-mobility scenarios. Hybrid protocols, particularly GRP, demonstrate a balanced approach; achieving superior overall performance with up to 30% lower energy consumption and higher packet delivery ratios compared to reactive protocols. These findings provide practical insights into the optimal selection and deployment of MANET routing protocols for diverse applications, emphasizing the potential of hybrid protocols for modern networks like IoT and emergency response systems.

Eken Ö., Öncü M., Kurtoğlu A., Bozkurt O., Türkmen M., Aldhahi M.I.

Introduction: While napping is recognized as an effective strategy for mitigating insufficient sleep and enhancing athletic recovery, limited research exists on its effects on football players’ anaerobic performance, particularly concerning chronotype variations. This study investigated the impact of strategic napping durations on anaerobic performance and agility in football players under the age of 19 (U19), considering individual chronotypes and psychological factors. Methods: Sixteen young football players (age: 17.18 ± 1.04 years) participated in this crossover randomized controlled study. Participants underwent three conditions: no nap (NoN), 25 min nap (N25), and 60 min nap (N60), with 48 h washout periods between sessions. Performance was assessed using the Countermovement Jump Test (CMJ), Illinois Agility Test, and Illinois Change-of-Direction Test with Ball. Chronotype assessment, sleep quality, and mood states were evaluated using the Morningness-Eveningness Questionnaire, Pittsburgh Sleep Quality Index, and Profile of Mood States Questionnaire, respectively. Results: The 60 min nap protocol demonstrated significant improvements in agility performance compared to other conditions, particularly in the Illinois Agility Test and Change-of-Direction Test with Ball. However, no significant differences were observed in CMJ parameters across napping conditions. Chronotype variations showed correlations with agility performance and psychological factors, with evening-type participants displaying different responses to napping interventions compared to morning-type participants. Conclusions: While a 60 min post-lunch nap did not affect anaerobic performance, it positively influenced agility performance in soccer players. Chronotypic differences significantly impacted both agility performance and associated psychological factors. These findings suggest that integrating napping strategies into athletic training programs, while considering individual chronotypic variations, may present opportunities for enhancing specific aspects of athletic performance. Further research is needed to elucidate the underlying physiological, psychological, and cognitive mechanisms of these effects.

Rezk N.G., Alshathri S., Sayed A., Hemdan E.E., El-Behery H.

Background/Objectives: Brain tumors are among the most aggressive diseases, significantly contributing to human mortality. Typically, the classification of brain tumors is performed through a biopsy, which is often delayed until brain surgery is necessary. An automated image classification technique is crucial for accelerating diagnosis, reducing the need for invasive procedures and minimizing the risk of manual diagnostic errors being made by radiologists. Additionally, the security of sensitive MRI images remains a major concern, with robust encryption methods required to protect patient data from unauthorized access and breaches in Medical Internet of Things (MIoT) systems. Methods: This study proposes a secure and automated MRI image classification system that integrates chaotic and Arnold encryption techniques with hybrid deep learning models using VGG16 and a deep neural network (DNN). The methodology ensures MRI image confidentiality while enabling the accurate classification of brain tumors and not compromising performance. Results: The proposed system demonstrated a high classification performance under both encryption scenarios. For chaotic encryption, it achieved an accuracy of 93.75%, precision of 94.38%, recall of 93.75%, and an F-score of 93.67%. For Arnold encryption, the model attained an accuracy of 94.1%, precision of 96.9%, recall of 94.1%, and an F-score of 96.6%. These results indicate that encrypted images can still be effectively classified, ensuring both security and diagnostic accuracy. Conclusions: The proposed hybrid deep learning approach provides a secure, accurate, and efficient solution for brain tumor detection in MIoT-based healthcare applications. By encrypting MRI images before classification, the system ensures patient data confidentiality while maintaining high diagnostic performance. This approach can empower radiologists and healthcare professionals worldwide, enabling early and secure brain tumor diagnosis without the need for invasive procedures.

Alotaibi A., Alqarras M., Podlasek A., Almanaa A., AlTokhis A., Aldhebaib A., Aldebasi B., Almutairi M., Tench C.R., Almanaa M., Mohammadi-Nejad A., Constantinescu C.S., Dineen R.A., Lee S.

Background and objectives: Type 2 diabetes mellitus (T2DM) affects brain white matter microstructure. While diffusion tensor imaging (DTI) has been used to study white matter abnormalities in T2DM, it lacks specificity for complex white matter tracts. Neurite orientation dispersion and density imaging (NODDI) offers a more specific approach to characterising white matter microstructures. This study aims to explore white matter alterations in T2DM using both DTI and NODDI and assess their association with disease duration and glycaemic control, as indicated by HbA1c levels. Methods and Materials: We analysed white matter microstructure in 48 tracts using data from the UK Biobank, involving 1023 T2DM participants (39% women, mean age 66) and 30,744 non-T2DM controls (53% women, mean age 64). Participants underwent 3.0T multiparametric brain imaging, including T1-weighted and diffusion imaging for DTI and NODDI. We performed region-of-interest analyses on fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), orientation dispersion index (ODI), intracellular volume fraction (ICVF), and isotropic water fraction (IsoVF) to assess white matter abnormalities. Results: We observed reduced FA and ICVF, and increased MD, AD, RD, ODI, and IsoVF in T2DM participants compared to controls (p < 0.05). These changes were associated with longer disease duration and higher HbA1c levels (0 < r ≤ 0.2, p < 0.05). NODDI identified microstructural changes in white matter that were proxies for reduced neurite density and disrupted fibre orientation, correlating with disease progression and poor glucose control. In conclusion, NODDI contributed to DTI in capturing white matter differences in participants with type 2 diabetes, suggesting the feasibility of NODDI in detecting white matter alterations in type 2 diabetes. Type 2 diabetes can cause white matter microstructural abnormalities that have associations with glucose control. Conclusions: The NODDI diffusion model allows the characterisation of white matter neuroaxonal pathology in type 2 diabetes, giving biophysical information for understanding the impact of type 2 diabetes on brain microstructure. Future research should focus on the longitudinal tracking of these microstructural changes to better understand their potential as early biomarkers for cognitive decline in T2DM.

Anjum M., Shahab S., Ahmad S., Whangbo T.

Aims: In the dynamic landscape of healthcare, integrating Artificial Intelligence paradigms has become essential for sophisticated brain image analysis, especially in tumor detection. This research addresses the need for heightened learning precision in handling sensitive medical images by introducing the Fragmented Segment Detection Technique. Background: The ever-evolving healthcare landscape demands advanced methods for brain image analysis, particularly in detecting tumors. This study responds to this need by introducing the Feature Segmentation and Detection Technique (FSDT), a novel approach designed to identify brain tumors precisely using MRI images. The focus is on enhancing detection accuracy, even for diminutive tumors. The primary objective of this study is to introduce and evaluate the efficacy of FSDT in identifying and sizing brain tumors through advanced medical image analysis. The proposed technique utilizes cross-section segmentation and pixel distribution analysis to improve detection accuracy, particularly in size-based tumor detection scenarios. Methods: The proposed technique commences by fragmenting the input through cross-section segmentation, enabling meticulous separation of pixel distribution in various sections. A Convolutional Neural Network then independently operates sequentially on the minimum and maximum representations. The segmented cross-section feature, exhibiting maximum accuracy, is employed in the neural network training process. Finetuning of the neural network optimizes feature distribution and pixel arrangements, specifically in consecutive size-based tumor detection scenarios. Results: The FSDT employs cross-sectional segmentation and pixel distribution analysis to enhance detection accuracy by leveraging a diverse dataset encompassing central nervous system CNS tumors. Comparative evaluations against existing methods, including ERV-Net, MRCNN, and ENet- B0, reveal FSDT's superiority in accuracy, training rate, analysis ratio, precision, recall, F1-score, and computational efficiency. The proposed technique demonstrates a remarkable 10.45% increase in accuracy, 14.12% in training rate, and a 10.78% reduction in analysis time. Conclusion: The proposed FSDT emerges as a promising solution for advancing the accurate identification and sizing of brain tumors through cutting-edge medical image analysis. The demonstrated improvements in accuracy, training rate, and analysis time showcase its potential for effective realworld healthcare applications.

Wogi L., Morawiec M., Ayana T., BaQais A., Urooj S.

Alshardan A., Alahmari S., Alghamdi M., Al Sadig M., Mohamed A., Pasha Mohammed G.

AI-generated content (AIGC) in the context of dermoscopic image analysis describes the application of artificial intelligence (AI) approaches to produce synthetic images for training and enriching machine learning (ML) methods. In dermatology, where dermoscopic images are essential in skin lesion detection, AIGC helps overcome the challenges of inadequate datasets and class imbalances. By employing advanced generative complex systems like Generative Adversarial Networks (GANs), AIGC will simulate different dermoscopic conditions by generating realistic-view synthetic images. These AI-generated images help to enhance the training dataset, contributing to the ML models with many widespread and varied sets of instances. This aids in increasing the ability of the model to precisely predict and generalize while handling new, unnoticed dermoscopic images. Deep learning (DL) methods, specifically convolutional neural networks (CNNs), can provide a massive breakthrough in an extensive range of computer vision (CV) tasks of complex systems, predominantly by exploiting large-scale annotated datasets. This paper develops a GAN-based Synthetic Medical Image Augmentation for the imbalanced Dermoscopic Image Analysis (GANSMIA-CDIA) method. The GANSMIA-CDIA technique’s primary target is exploiting the GAN model for synthetic image generation to handle class imbalance data problems. In addition, the GANSMIA-CDIA technique diagnoses the melanoma using the optimal DL model. The GANSMIA-CDIA technique applies a contrast enhancement process for the noise eradication process. The GANSMIA-CDIA technique follows a feature fusion method comprising different DL methods such as MobileNetV2, AlexNet, ResNet50, and Inceptionv3 to learn feature patterns in the preprocessed images. Meanwhile, the Social Network Search (SNS) technique is utilized for the hyperparameter tuning process. Also, the bidirectional long short-term memory (BiLSTM) technique is implemented to detect and classify melanoma. A series of simulations were performed on the standard dermoscopic image dataset to evaluate the performance of the GANSMIA-CDIA technique. The experimental values indicate the excellence of the GANSMIA-CDIA technique over existing techniques.

Li Q., Li Q., Ling B.W., Pun C., Huang G., Yuan X., Zhong G., Ayouni S., Chen J.

Uddin M., Abdelhaq M., Alsufyani H., Alanazi F., Alsaqour R.

Alshqaq S.S., Fakhfakh R., Alshahrani F.

The free Meixner family (FMF) is the family of measures that produces quadratic Cauchy–Stieltjes Kernel (CSK) families (i.e., meaning that the associated variance function (VF) is a polynomial with degree ≤2 in the mean). Furthermore, a cubic class is introduced in the context of CSK families and is connected to the quadratic class via a reciprocity relation. The associated probability measures are the so-called free analog of the Letac–Mora class (with VF of degree 3). In free probability theory, these two classes of probabilities are crucial. However, a novel transformation of measures is introduced in the setting of free probability, known as the Ta-transformation of probability measures. Denote by P the set of (non-degenerate) real probabilities. For ν∈P and a∈R, consider the transformation of measure ν, denoted Ta(ν), defined by FTa(ν)(w)=Fν(w−a)+a, where Fν(·) is the inverse of the Cauchy–Stieltjes transformation of ν. In this study, we provide important insights into the notion of the Ta-transformation of probabilities. We demonstrate that the FMF (respectively, the free counterpart of the Letac–Mora class of measures) is invariant under the Ta-transformation. Furthermore, we develop additional characteristics of the Ta-transformation, which yield intriguing findings for significant free probability distributions such as the free Poisson and free Gamma distributions.

Alwaili M.A., Alminderej F.M., Alsehli B.R., Rudayni H.A., Allam A.A., Saleh S.M., Mohamed M.A.

Reza M.S., Ghosh A., Reza M.S., Aktarujjaman M., Talukder M.J., Aljazzar S.O., Al-Humaidi J.Y., Mukhrish Y.E.