Browse publications by year: 2025

  1. Jameel MH, Yasin A, Tuama AN, Jabbar AH, Kousar S, Mayzan MZH, et al.
    R Soc Open Sci, 2025 Mar;12(3):241560.
    PMID: 40061220 DOI: 10.1098/rsos.241560
    Two-dimensional materials are among the most scientifically accessible materials in material science at the beginning of the twenty-first century. There has been interest in the monolayer transition metal dichalcogenide (TMDC) family because of its large active site surface area for UV photons of light for wastewater treatment. In the present work, density functional theory (DFT) is utilized to model the optical, structural and electrical properties of TMDCs such as NbS2, ZrS2, ReS2 and NbSe2 using the GGA-PBE simulation approximation. Based on DFT calculations, it is determined that NbS2, ZrS2, ReS2 and NbSe2 have zero energy bandgap (E g). The additional gamma-active states that are generated in NbS2, ZrS2, ReS2 and NbSe2 materials aid in the construction of the conduction and valence bands, resulting in a zero E g. In the ultraviolet (UV) spectrum, the increase in optical conductance peaks from 4.5 to 15.7 suggests that the material exhibits stronger absorption or interaction with UV light due to the excitation of electronic transitions or inter-band transitions. The highest optical conductivity and absorbance of two-dimensional TMDCs NbS2, ZrS2, NbSe2 and ReS2 show 2.4 × 105, 2.5 × 105, 2.8 × 105 and 7 × 105 Ω - 1 c m - 1 , respectively. The TMDC family, including two-dimensional TMDCs NbS2, ZrS2, NbSe2 and ReS2, is known for its unique electronic and optical properties. Their layered structure and high surface area make them excellent candidates for applications involving light absorption and photodetection. These materials reduce photon recombination and improve charge transport, making them suitable for photocatalytic and photoanode applications.
  2. Ab Kadir MA, Abdul Manaf R, Mokhtar SA, Ismail LI
    PeerJ, 2025;13:e18851.
    PMID: 40061226 DOI: 10.7717/peerj.18851
    BACKGROUND: Leptospirosis is an endemic disease in countries with tropical climates such as South America, Southern Asia, and Southeast Asia. There has been an increase in leptospirosis incidence in Malaysia from 1.45 to 25.94 cases per 100,000 population between 2005 and 2014. With increasing incidence in Selangor, Malaysia, and frequent climate change dynamics, a study on the disease hotspot areas and their association with the hydroclimatic factors could enhance disease surveillance and public health interventions.

    METHODS: This ecological cross-sectional study utilised a geographic information system (GIS) and remote sensing techniques to analyse the spatiotemporal distribution of leptospirosis in Selangor from 2011 to 2019. Laboratory-confirmed leptospirosis cases (n = 1,045) were obtained from the Selangor State Health Department. Using ArcGIS Pro, spatial autocorrelation analysis (Moran's I) and Getis-Ord Gi* (hotspot analysis) was conducted to identify hotspots based on the monthly aggregated cases for each subdistrict. Satellite-derived rainfall and land surface temperature (LST) data were acquired from NASA's Giovanni EarthData website and processed into monthly averages. These data were integrated into ArcGIS Pro as thematic layers. Machine learning algorithms, including support vector machine (SVM), Random Forest (RF), and light gradient boosting machine (LGBM) were employed to develop predictive models for leptospirosis hotspot areas. Model performance was then evaluated using cross-validation and metrics such as accuracy, precision, sensitivity, and F1-score.

    RESULTS: Moran's I analysis revealed a primarily random distribution of cases across Selangor, with only 20 out of 103 observed having a clustered distribution. Meanwhile, hotspot areas were mainly scattered in subdistricts throughout Selangor with clustering in the central region. Machine learning analysis revealed that the LGBM algorithm had the best performance scores compared to having a cross-validation score of 0.61, a precision score of 0.16, and an F1-score of 0.23. The feature importance score indicated river water level and rainfall contributes most to the model.

    CONCLUSIONS: This GIS-based study identified a primarily sporadic occurrence of leptospirosis in Selangor with minimal spatial clustering. The LGBM algorithm effectively predicted leptospirosis hotspots based on the analysed hydroclimatic factors. The integration of GIS and machine learning offers a promising framework for disease surveillance, facilitating targeted public health interventions in areas at high risk for leptospirosis.

    MeSH terms: Machine Learning; Adolescent; Adult; Aged; Child; Child, Preschool; Cross-Sectional Studies; Female; Humans; Malaysia/epidemiology; Male; Middle Aged; Rain; Incidence; Geographic Information Systems*; Young Adult; Climate Change
  3. Dong Y, Tang L, Badrin S, Badrin S, Wu J
    PeerJ, 2025;13:e19052.
    PMID: 40061230 DOI: 10.7717/peerj.19052
    BACKGROUND: Post-stroke fatigue (PSF) is a common complication experienced by stroke survivors. These individuals often confront psychological challenges such as depression and anxiety, along with significant obstacles like reduced quality of life (QoL) and limitations in activities of daily living (ADLs). Such challenges can profoundly affect their overall recovery and well-being. Despite its prevalence, the associated factors contributing to PSF remain poorly understood. This study aims to primarily investigate these associated factors, while also examining the interrelationships among PSF, depression level, QoL, and ADLs, highlighting the need for a better understanding of these complex interactions.

    METHODS: This cross-sectional study involved 271 stroke survivors and was conducted at the Department of Neurology, Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, China, from September 2023 to January 2024. Participants independently completed the Fatigue Severity Scale (FSS), Patient Health Questionnaire-9 (PHQ-9), and the Short Version of the Stroke-Specific Quality of Life Scale (SV-SS-QoL) as part of a convenience sampling method, while medical professionals assessed the Barthel Index (BI) using the same sampling framework. Multivariable linear regression analyses were employed to determine the factors associated with the persistence of PSF.

    RESULTS: The mean FSS score was 35.04 ± 11.60, while the average score for the SV-SS-QoL was 34.28 ± 9.51, and the BI score averaged 77.79 ± 25.90. Approximately 45.8% of participants (n = 124) experienced PSF. The mean score on the PHQ-9 was 7.63 ± 6.13. A significant negative correlation was identified between fatigue and both QoL and ADLs (P 

    MeSH terms: Adult; Aged; China/epidemiology; Cross-Sectional Studies; Female; Humans; Male; Middle Aged; Risk Factors
  4. Ranade M, Foster MT, Brady PS, Sokol SI, Butty S, Klein A, et al.
    J Soc Cardiovasc Angiogr Interv, 2025 Jan;4(1):102463.
    PMID: 40061412 DOI: 10.1016/j.jscai.2024.102463
    BACKGROUND: There is a need for additional data to assess procedural efficacy and risks associated with mechanical thrombectomy for treating pulmonary embolism (PE) due to its increased utilization and diversity of patient populations presenting with PE. This study evaluated the safety and efficacy of percutaneous mechanical aspiration thrombectomy with the AlphaVac F1885 System (AngioDynamics) in patients with acute intermediate-risk PE.

    METHODS: Patients with acute intermediate-risk PE and a right ventricular (RV)/left ventricular (LV) diameter ratio of ≥0.9 were eligible for enrollment in this prospective, multicenter, single-arm study. The primary effectiveness end point was reduction in the RV/LV ratio at 48 hours. The primary safety end point was the rate of major adverse events (MAEs) defined as subjects who experienced major bleeding, device-related deaths, clinical deterioration, or pulmonary vascular or cardiac injury within 48 hours postprocedurally.

    RESULTS: In total, 122 subjects were enrolled at 25 sites. Mean procedure time was 37.2 ± 17.7 minutes. There were statistically significant reductions in mean 48-hour postprocedural RV/LV diameter ratio (-0.45 ± 0.27; P < .001). Postprocedural mean pulmonary arterial pressure also significantly declined from 27.8 ± 7.8 mm Hg before the procedure to 21.8 ± 7.2 mm Hg (P < .001). There was a 35.5% mean reduction in clot burden as measured by the modified Miller index score. Five (4.1%) subjects developed 7 MAEs during the postprocedural 48-hour assessment period, the majority of which were access site bleeding.

    CONCLUSIONS: Percutaneous mechanical aspiration thrombectomy with the AlphaVac system provided a safe and effective treatment for acute intermediate-risk PE with a significant reduction in RV/LV ratio and clot burden with a low rate of adverse events.

  5. Rajan R, Al Jarallah M, Daoulah A, Panduranga P, Elmahrouk A, Mohamed Al Rawahi AS, et al.
    J Soc Cardiovasc Angiogr Interv, 2025 Jan;4(1):102461.
    PMID: 40061415 DOI: 10.1016/j.jscai.2024.102461
    BACKGROUND: Outcomes of patients with acute myocardial infarction-related cardiogenic shock (AMICS) stratified by the Society for Cardiovascular Angiography & Interventions (SCAI) shock stages in the Gulf region are not well known.

    METHODS: We analyzed data from patients with AMICS presenting to multiple centers across the Gulf region between January 2020 and December 2022. Patients were grouped according to SCAI-Cardiogenic Shock Working Group classification: group 1 (SCAI shock stages B/C) and group 2 (SCAI shock stages D/E). Primary end points were survival at 6, 12, 18, and 24 months. Both univariate and multivariate statistical methods were employed in the analysis.

    RESULTS: A total of 1513 patients from the Gulf Cardiogenic Shock registry, were included with 31.1% in group 1 and 68.9% in group 2. The median follow-up was 6 months. Survival rates in group 1 were 87%, 72%, 56%, and 48% at 6, 12, 18, and 24 months, respectively, whereas group 2 exhibited survival rates of 66%, 29%, 14%, and 4%, respectively, over the same periods. Survival progressively declined with advancing SCAI shock stages, with stage B having the highest survival rates and stage E the lowest (P < .001). Multivariable Cox regression analysis identified higher SCAI stages as strong predictors of increased mortality, with patients in group 2 having a more than 3-fold higher risk of mortality compared to those in group 1 (hazard ratio, 3.13; 95% CI, 2.40-4.07; P < .001). Additionally, lower left ventricular ejection fraction, advanced age, and the presence of tachyarrhythmias were associated with increased mortality risk.

    CONCLUSIONS: This is the first study to validate SCAI-Cardiogenic Shock Working Group stages in a large cohort of patients with AMICS. The SCAI shock staging classification was significantly associated with higher short- and long-term mortality in this cohort, with patients in more advanced stages (D/E) experiencing markedly worse survival outcomes. These findings underscore the utility of SCAI staging in stratifying long-term risk among AMICS patients in the Gulf region. Identification of cardiogenic shock patients at SCAI stages D and E with early hemodynamic monitoring and treating them aggressively with newer mechanical circulatory support in the early stages may improve patient survival.

  6. Nadi F, Najafabadi EF, Rastegari H
    MethodsX, 2025 Jun;14:103223.
    PMID: 40061569 DOI: 10.1016/j.mex.2025.103223
    Maintaining optimal water quality is critical for the success of aquaculture operations, where pH monitoring plays a pivotal role. This study presents a novel approach for pH monitoring in aquaculture ponds by harnessing biomass-based indicators and smartphone-based colorimetric sensing using different setups designs. Three biomass indicators including red cabbage, mango leaf, and used coffee grounds extracts were tested. Standard solutions across a pH range of 1-13 were tested using four setups: black and white polypropylene enclosures, a polyethylene pipe assembly, and a polystyrene tray configuration. The polystyrene tray configuration was determined to be the most effective, as its longer light path (4.5 cm) significantly enhanced color visibility and produced more vibrant color changes, making it ideal for further investigations. The method is as follows:•Water samples were collected from aquaculture ponds. pH were analyzed using this method and standard pH meters.•Mango leaf extract showed strong pH sensitivity and correlation (R² =0.9654).•The mango leaf extract attained a quantification accuracy of 0.5 pH units within a pH range of 3-12.•This smartphone-based approach offers simplicity and ease of implementation empowering aquaculture farmers with a practical tool for monitoring water quality.
  7. Al-Masawa ME, Elfawy LA, Ng CY, Ng MH, Law JX
    Int J Nanomedicine, 2025;20:2673-2693.
    PMID: 40061879 DOI: 10.2147/IJN.S494574
    Atopic dermatitis (AD) is a global concern marked by inflammation, skin barrier dysfunction, and immune dysregulation. Current treatments primarily address symptoms without offering a cure, underscoring the need for innovative therapeutic approaches. Mesenchymal stromal cell-derived extracellular vesicles (MSC-EVs) have attracted attention for their potential in immunomodulation and tissue repair, similar to their parent cells. This review provides a comprehensive analysis of the current landscape of MSC-EV research for AD management. We identified 12 studies that met our predefined inclusion criteria. We thoroughly reviewed both human and animal studies, analyzing aspects such as the source, isolation, and characterization of MSC-EVs, as well as the animal and disease models, dosage strategies, efficacy, mechanisms, and adverse effects. While this review highlights the promising potential of MSC-EV therapy for AD, it also emphasizes significant challenges, including heterogeneity and insufficient reporting. Given that this research area is still in its early stages, addressing these uncertainties will require collaborative efforts among researchers, regulatory bodies, and international societies to advance the field and improve patient outcomes.
    MeSH terms: Animals; Disease Models, Animal; Humans; Mesenchymal Stem Cell Transplantation/methods; Immunomodulation
  8. Shahzadi A, Ishaq K, Nawaz NA, Rosdi F, Khan FA
    PeerJ Comput Sci, 2025;11:e2598.
    PMID: 40062254 DOI: 10.7717/peerj-cs.2598
    Gamification has emerged as a transformative e-business strategy, introducing innovative methods to engage customers and drive sales. This article explores the integration of game design principles into business contexts, termed "gamification," a subject of increasing interest among both scholars and industry professionals. The discussion systematically addresses key themes, like the role of gamification in marketing strategies, enhancing website functionality, and its application within the financial sector, including e-banking, drawing insights from academic and industry perspectives. By conducting a systematic literature review of 48 academic articles published between 2015 and 2024, this study examines the use of personalized, gamification-based strategies to mitigate cyber threats in the financial domain. The review highlights the growing digitization of financial services and the corresponding rise in sophisticated cyber threats, including traditional attacks and advanced persistent threats (APTs). This article critically assesses the evolving landscape of cyber threats specific to the financial industry, identifying trends, challenges, and innovative solutions to strengthen cybersecurity practices. Of particular interest is the application of AI-enhanced gamification strategies to reinforce cybersecurity protocols, particularly in the face of novel threats in gaming platforms. Furthermore, the review evaluates techniques grounded in user behavior, motivation, and readiness to enhance cybersecurity. The article also offers a comprehensive taxonomy of financial services, categorizing cyber threats into game-based (e.g., phishing, malware, APTs) and non-game-based (e.g., social engineering, compliance issues) threats. AI-driven measures for prevention and detection emphasize regular security assessments, user training, and system monitoring with incident response plans. This research provides valuable insights into the intersection of gamification and cybersecurity, offering a forward-looking perspective for both academic researchers and industry professionals.
  9. Liu M, Khairuddin ASM, Hasikin K, Liu W
    PeerJ Comput Sci, 2025;11:e2725.
    PMID: 40062258 DOI: 10.7717/peerj-cs.2725
    The fundamental aspects of multimodal applications such as image-text matching, and cross-modal heterogeneity gap between images and texts have always been challenging and complex. Researchers strive to overcome the challenges by proposing numerous significant efforts directed toward narrowing the semantic gap between visual and textual modalities. However, existing methods are usually limited to computing the similarity between images (image regions) and text (text words), ignoring the semantic consistency between fine-grained matching of word regions and coarse-grained overall matching of image and text. Additionally, these methods often ignore the semantic differences across different feature dimensions. Such limitations may result in an overemphasis on specific details at the expense of holistic understanding during image-text matching. To tackle this challenge, this article proposes a new Cross-Dimensional Coarse-Fine-Grained Complementary Network (CDGCN). Firstly, the proposed CDGCN performs fine-grained semantic alignment of image regions and sentence words based on cross-dimensional dependencies. Next, a Coarse-Grained Cross-Dimensional Semantic Aggregation module (CGDSA) is developed to complement local alignment with global image-text matching ensuring semantic consistency. This module aggregates local features across different dimensions as well as within the same dimension to form coherent global features, thus preserving the semantic integrity of the information. The proposed CDGCN is evaluated on two multimodal datasets, Flickr30K and MS-COCO against state-of-the-art methods. The proposed CDGCN achieved substantial improvements with performance increment of 7.7-16% for both datasets.
  10. Shamrat FMJM, Khalid M, Qadah TM, Farrash M, Alshanbari H
    PeerJ Comput Sci, 2025;11:e2682.
    PMID: 40062267 DOI: 10.7717/peerj-cs.2682
    As the world grapples with pandemics and increasing stress levels among individuals, heart failure (HF) has emerged as a prominent cause of mortality on a global scale. The most effective approach to improving the chances of individuals' survival is to diagnose this condition at an early stage. Researchers widely utilize supervised feature selection techniques alongside conventional standalone machine learning (ML) algorithms to achieve the goal. However, these approaches may not consistently demonstrate robust performance when applied to data that they have not encountered before, and struggle to discern intricate patterns within the data. Hence, we present a Multi-objective Stacked Enable Hybrid Model (MO-SEHM), that aims to find out the best feature subsets out of numerous different sets, considering multiple objectives. The Stacked Enable Hybrid Model (SEHM) plays the role of classifier and integrates with a multi-objective feature selection method, the Non-dominated Sorting Genetic Algorithm II (NSGA-II). We employed an HF dataset from the Faisalabad Institute of Cardiology (FIOC) and evaluated six ML models, including SEHM with and without NSGA-II for experimental purposes. The Pareto front (PF) demonstrates that our introduced MO-SEHM surpasses the other models, obtaining 94.87% accuracy with the nine relevant features. Finally, we have applied Local Interpretable Model-agnostic Explanations (LIME) with MO-SEHM to explain the reasons for individual outcomes, which makes our model transparent to the patients and stakeholders.
  11. Mir RR, Ul Haq N, Ishaq K, Safie N, Dogar AB
    PeerJ Comput Sci, 2025;11:e2568.
    PMID: 40062277 DOI: 10.7717/peerj-cs.2568
    Self-awareness and self-management in diabetes are critical as they enhance patient well-being, decrease financial burden, and alleviate strain on healthcare systems by mitigating complications and promoting healthier life expectancy. Incomplete understanding persists regarding the synergistic effects of diet and exercise on diabetes management, as existing research often isolates these factors, creating a knowledge gap in comprehending their combined influence. Current diabetes research overlooks the interplay between diet and exercise in self-management. A holistic study is crucial to mitigate complications and healthcare burdens effectively. Multi-dimensional research questions covering complete diabetic management such as publication channels for diabetic research, existing machine learning solutions, physical activity tacking existing methods, and diabetic-associated datasets are included in this research. In this study, using a proper research protocol primary research articles related to diet, exercise, datasets, and blood analysis are selected and their quality is assessed for diabetic management. This study interrelates two major dimensions of diabetes management together that are diet and exercise.
  12. Fang C, Ullah N, Batumalay M, Al-Rahmi WM, Alblehai F
    PeerJ Comput Sci, 2025;11:e2466.
    PMID: 40062281 DOI: 10.7717/peerj-cs.2466
    The COVID-19 pandemic has had a significant impact on small and medium-sized enterprises (SMEs), leading to disruptions in supply chains, financial losses, and closures. To overcome these challenges, organizations, including those in developing economies like Malaysia, are turning to blockchain technology as a solution to enhance traditional supply chain management frameworks. This study aims to identify the factors that influence the acceptance of blockchain technology among SMEs. By drawing on established adoption theories such as the technology acceptance model (TAM), diffusion of innovation (DOI) theory, and theory of planned behavior (TPB), the researchers developed a research framework. They utilized partial least square structural equation modeling (PLS-SEM) to analyze the causal relationships between different constructs and test their hypotheses. The findings confirmed that the constructs of the technology acceptance model, specifically perceived usefulness, perceived ease of use and attitude were significantly associated with the intention to use blockchain technology. Additionally, the constructs of the diffusion of innovation theory, relative advantage and compatibility, showed significant associations with perceived ease of use, while complexity had a negligible relationship with perceived usefulness and perceived ease of use. The construct of subjective norms from the theory of planned behavior exhibited a significant relationship with perceived usefulness and an insignificant relationship with intention to use. Finally, perceived behavioral control demonstrated a positive relationship with intention to use. The study's findings provide valuable insights for blockchain developers and organizations aiming to make informed decisions regarding the application of blockchain technology as a process innovation in SMEs.
  13. El-Shorbagy MA, Bouaouda A, Abualigah L, Hashim FA
    PeerJ Comput Sci, 2025;11:e2722.
    PMID: 40062283 DOI: 10.7717/peerj-cs.2722
    The Atom Search Optimization (ASO) algorithm is a recent advancement in metaheuristic optimization inspired by principles of molecular dynamics. It mathematically models and simulates the natural behavior of atoms, with interactions governed by forces derived from the Lennard-Jones potential and constraint forces based on bond-length potentials. Since its inception in 2019, it has been successfully applied to various challenges across diverse fields in technology and science. Despite its notable achievements and the rapidly growing body of literature on ASO in the metaheuristic optimization domain, a comprehensive study evaluating the success of its various implementations is still lacking. To address this gap, this article provides a thorough review of half a decade of advancements in ASO research, synthesizing a wide range of studies to highlight key ASO variants, their foundational principles, and significant achievements. It examines diverse applications, including single- and multi-objective optimization problems, and introduces a well-structured taxonomy to guide future exploration in ASO-related research. The reviewed literature reveals that several variants of the ASO algorithm, including modifications, hybridizations, and multi-objective implementations, have been developed to tackle complex optimization problems. Moreover, ASO has been effectively applied across various domains, such as engineering, healthcare and medical applications, Internet of Things and communication, clustering and data mining, environmental modeling, and security, with engineering emerging as the most prevalent application area. By addressing the common challenges researchers face in selecting appropriate algorithms for real-world problems, this study provides valuable insights into the practical applications of ASO and offers guidance for designing ASO variants tailored to specific optimization problems.
  14. Halabouni M, Roslee M, Mitani S, Abuajwa O, Osman A, Binti Ali FZ, et al.
    PeerJ Comput Sci, 2025;11:e2388.
    PMID: 40062289 DOI: 10.7717/peerj-cs.2388
    Non-orthogonal multiple access (NOMA) is a technology that leverages user channel gains, offers higher spectral efficiency, improves user fairness, better cell-edge throughput, increased reliability, and low latency, making it a potential technology for the next generation of cellular networks. The application of NOMA in the power domain (NOMA-PD) with multiple-input multiple-output (MIMO) and other emerging technologies allows to achieve the demand for higher data rates in next-generation networks. This survey aims to funnel down NOMA MIMO resource allocation issues and different optimization problems that exist in the literature to enhance the data rate. We examine the most recent NOMA-MIMO clustering, power allocation, and joint allocation schemes and analyze various parameters used in optimization methods to design 5G systems. We finally identify a promising research problem based on the signal-to-interference-plus-noise ratio (SINR) parameter in the context of NOMA-PD with MIMO configuration.
  15. Liu Y, Abidin SZ, Vermol VV, Yang S, Liu H
    PeerJ Comput Sci, 2025;11:e2707.
    PMID: 40062301 DOI: 10.7717/peerj-cs.2707
    With the rapid development of e-commerce and the increasing aging population, more elderly people are engaging in online shopping. However, challenges they face during this process are becoming more apparent. This article proposes a recommendation system based on click-through rate (CTR) prediction, aiming to enhance the online shopping experience for elderly users. By analyzing user characteristics, product features, and their interactions, we constructed a model combining bidirectional long short-term memory (Bi-LSTM) and multi-head self-attention mechanism to predict the item click behavior of elderly users in the recommendation section. Experimental results demonstrated that the model excels in CTR prediction, effectively improving the relevance of recommended content. Compared to the baseline model long short-term memory (LSTM), the GATI-RS framework improved CTR prediction accuracy by 40%, and its loss function rapidly decreased and remained stable during training. Additionally, the GATI-RS framework showed significant performance improvement when considering only elderly users, with accuracy surpassing the baseline model by 42%. These results indicate that the GATI-RS framework, through optimized algorithms, significantly enhances the model's global information integration and complex pattern recognition capabilities, providing strong support for developing recommendation systems for elderly online shoppers. This research not only offers new insights for e-commerce platforms to optimize services but also contributes to improving the quality of life and well-being of the elderly.
  16. Suzelan Amir NA, Abd Latiff FN, Wong KB, Mior Othman WA
    PeerJ Comput Sci, 2025;11:e2665.
    PMID: 40062304 DOI: 10.7717/peerj-cs.2665
    The transmission of healthcare data plays a vital role in cities worldwide, facilitating access to patient's health information across healthcare systems and contributing to the enhancement of care services. Ensuring secure healthcare transmission requires that the transmitted data be reliable. However, verifying this reliability can potentially compromise patient privacy. Given the sensitive nature of health information, preserving privacy remains a paramount concern in healthcare systems. In this work, we present a novel secure communication scheme that leverages a chaos cryptosystem to address the critical concerns of reliability and privacy in healthcare data transmission. Chaos-based cryptosystems are particularly well-suited for such applications due to their inherent sensitivity to initial conditions, which significantly enhances resistance to adversarial violations. This property makes the chaos-based approach highly effective in ensuring the security of sensitive healthcare data. The proposed chaos cryptosystem in this work is built upon the synchronization of fractional-order chaotic systems with varying structures and orders. The synchronization between the primary system (PS) and the secondary system (SS) is achieved through the application of Lyapunov stability theory. For the encryption and decryption of sensitive healthcare data, the scheme employs the n-shift encryption principle. Furthermore, a detailed analysis of the key space was conducted to ensure the scheme's robustness against potential attacks. Numerical simulations were also performed to validate the effectiveness of the proposed scheme.
  17. Salisu S, Danyaro KU, Nasser M, Hayder IM, Younis HA
    PeerJ Comput Sci, 2025;11:e2574.
    PMID: 40062308 DOI: 10.7717/peerj-cs.2574
    Human pose estimation (HPE) is designed to detect and localize various parts of the human body and represent them as a kinematic structure based on input data like images and videos. Three-dimensional (3D) HPE involves determining the positions of articulated joints in 3D space. Given its wide-ranging applications, HPE has become one of the fastest-growing areas in computer vision and artificial intelligence. This review highlights the latest advances in 3D deep-learning-based HPE models, addressing the major challenges such as accuracy, real-time performance, and data constraints. We assess the most widely used datasets and evaluation metrics, providing a comparison of leading algorithms in terms of precision and computational efficiency in tabular form. The review identifies key applications of HPE in industries like healthcare, security, and entertainment. Our findings suggest that while deep learning models have made significant strides, challenges in handling occlusion, real-time estimation, and generalization remain. This study also outlines future research directions, offering a roadmap for both new and experienced researchers to further develop 3D HPE models using deep learning.
  18. George RE, Dueñas AN, Brown MEL, Oladipo ETO, Danquah A, Nadarajah VDV, et al.
    Med Teach, 2025 Mar 10.
    PMID: 40062547 DOI: 10.1080/0142159X.2025.2459363
    SERIES INTRODUCTORY ABSTRACTWidening Access, Participation and SuccessThis AMEE guide series explores three interconnected strategies for widening access, participation, and success in medical education. The series emphasises the interdependence of these areas as essential to supporting students from entry to graduation. Each guide in the series addresses a specific phase of the student journey, from the first steps of widening access, through enhancing participation during their studies, to supporting their ultimate success. Aimed at supporting students from disadvantaged, under-represented, and culturally diverse backgrounds, these guides offer practical insights and examples. Through this series, we provide a roadmap to ensure that access, participation, and success are not treated as isolated concepts but as essential, interdependent elements that together support students throughout their journey. By presenting a cohesive framework that connects these three areas, the series aims to build a shared understanding and foster systemic changes that promote equity across all stages of medical education-from admissions to graduation and beyond. In addressing these issues holistically, medical schools can ensure that students not only enter and participate but also succeed and thrive, fulfilling their potential as future doctors.
  19. Feng G, Yilmaz Y, Valenti L, Seto WK, Pan CQ, Méndez-Sánchez N, et al.
    Liver Int, 2025 Apr;45(4):e70058.
    PMID: 40062742 DOI: 10.1111/liv.70058
    BACKGROUND: This study utilised the Global Burden of Disease data (2010-2021) to analyse the rates and trends in point prevalence, annual incidence and years lived with disability (YLDs) for major chronic liver diseases, such as hepatitis B, hepatitis C, metabolic dysfunction-associated liver disease, cirrhosis and other chronic liver diseases.

    METHODS: Age-standardised rates per 100,000 population for prevalence, annual incidence and YLDs were compared across regions and countries, as well as the socio-demographic index (SDI). Trends were expressed as percentage changes (PC) and estimates were reported with uncertainty intervals (UI).

    RESULTS: Globally, in 2021, the age-standardised rates per 100,000 population for the prevalence of hepatitis B, hepatitis C, MASLD and cirrhosis and other chronic liver diseases were 3583.6 (95%UI 3293.6-3887.7), 1717.8 (1385.5-2075.3), 15018.1 (13756.5-16361.4) and 20302.6 (18845.2-21791.9) respectively. From 2010 to 2021, the PC in age-standardised prevalence rates were-20.4% for hepatitis B, -5.1% for hepatitis C, +11.2% for MASLD and + 2.6% for cirrhosis and other chronic liver diseases. Over the same period, the PC in age-standardized incidence rates were -24.7%, -6.8%, +3.2%, and +3.0%, respectively. Generally, negative associations, but with fluctuations, were found between age-standardised prevalence rates for hepatitis B, hepatitis C, cirrhosis and other chronic liver diseases and the SDI at a global level. However, MASLD prevalence peaked at moderate SDI levels.

    CONCLUSIONS: The global burden of chronic liver diseases remains substantial. Hepatitis B and C have decreased in prevalence and incidence in the last decade, while MASLD, cirrhosis and other chronic liver diseases have increased, necessitating targeted public health strategies and resource allocation.

    MeSH terms: Adult; Aged; Chronic Disease/epidemiology; Female; Humans; Liver Cirrhosis/epidemiology; Male; Middle Aged; Global Health/statistics & numerical data; Incidence; Prevalence; Hepatitis C, Chronic/epidemiology
  20. Ren OY, Siddiqui Y, Ansari MT, Ali A
    J Asian Nat Prod Res, 2025 Mar 10.
    PMID: 40063011 DOI: 10.1080/10286020.2025.2459597
    This review explores the therapeutic potential of resveratrol, focusing on its molecular and cellular effects on Chronic Obstructive Pulmonary Disease (COPD), bioavailability enhancement strategies, and development challenges for applications in food, pharmaceuticals, and postharvest sectors. Resveratrol protects lungs by activating SIRT1, reducing oxidative stress via ROS regulation through Nrf2-mediated antioxidant enzymes. It shows promise as an alternative to corticosteroids in COPD and cancer treatment. Encapsulation innovations in resveratrol offer opportunities for food fortification, minimizing risks of chemical degradation and isomerization during storage, paving the way for its broader utility in health and nutrition applications.
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