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Non-alcoholic fatty liver disease (NAFLD) and myocardial infarction (MI) are two major health burdens with significant prevalence and mortality. This study aimed to explore the co-expressed genes to understand the relationship between NAFLD and MI and identify potential crucial biomarkers of NAFLD-related MI using bioinformatics and machine learning. Functional enrichment analysis was conducted, a co-protein-protein interaction (PPI) network diagram was constructed, and support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) techniques were employed to identify one differentially expressed gene (DEG), Thrombospondin 1 (THBS1). THBS1 demonstrated strong performance in distinguishing NAFLD patients (AUC = 0.981) and MI patients (AUC = 0.900). Immuno-infiltration analysis revealed significantly lower CD8+ T cells and higher neutrophil levels in patients with NAFLD and MI. CD8+ T cells and neutrophils were effective in distinguishing NAFLD/MI from healthy controls. Correlation analysis showed that THBS1 was positively correlated with CCR (chemokine receptor), MHC class (major histocompatibility complex class), neutrophils, parainflammation, and Tfh (follicular helper T cells), and negatively correlated with CD8+ T cells, cytolytic activity, and TIL (tumor-infiltrating lymphocytes) in NAFLD and MI patients. THBS1 emerged as a novel biomarker for diagnosing NAFLD/MI in comparison to healthy controls. The results indicate that CD8+ T cells and neutrophils could serve as inflammatory immune features for differentiating patients with NAFLD/MI from healthy individuals.
Asunto(s)
Enfermedad del Hígado Graso no Alcohólico , Trombospondina 1 , Humanos , Enfermedad del Hígado Graso no Alcohólico/inmunología , Enfermedad del Hígado Graso no Alcohólico/genética , Enfermedad del Hígado Graso no Alcohólico/metabolismo , Trombospondina 1/genética , Trombospondina 1/metabolismo , Infarto del Miocardio/inmunología , Infarto del Miocardio/metabolismo , Infarto del Miocardio/genética , Máquina de Vectores de Soporte , Biomarcadores/metabolismo , Biomarcadores/análisisRESUMEN
The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative models. These advancements are reshaping data-driven planning strategies, practices, and approaches, thereby facilitating the achievement of environmental sustainability goals. This transformative wave signals a fundamental shift - marked by the synergistic operation of artificial intelligence (AI), artificial intelligence of things (AIoT), and urban digital twin (UDT) technologies. While previous research has largely explored urban AI, urban AIoT, and UDT in isolation, a significant knowledge gap exists regarding their synergistic interplay, collaborative integration, and collective impact on data-driven environmental planning in the dynamic context of sustainable smart cities. To address this gap, this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies, models, and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart cities. Central to this study are four guiding research questions: 1. What theoretical and practical foundations underpin the convergence of AI, AIoT, UDT, data-driven planning, and environmental sustainability in sustainable smart cities, and how can these components be synthesized into a novel comprehensive framework? 2. How does integrating AI and AIoT reshape the landscape of data-driven planning to improve the environmental performance of sustainable smart cities? 3. How can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities? 4. What challenges and barriers arise in integrating and implementing AI, AIoT, and UDT in data-driven environmental urban planning, and what strategies can be devised to surmount or mitigate them? Methodologically, this study involves a rigorous analysis and synthesis of studies published between January 2019 and December 2023, comprising an extensive body of literature totaling 185 studies. The findings of this study surpass mere interdisciplinary theoretical enrichment, offering valuable insights into the transformative potential of integrating AI, AIoT, and UDT technologies to advance sustainable urban development practices. By enhancing data-driven environmental planning processes, these integrated technologies and models offer innovative solutions to address complex environmental challenges. However, this endeavor is fraught with formidable challenges and complexities that require careful navigation and mitigation to achieve desired outcomes. This study serves as a comprehensive reference guide, spurring groundbreaking research endeavors, stimulating practical implementations, informing strategic initiatives, and shaping policy formulations in sustainable urban development. These insights have profound implications for researchers, practitioners, and policymakers, providing a roadmap for fostering resiliently designed, technologically advanced, and environmentally conscious urban environments.
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The rapid advancement of modern communication technologies necessitates the development of generalized multi-access frameworks and the continuous implementation of rate splitting, augmented with semantic awareness. This trend, coupled with the mounting pressure on wireless services, underscores the need for intelligent approaches to radio signal propagation. In response to these challenges, intelligent reflecting surfaces (IRS) have garnered significant attention for their ability to control data transmission systems in a goal-oriented and dynamic manner. This innovation is largely attributed to equitable resource allocation and the dynamic enhancement of network performance. However, the integration of rate-splitting multi-access (RSMA) architecture with semantic considerations imposes stringent requirements on IRS platforms to ensure seamless connectivity and broad coverage for a diverse user base without interference. Semantic communications hinge on a knowledge base-a centralized repository of integrated information related to the transmitted data-which becomes critically important in multi-antenna scenarios. This article proposes a novel set of design strategies for RSMA-IRS systems, enabled by reconfigurable intelligent surface synergizing with semantic communication principles. An experimental analysis is presented, demonstrating the effectiveness of these design guidelines in the context of Beyond 5G/6G communication systems. The RSMA-IRS model, infused with semantic communication, offers a promising solution for future wireless networks. Performance evaluations of the proposed approach reveal that, despite an increase in the number of users, the delay in the RSMA-IRS framework incorporating semantics is 2.94% less than that of a RSMA-IRS system without semantic integration.
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This survey provides a comprehensive insight into the world of non-invasive brain stimulation and focuses on the evolving landscape of deep brain stimulation through microwave research. Non-invasive brain stimulation techniques provide new prospects for comprehending and treating neurological disorders. We investigate the methods shaping the future of deep brain stimulation, emphasizing the role of microwave technology in this transformative journey. Specifically, we explore antenna structures and optimization strategies to enhance the efficiency of high-frequency microwave stimulation. These advancements can potentially revolutionize the field by providing a safer and more precise means of modulating neural activity. Furthermore, we address the challenges that researchers currently face in the realm of microwave brain stimulation. From safety concerns to methodological intricacies, this survey outlines the barriers that must be overcome to fully unlock the potential of this technology. This survey serves as a roadmap for advancing research in microwave brain stimulation, pointing out potential directions and innovations that promise to reshape the field.