Association between systemic inflammation response index and chronic kidney disease: a population-based study

IntroductionOur objective was to explore the potential link between systemic inflammation response index (SIRI) and chronic kidney disease (CKD).MethodsThe data used in this study came from the National Health and Nutrition Examination Survey (NHANES), which gathers data between 1999 and 2020. CKD was diagnosed based on the low estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m2 or albuminuria (urinary albumin-to-creatinine ratio (ACR) of more than 30 mg/g). Using generalized additive models and weighted multivariable logistic regression, the independent relationships between SIRI and other inflammatory biomarkers (systemic immune-inflammation index (SII), monocyte/high-density lipoprotein ratio (MHR), neutrophil/high-density lipoprotein ratio (NHR), platelet/high-density lipoprotein ratio (PHR), and lymphocyte/high-density lipoprotein ratio (LHR)) with CKD, albuminuria, and low-eGFR were examined.ResultsAmong the recruited 41,089 participants, males accounted for 49.77% of the total. Low-eGFR, albuminuria, and CKD were prevalent in 8.30%, 12.16%, and 17.68% of people, respectively. SIRI and CKD were shown to be positively correlated in the study (OR = 1.24; 95% CI: 1.19, 1.30). Furthermore, a nonlinear correlation was discovered between SIRI and CKD. SIRI and CKD are both positively correlated on the two sides of the breakpoint (SIRI = 2.04). Moreover, increased SIRI levels were associated with greater prevalences of low-eGFR and albuminuria (albuminuria: OR = 1.27; 95% CI: 1.21, 1.32; low-eGFR: OR = 1.11; 95% CI: 1.05, 1.18). ROC analysis demonstrated that, compared to other inflammatory indices (SII, NHR, LHR, MHR, and PHR), SIRI exhibited superior discriminative ability and accuracy in predicting CKD, albuminuria, and low-eGFR.DiscussionWhen predicting CKD, albuminuria, and low-eGFR, SIRI may show up as a superior inflammatory biomarker when compared to other inflammatory biomarkers (SII, NHR, LHR, MHR, and PHR). American adults with elevated levels of SIRI, SII, NHR, MHR, and PHR should be attentive to the potential risks to their kidney health

Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective

As generative artificial intelligence (GAI) models continue to evolve, their generative capabilities are increasingly enhanced and being used extensively in content generation. Beyond this, GAI also excels in data modeling and analysis, benefitting wireless communication systems. In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems. Specifically, we first provide an overview of GAI and ISAC, touching on GAI's potential support across multiple layers of ISAC. We then concentrate on the physical layer, investigating GAI's applications from various perspectives thoroughly, such as channel estimation, and demonstrate the value of these GAI-enhanced physical layer technologies for ISAC systems. In the case study, the proposed diffusion model-based method effectively estimates the signal direction of arrival under the near-field condition based on the uniform linear array, when antenna spacing surpassing half the wavelength. With a mean square error of 1.03 degrees, it confirms GAI's support for the physical layer in near-field sensing and communications

Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study

As the next-generation paradigm for content creation, AI-Generated Content (AIGC), i.e., generating content automatically by Generative AI (GAI) based on user prompts, has gained great attention and success recently. With the ever-increasing power of GAI, especially the emergence of Pretrained Foundation Models (PFMs) that contain billions of parameters and prompt engineering methods (i.e., finding the best prompts for the given task), the application range of AIGC is rapidly expanding, covering various forms of information for human, systems, and networks, such as network designs, channel coding, and optimization solutions. In this article, we present the concept of mobile-edge AI-Generated Everything (AIGX). Specifically, we first review the building blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX applications. Then, we present a unified mobile-edge AIGX framework, which employs edge devices to provide PFM-empowered AIGX services and optimizes such services via prompt engineering. More importantly, we demonstrate that suboptimal prompts lead to poor generation quality, which adversely affects user satisfaction, edge network performance, and resource utilization. Accordingly, we conduct a case study, showcasing how to train an effective prompt optimizer using ChatGPT and investigating how much improvement is possible with prompt engineering in terms of user experience, quality of generation, and network performance.Comment: 9 pages, 6 figur

User-Centric Interactive AI for Distributed Diffusion Model-based AI-Generated Content

Distributed Artificial Intelligence-Generated Content (AIGC) has attracted increasing attention. However, it faces two significant challenges: how to maximize the subjective Quality of Experience (QoE) and how to enhance the energy efficiency, which are particularly pronounced in widely adopted Generative Diffusion Model (GDM)-based AIGC services for image generation. In this paper, we propose a novel user-centric Interactive AI (IAI) approach for service management, with a distributed GDM-based AIGC framework, prioritizing efficient and collaborative GDM deployment. Specifically, we restructure the GDM's inference process, i.e., the denoising chain, to enable users' semantically similar prompts to share a portion of diffusion steps. Furthermore, to maximize the users' subjective QoE, we propose an IAI approach, i.e., Reinforcement Learning With Large Language Models Interaction (RLLI), which utilizes Large Language Model (LLM)-empowered generative agents to replicate users interaction, providing real-time and subjective QoE feedback that reflects a spectrum of user personalities. Lastly, we present the GDM-based Deep Deterministic Policy Gradient (G-DDPG) algorithm, adapted to the proposed RLLI framework, for effective communication and computing resource allocation while considering user subjective personalities and dynamic wireless environments in decision-making. Simulation results show that G-DDPG can increase the sum QoE by 15%, compared with the conventional DDPG algorithm

A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications

Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive survey of research on XL-MIMO wireless systems. In particular, we introduce four XL-MIMO hardware architectures: uniform linear array (ULA)-based XL-MIMO, uniform planar array (UPA)-based XL-MIMO utilizing either patch antennas or point antennas, and continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze and discuss their characteristics and interrelationships. Following this, we examine exact and approximate near-field channel models for XL-MIMO. Given the distinct electromagnetic properties of near-field communications, we present a range of channel models to demonstrate the benefits of XL-MIMO. We further motivate and discuss low-complexity signal processing schemes to promote the practical implementation of XL-MIMO. Furthermore, we explore the interplay between XL-MIMO and other emergent 6G technologies. Finally, we outline several compelling research directions for future XL-MIMO wireless communication systems.Comment: 38 pages, 10 figure

Associations between estradiol and hyperuricemia and the mediating effects of TC, TG, and TyG: NHANES 2013–2016

ObjectivesTo explore the relationship between estradiol (E2) and the incidence of hyperuricemia (HUA) in adult women and to explore whether glucolipid metabolism disorders play a mediating role in mediating this relationship.MethodsA total of 2,941 participants aged 20–65 years were included in the National Health and Nutrition Examination Survey (NHANES) 2013–2016. Multivariate logistic regression analysis was performed to evaluate the correlations of E2 with HUA. Multivariate linear regression analysis was performed to evaluate the associations between E2 and triglyceride (TG), total cholesterol (TC), and the triglyceride-glucose index (TyG). The restricted cubic spline (RCS) model was used to further explore the association between E2 and HUA and between TG, TC, and TyG and HUA. Mediation analyses were performed to examine whether TC, TG, and TyG mediated the relationship between E2 and HUA.ResultsAfter adjusting for covariates, logistic regression revealed that ln(E2) was significantly associated with HUA in the female subgroup (p = 0.035) and that the incidence of HUA tended to increase with decreasing ln(E2) (p for trend = 0.026). Linear regression showed that E2 was significantly associated with TC (p = 0.032), TG (p = 0.019), and TyG (p = 0.048). The RCS model showed that ln(E2) was linearly correlated with the incidence of HUA (p-overall = 0.0106, p-non-linear = 0.3030). TC and TyG were linearly correlated with HUA (TC: p-overall = 0.0039, p-non-linear = 0.4774; TyG: p-overall = 0.0082, p-non-linear = 0.0663), whereas TG was non-linearly correlated with HUA. Mediation analyses revealed that TC, TG, and TyG significantly mediated the relationship between ln(E2) and HUA (TC, indirect effect: −0.00148, 7.5%, p = 0.008; TG, indirect effect: −0.00062, 3.1%, p = 0.004; TyG, indirect effect: −0.00113, 5.6%, p = 0.016).ConclusionIn conclusion, this study demonstrated that compared with women aged 20–45 years, women aged 45–55 years and 55–65 years had lower E2 levels and a greater incidence of HUA. E2 levels and the incidence of HUA were negatively associated in female individuals but not in male individuals. In addition, TC, TG, and TyG, which are markers of glucolipid metabolism, played a mediating role in the association between E2 and HUA

Insight into the dynamics of dissolved organic matter components under latitude change perturbation

Dissolved organic matter (DOM) which can help the transportation of nutrients and pollutants plays essential role in the aquatic ecosystems. However, the dynamics of individual DOM component under the change of latitude have not been elucidated to date. The composition and dynamics of DOM were assessed in this study. Two individual parallel factor analysis (PARAFAC) components were found in each sampling site in Heilongjiang. To further characterize the inner change of the identified PARAFAC components, two-latitude correlation spectroscopy (2DCOS) technique was applied to the excitation loadings data. Interestingly, not all the fluorophore in a PARAFAC component change in the same direction as the overall change of a component. From upstream to downstream, the peak A1 in PARAFAC component C1 showed a downward trend, but peak A2 presented an upward trend. In PARAFAC component C2, the peak T2 and peak T3 showed an inverse changing trend under latitude perturbation. Furthermore, basic nutrients parameters in Heilongjiang were also characterized in each sampling sites. The relationships between DOM and nutrients showed that component C1 made a significant contribution to chemical oxygen demand (COD) and biochemical oxygen demand (BOD5). The evolutions of DOM peak A1 and peak A2 were accompanied by the changing of Total phosphorus (TP). The findings in this study could make a contribution to explore the fate of DOM in high humic-like substance containing river

Optimizing Attribute Reduction in Multi-Granularity Data through a Hybrid Supervised–Unsupervised Model

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have numerous shortcomings, such as addressing complex constraints and conducting multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This novel method stands out by addressing complex constraints and facilitating multi-perspective effectiveness evaluations. It has several advantages: (1) it combines supervised and unsupervised learning methods, allowing for nuanced data interpretation and enhanced attribute selection; (2) by incorporating multi-granularity structures, the algorithm can analyze data at various levels of granularity. This allows for a more detailed understanding of data characteristics at each level, which can be crucial for complex datasets; and (3) by using neighborhood relations instead of indiscernibility relations, the method effectively handles uncertain and fuzzy data, making it suitable for real-world datasets that often contain imprecise or incomplete information. It not only selects the optimal granularity level or attribute set based on specific requirements, but also demonstrates its versatility and robustness through extensive experiments on 15 UCI datasets. Comparative analyses against six established attribute reduction algorithms confirms the superior reliability and consistency of our proposed method. This research not only enhances the understanding of attribute reduction mechanisms, but also sets a new benchmark for future explorations in the field

Uncoordinated expression of DNA methylation-related enzymes in human cancer

Abstract Background In addition to the important roles played by 5-methylcytosine (5mC), emerging evidence suggests that 5mC derivatives, such as 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC), also exhibit regulatory functions in physiological and pathological processes. Four cytosine modifications (5mC, 5hmC, 5fC and 5caC) are produced and erased by a cyclic enzymatic cascade mediated by DNA methyltransferases (DNMTs), ten-eleven translocation (TET) family enzymes and thymine DNA glycosylase (TDG). Stable maintenance of the DNA methylation profile is important for normal cell homeostasis, but its underlying mechanisms are largely unknown. Methods The expression levels of 7 DNA methylation-related enzymes from normal mouse tissues were assessed using quantitative real-time RT-PCR (qRT-PCR). The gene expression data and related information of human normal tissues and tumor tissues were obtained from the Genotype-Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA), respectively. Results We observed significant positive correlations among the expression levels of DNA methylation-related enzymes in various mice and human normal tissues. By contrast, we found significantly decreased correlations in various tumor tissues compared with their corresponding normal tissues. Furthermore, we also found that alterations in these correlations are associated with several clinicopathological characteristics of cancer patients. Conclusions These observations suggest that uncoordinated expression of DNA methylation-related enzymes is another epigenetic hallmark of cancer. Our work provides important insights into an additional regulatory layer of the DNA methylation maintenance machinery