Ajman University

Sodium-glucose cotransporter-2 inhibitors (SGLT-2is) are widely prescribed for type 2 diabetes due to their glycemic, cardiovascular, and renal benefits. However, concerns have emerged regarding their potential association with cancer. This editorial critically reviews current evidence from clinical trials, meta-analyses, and pharmacovigilance studies, revealing conflicting data on the cancer risk of different SGLT-2is. While some agents, like canagliflozin show protective effects, others, such as empagliflozin and dapagliflozin, have raised concerns for specific malignancies. The heterogeneity of findings underscores the need for long-term, high-quality studies to clarify these risks and guide safe, personalized therapy in diabetic populations.

The advent of mRNA vaccines has heralded a transformative era in oncology, exemplified by the BNT116 mRNA lung cancer vaccine. Leveraging the same ground-breaking technology as COVID-19 vaccines, BNT116 delivers tumor-specific genetic in-structions to the immune system, targeting non-small cell lung cancer (NSCLC), the most prevalent lung cancer subtype. This approach contrasts with conventional therapies that lack precision and often damage healthy tissues. By encoding tumor antigens, BNT116 educates cytotoxic T cells to recognize and eradicate malignant cells, aligning with the principles of precision medicine. Early-phase clinical trials (e.g., NCT05142189) have demonstrated a favorable safety profile and promising antitumor activity, with ongoing re-search exploring its use in combination therapies, such as checkpoint inhibitors. Despite logistical challenges, such as mRNA instability and cold chain requirements, advances in lipid nanoparticle delivery systems are enhancing vaccine stability and efficacy. The adaptability of mRNA technology positions it as a cornerstone for personalized oncology, with potential applications extending to other cancers. Success in the BNT116 trials could redefine NSCLC treatment paradigms, offering a targeted, less cytotoxic alternative. This innovation can not only improve therapeutic outcomes, but also pave the way for preven-tive cancer vaccines, signaling a new dawn in cancer treatment.

Non-invasive diagnostic monitoring techniques have become essential for treating lung cancer (LC), which continues to be the primary cause of cancer-related death worldwide. The new diagnostic biomarkers called tumour-educated platelets (TEPs) show strong prospects for providing vital information about tumor biology, tumor spread pathways, and treatment reaction patterns. Despite lacking a nucleus, platelets exhibit an active RNA profile that develops through interactions with tumor-derived compounds and the tumor microenvironments (TME). This review explains platelet-tumour interaction regulatory mechanisms while focusing on platelet contributions toward cancer development, immune system avoidance, and blood clot formation. The detection and classification of LC show promise through the analysis of RNA molecules extracted from platelets that encompass mRNAs and non-coding RNAs. RNA sequencing technology based on TEP demonstrates excellent diagnostic power by correctly identifying LC patients alongside their oncogenic alterations of EGFR, KRAS, and ALK. Treatment predictions have proven successful using platelet RNA profiles, specifically in immunotherapy and targeted therapy. Integrating next-generation sequencing with machine learning and artificial intelligence enhances TEP-based diagnostic tools, improving detection accuracy. Standardizing platelet extraction methods and vesicle purification from tumor material needs better development for effective and affordable clinical use. Future investigations should combine TEPs with circulating tumor DNA and exosomal RNA markers to enhance both earliest-stage LC diagnosis and patient-specific therapeutic approaches. TEPs introduce a groundbreaking technique in oncology since they can transform non-invasive medical diagnostics and therapeutic monitoring for cancer.

Leukemia is a serious problem affecting both children and adults, leading to death if left untreated. Leukemia is a kind of blood cancer described by the rapid proliferation of abnormal blood cells. An early, trustworthy, and precise identification of leukemia is important to treating and saving patients’ lives. Acute and myelogenous lymphocytic, chronic and myelogenous leukemia are the four kinds of leukemia. Manual inspection of microscopic images is frequently used to identify these malignant growth cells. Leukemia symptoms include fatigue, a lack of enthusiasm, a dull appearance, recurring illnesses, and easy blood loss. Identifying subtypes of leukemia for specialized therapy is one of the hurdles in this area. The suggested work predicts and classifies leukemia subtypes in gene data CuMiDa (GSE9476) using feature selection and ML techniques. The Curated Microarray Database (CuMiDa) collected 64 samples representing five classes of leukemia genes out of 22283 genes. The proposed approach utilizes the 25 most differentiating selected features for classification using machine and deep learning techniques. This study has a classification accuracy of 96.15% using Random Fores, 92.30 using Linear Regression, 96.15% using SVM, and 100% using LSTM. Deep learning methods have been shown to outperform traditional methods in leukemia gene classification by utilizing specific features.

Accurate and efficient medical image segmentation is a critical yet challenging task due to issues like intensity inhomogeneity, poor contrast, noise, and blur. In this paper, we introduce a novel framework that addresses these challenges by leveraging adaptive level set evolution, enhanced with a unique edge indication function. Unlike prior edge-based algorithms, which frequently fail with noisy images and have large computing costs, our method incorporates an improved edge indicator term into the level set architecture, considerably improving performance on degraded images. The efficiency of proposed model depends on the optimization and implementation of proximal alternating direction technique of multipliers (PADMM). Our findings were validated using qualitative and quantitative methods such as dice coefficient assessment, sensitivity, accuracy, and mean absolute distance (MAD). Experimental findings show that the model successfully detects boundaries of objects within noisy and blurred visual data. The algorithm showed exceptional precision through its average dice coefficient of 0.96 which matched the ground truth data measurement standards. The system runs efficiently for only 0.90 seconds on average as a performance result. The framework achieved standout performance metrics that included 0.9552 accuracy together with 0.8854 sensitivity and 0.0796 MAD. The framework demonstrates robust capabilities in medical image evaluation which makes it an optimistic instrument for advancing the field.

This article compares the knowledge and interpretation ability of Chat Generative Pre-Trained Transformer (ChatGPT), with undergraduate dental students by administering a dental anatomy multiple-choice question-based examination. This analytical cross-sectional study determined ChatGPT's justification for each response to evaluate its suitability as an e-learning tool. The frequency and percentage of students and ChatGPT were calculated to obtain the correct answers for a multiple-choice examination. The data analysis was performed through Statistical Package for Social Sciences (SPSS) by IBM (Version 20) and Microsoft Excel by Microsoft Corporation. The frequency and percentage of students and the ChatGPT were calculated for the correct answers. The p-value of the Shapiro–Wilk test was 0.001, therefore, the Kolmogorov test was applied to check the hypothesis for the distribution of the average ChatGPT explanation score given by the experts. The results revealed that students performed better in the introductory dental anatomy examination. The average score of students was 74.28%, while that of ChatGPT was 60%. A good agreement was observed between the experts regarding the grading of the explanation. ChatGPT possesses a foundational understanding of basic dental anatomy, sufficient to achieve a passing grade on an undergraduate examination, its performance exhibits limitations in accuracy and reliability, therefore, it cannot be recommended as a sole learning resource.

The global shift toward sustainable energy and electric mobility addresses environmental concerns related to fossil fuels. While these alternatives are increasingly utilized in residential and commercial sectors, integrating renewable energy in building systems presents significant challenges. This is particularly evident in cold regions where unpredictable resource availability complicates energy reliability. The study emphasizes the need for innovative approaches to address these complexities and ensure consistent energy performance in dynamic conditions. This research explores the energy dynamics within a residential community located in a relatively cold climate region (Tabriz). The study begins by estimating the energy requirements of individual buildings, including the additional demand generated by electric vehicles. It then evaluates the potential for solar energy generation from photovoltaic systems. Finally, a machine learning-based approach (i.e., LSTM, Long Short-Term Memory) is employed to optimize the management of energy supply and demand across the community. This study demonstrates that heating demands in a cold climate are substantially higher than cooling needs, with solar energy providing sufficient (~ 32.1%) coverage during warmer months but requiring grid support in colder seasons. The prediction of EV charging patterns using LSTM models achieved over 93% accuracy, enabling improved energy demand forecasting and load management. These findings highlight the potential for optimizing renewable energy use, reducing grid dependency, and enhancing energy efficiency through effective production-demand management.

The sustainable development of heritage sites is essential for preserving cultural legacies and promoting the well-being of people and the environment. This chapter examines the sustainable management of two UNESCO World Heritage sites in the Middle East: Wadi Hanifah in Saudi Arabia and Dana Biosphere Reserve in Jordan. It explores the social, cultural, and environmental dimensions of heritage management, emphasizing community engagement, conservation ethics, and sustainable development practices. The study highlights the challenges faced by these heritage sites due to rapid urbanization, conflicts, and economic pressures and discusses the need for balancing preservation with economic demands. Additionally, it addresses the importance of environmental sustainability in heritage management and the necessity of mitigating negative impacts on natural ecosystems. Case studies of Wadi Hanifa and Dana Biosphere Reserve illustrate successful models of sustainable development through innovative environmental restoration projects and eco-friendly practices. The paper concludes by emphasizing the significance of achieving sustainable development goals for heritage sites to ensure their long-term preservation and benefits for current and future generations.

In this paper, a compact hybrid ring coupler is designed, simulated, and fabricated to achieve both size reduction and unwanted harmonic suppression in the L-band frequency range. As the demand for miniaturized and high-performance microwave components increases, reducing the footprint of passive devices such as couplers has become a key objective in modern electronic design. The proposed coupler integrates a specially designed resonator between its ports to effectively suppress unwanted harmonics, ensuring improved signal integrity and enhanced operational efficiency. Through this approach, 2nd to 10th harmonics are successfully eliminated, minimizing interference and improving overall system performance. The proposed design operates at a center frequency of 1.4 GHz, achieving an exceptionally low insertion loss of 0.3 dB. Additionally, it demonstrates a remarkable 70% reduction in size compared to conventional couplers while maintaining excellent isolation and phase balance. The design and simulation of the rat-race coupler were conducted using Advanced Design System (ADS) software, and the device was fabricated using a Rogers RT/duroid 5880 substrate with a thickness of 20 mil and a dielectric constant of 2.2. Experimental validation confirms strong agreement between simulated and measured results, highlighting the effectiveness of the proposed structure in achieving compactness and superior performance. This novel design presents a promising solution for modern microwave applications requiring highly efficient, compact, and low-loss couplers.

Background Individuals with disabilities often experience greater challenges in managing oral diseases, including dental caries and periodontal conditions, due to functional limitations. This study aims to: (1) assess the oral health status of disabled individuals in Pakistan and Saudi Arabia, (2) evaluate their oral hygiene knowledge and behaviors, and (3) determine their caries risk using the Caries Management by Risk Assessment (CAMBRA) protocol. Methods A cross-sectional study was conducted on 189 participants aged 13 years and older, including both young people and adults with hearing, visual, or intellectual disabilities from Pakistan and Saudi Arabia between September 2023 and April 2024. The participants were recruited from the Institute of Special Education, Pakistan, and the Saudi Institute of Rehabilitation Medicine, Saudi Arabia. Intraoral examinations and bitewing radiographs assessed oral health, including Decayed, Missing, and Filled Teeth (DMFT) index, Gingival Index, visible plaque, and molar alignment. A self-administered questionnaire gathered sociodemographic data and evaluated oral hygiene knowledge and behaviors. Caries risk was analyzed using the CAMBRA tool. Data were analyzed using descriptive statistics, Chi-square tests, and binary logistic regression. Results The mean DMFT score was 6.30 (SD = 1.83), with a statistically significant difference between Pakistan and Saudi Arabia ( p = 0.007). Gingival health was fair to poor in 47% of participants, while 43.4% exhibited bleeding on probing and 34.9% had visible plaque. Class III malocclusion affected approximately 30% of participants in both countries. Tooth brushing frequency showed a significant difference between the two groups ( p = 0.005). Most participants (76% in Pakistan, 62% in Saudi Arabia) were classified as high caries risk. Deep pits and fissures (69.4%) and frequent snacking (63.8%) were the main risk factors in Pakistan, while frequent snacking (71.6%) and heavy plaque (60.4%) were prevalent in Saudi Arabia. Saudi participants had a significantly higher likelihood of being in the high-risk group for caries (OR = 1.86, 95% CI [0.95–3.65], p = 0.04). Conclusion The disabled individuals in both countries face significant oral health challenges, with high caries risk and poor oral hygiene practices. Targeted preventive measures and improved dental care access are essential to addressing these disparities.

X-linked myotubular myopathy (XLMTM) is a rare neuromuscular disorder caused by mutations in the MTM1 gene, resulting in severe skeletal muscle weakness and respiratory insufficiency. This letter discusses recent advances in gene therapy, particularly the use of adeno-associated virus (AAV)-mediated delivery of MTM1 transgenes. Preliminary clinical trials with AT132 have shown promising improvements in motor function and respiratory capability, though safety concerns, highlighted by a patient fatality, necessitate ongoing monitoring. Challenges include immune responses to AAV vectors, genetic heterogeneity, and age-related therapeutic efficacy. The letter underscores the need for optimized delivery systems, personalized therapeutic strategies, and long-term safety assessments. Emerging gene-editing technologies like CRISPR/Cas9 are also highlighted as potential future interventions.

In series and parallel strings connected Lithium-ion (Li-ion) battery modules or packs, it is essential to equalise each Li-ion cell to enhance the power delivery performance and usable capacity, otherwise, it is restricted by the worst cell in the string. An active cell balancing algorithm based on Charging State-of-Power (CSoP) and Discharging State-of-Power (DSoP) derived from the dynamically estimated State-of-Charge (SoC) or State-of-Health (SoH) is proposed to handle the problem of cell imbalance during both charging and discharging operation. Compared with the voltage-based and SoC-based cell equalization algorithms, the proposed algorithm determines cell imbalance using State-of-Power (SoP) invariance among cells in the battery pack, which allows the Battery Management System (BMS) to regulate the power flow of the Electric Vehicle (EV) with minimum balancing efforts and fully charge/discharge each cell in the battery pack. This ensures the better performance of the proposed cell balancing as compared to other (Voltage/SoC-based) balancing in maximizing the battery pack capacity and minimizing balancing losses. To validate the efficacy of the novel SoP-based cell equalization algorithm, a simulation is conducted in which a Li-ion battery model is built in MATLAB/Simulink platform. The simulation results show that the usable capacity using the proposed SoP-based method is improved by 16% as compared to the usable capacity of the battery pack without-balancing. An experimental setup using four Li-ion cells is also executed to explore the stability, robustness, and precision of the proposed cell balancing algorithm. The parameters of cells differ in capacity and initial SoC from each other to resemble the imbalance among the cells in the battery pack.

The glymphatic system, a vital brain perivascular network for waste clearance, hinges on the functionality of the aquaporin 4 (AQP4) water channel. Alarmingly, AQP4 single nucleotide polymorphisms (SNPs) are linked to impaired glymphatic clearance, or glymphopathy, which contributes to sleep disturbances and various age-related neurodegenerative diseases. Despite the critical role of glymphopathy and sleep disturbances in cerebral small vessel disease (CSVD) – a silent precursor to age-related neurodegenerative disorders – their interplay remains underexplored. CSVD is a major cause of stroke and dementia, yet its pathogenesis is not fully understood. Emerging evidence implicates glymphopathy and sleep disorders as pivotal factors in age-related CSVD, exacerbating the condition by hindering waste removal and compromising blood-brain barrier (BBB) integrity. Advanced imaging techniques promise to enhance diagnosis and monitoring, while lifestyle modifications and personalised medicine present promising treatment avenues. This narrative review underscores the need for a multidisciplinary approach to understanding glymphopathy and sleep disorders in CSVD. By exploring their roles, emphasising the necessity for longitudinal studies, and discussing potential therapeutic interventions, this paper aims to pave the way for new research and therapeutic directions in CSVD management.

Ubiquitin-specific protease 21 (USP21) is a member of the ubiquitin-specific protease subfamily of deubiquitinating enzymes implicated in tumorigenesis and could be a target for anticancer therapy. Remarkably, it has been reported that overexpression and increased activity of USP21 are observed in various types of cancer, which explains the need for its novel small-molecule inhibitors. Plant-based compounds have emerged as promising candidates for therapeutic development due to their diverse biological activities and potential to modulate key molecular targets in disease pathways. In the present study, an integrated virtual screening strategy was adopted using IMPPAT 2.0. database to identify bioactive phytoconstituents that can potentially inhibit USP21. The selected compounds were subjected to physicochemical properties and binding affinity analysis for primary screening against USP21. Pharmacokinetic analysis, PASS evaluation, and interaction studies pinpointed two bioactive phytoconstituents, Ranmogenin A and Tokorogenin, as potential candidates against USP21. Further, molecular dynamics (MD) simulations for 500 ns were performed to analyze the conformational flexibility and stability of USP21-phytoconstituent complexes. The phytoconstituents were found to form stable protein-ligand complexes with USP21 throughout the simulation time. These findings provide a basis for subsequent research on Ranmogenin A and Tokorogenin as promising leads for drug development against USP21 in cancer treatment.

Maintaining genomic stability is essential for detecting DNA damage and activating appropriate responses such as repair, apoptosis, or senescence, primarily mediated by the ATM-p53 axis. ATM is the main sensor of double-strand breaks, and once activated, it will either promote the repair of damaged DNA or eliminate the damaged cells through apoptosis. ATM and p53 mutations upset this equilibrium to cause genomic instability, therapy resistance, and tumor progression in the context of cancer. Oncogene-induced senescence is bypassed by ATM inactivation, which allows cells to progress to become tumors, and p53 mutations allow for uncontrolled proliferation and sensitivity to apoptosis. In addition, persistent ATM signaling can trigger a SASP, which paradoxically further enhances an inflammatory tumor microenvironment and contributes to aging-related diseases and cancer progression. Chemical small molecule p53 activators (PRIMA-1, Nutlin-3) and ATM inhibitors (AZD0156, M4076) sensitize cancer to DNA damaging therapy in cells and nude mice without p53. It remains to be seen whether ATM loss results in ATM/p53 signaling that is always detrimental to tumor proliferation or has context-dependent effects since ATM loss can also promote p53-dependent tumor suppression through senescence and apoptosis in specific cancer types. In this review, we consolidate state-of-the-art findings on ATM and p53 coordination in the processes involved in DNA repair, apoptosis, and senescence to show how ATM and p53 dual involvement in tumor suppression and cancer progression is occurring. It also focuses on therapeutic approaches targeting these pathways to benefit from senescence and intimidating cancer treatment outcomes.

This study examines how differences in nation brand strength affect trade between two countries, how it influences the association between geographic distance and trade, and how it impacts the effects of trade agreements on trade. This study uses panel data on export and import flows between the United States and its 36 major trading partners from 1993 to 2016. A gravity model is developed using a first-order Taylor approximation of multilateral resistance terms and estimated by OLS and PPML. The paper constructs a Nation Brand Distance (NBD) measure, measuring the degree to which nation brand strength scores differ between the United States and its trading partners. NBD is calculated based on differences in the Country Brand Strength Index (CBSI) developed by Fetscherin (2010), consisting of per capita values of exports, tourism, foreign direct investment, immigration, and the government environment. The NBD enters the gravity model by itself and through NBD-geographic distance and NBD-trade agreement interaction terms. The findings suggest that NBD mitigates the negative impact of geographic distance on trade, implying that NBD is a significant factor in explaining bilateral trade and overcoming geographic distance. Moreover, NBD mitigates the positive influence of free trade agreements on exports. This means that free trade agreements are less effective when the NBD between trading partners is significant. Countries must develop their nation’s brand strength to enhance trade. Policymakers should prioritize the development of a strong nation brand to make trade agreements more effective and help overcome the barriers of geographic distance. This means that trade policy strategies should integrate different nation branding initiatives, such as public diplomacy, cultural exchanges, and promoting a positive country image abroad. This study contributes to international trade research, particularly to the stream of New Trade Theory (NTT) studies on different types of distances affecting trade. We introduce a new type of distance, the NBD, to complement the development of NTT. JEL Classification: F14

Urbanisation and reduced natural spaces pose increasing challenges to children’s holistic development in early learning environments. This study investigates how four biophilic design elements—water, plants, animals, and ecosystems—affect the physical, mental, and social well-being of kindergarten children in Henan Province, China. A quantitative questionnaire survey was conducted with children, parents, and teachers from four selected kindergartens. The questionnaire consisted of three parts: demographic information, preferences toward biophilic design elements, and perceived impacts of these elements on children’s development. Considering young children’s limited ability to self-report psychological and emotional states, children’s preferences were statistically compared to those of parents and teachers using IBM SPSS Statistics Version 26. Results showed no significant differences; thus, data from parents and teachers were retained for further analysis. Subsequently, Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to explore relationships between biophilic elements and children’s developmental outcomes. Results indicated that water and animal elements were associated with higher levels of physical activity and psychological resilience, plants were linked to greater social adaptability, and ecosystem landscapes were related to overall indicators of child development. Because the dataset is geographically limited, these quantitative results should be interpreted as exploratory evidence. Importantly, these interventions can be feasibly incorporated into existing facilities, offering practical avenues for swift implementation. To better facilitate such practical implementation, this study synthesises key findings into a comprehensive framework, explicitly outlining how these biophilic elements can be prioritised and effectively integrated into kindergarten designs. Future research is recommended to examine long-term effects and cultural adaptability.