Search Results (1,514)

Chronic Kidney Disease (CKD) and Colorectal Cancer (CRC) share a profound epidemiological link, supported by Mendelian randomization studies suggesting causality. This review articulates a refined Gut–Kidney Axis, focusing on the pathophysiology of indole-derived uremic toxins. CKD-induced dysbiosis drives hepatic synthesis and systemic accumulation of indoxyl sulfate, which is proposed to promote carcinogenesis via Aryl Hydrocarbon Receptor (AhR) and Akt signaling, ultimately upregulating c-Myc and EGFR. We propose a two-compartment model: while systemic indoxyl sulfate reflects the total gut indole pool (mainly from planktonic bacteria), adherent bacteria like Fusobacterium nucleatum may create high-concentration indole hotspots within the tumor microenvironment. Clinically, we advocate for protein-independent DNA methylation biomarkers (SEPT9, SDC2) to avoid renal confounding. Furthermore, we propose a novel diagnostic panel integrating serum indoxyl sulfate (systemic load) and urinary indoxyl sulfate (gut production) to guide therapy. Therapeutically, targeting upstream drivers (AhR/Akt) may bypass resistance to anti-EGFR therapies in KRAS-mutated tumors. We also discuss the repurposing of the oral adsorbent AST-120 and emerging bacteriophage therapies as strategies to disrupt this oncogenic axis. This review offers a comprehensive framework for stratified management of CKD-associated CRC. Full article

Distributed drive electric vehicles (DDEVs) offer remarkable advantages in handling stability owing to the independent torque and steering control of each wheel. Traditional in-dependent strategies have the disadvantages of slow response speed and unsmooth control interval switching. To overcome the performance tradeoffs of traditional independent strategies, this study proposes an integrated control approach combining four-wheel steering (4WS) and direct yaw moment control (DYC) to achieve coordinated multiobjective optimization. Based on phase-plane theory, the vehicle’s stable domain is divided using a double line method, and speed-dependent control regions and weights are designed to enable smooth switching between control modes. Simulation results demonstrate that, in high-adhesion conditions, compared with the DYC-only strategy, the integrated system reduces the maximum sideslip angle by about 77.8% and the cost function peak by 22.4%. Moreover, it decreases the maximum rear-wheel steering angle by 38.4% and maximum sideslip angle by about 15.4% compared with 4WS-only strategy. Under low-adhesion conditions, compared with the DYC-only strategy, the integrated system reduces the maximum sideslip angle by about 21.1% and the cost function peak by 37.6%. Additionally, the integrated system decreases the maximum rear-wheel steering angle by 60.2% and maximum sideslip angle by about 64.3% compared with 4WS-only strategy. Full article

Amid the growing global environmental challenges, precise and efficient vehicle emission management plays a critical role in achieving energy-saving and emission reduction goals. At the same time, the rapid development of connected vehicles and autonomous driving technologies has generated a large amount of high-dimensional vehicle operation data. This not only provides a rich data foundation for refined emission accounting but also raises higher demands for the construction of accounting models. Therefore, this study aims to develop an accurate and efficient emission accounting model to contribute to the precise nitrogen oxide (NO_x) emission accounting for hybrid electric vehicles (HEVs). A systematic approach is proposed that combines incremental dimensionality reduction with advanced regression algorithms to achieve refined and efficient emission accounting based on multiple variables. Specifically, the dimensionality of the real driving emission (RDE) data is first reduced using the feature selection and t-distributed stochastic neighbor embedding (t-SNE) feature extraction method to capture key parameter information and reduce subsequent computational complexity. Next, an incremental dimensionality reduction method based on dictionary learning is employed to efficiently embed new data into a low-dimensional space through straightforward matrix operations. Given the computational cost of the dictionary learning training process, this study introduces the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) for accelerated iterative optimization and enhances the computational efficiency through parameter optimization, while maintaining the accuracy of dictionary learning. Subsequently, an NO_x emission factor correction factor prediction model is trained using the low-dimensional data obtained from t-SNE embeddings, enabling direct computation of the corresponding correction factor when presented with new incremental low-dimensional embeddings. Finally, validation on independent HEV datasets shows that parameter K improves to 1 ± 0.05 and R² increases up to 0.990, laying a foundation for constructing an emission accounting model with broad applicability based on multiple variables. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection results in dysregulated inflammatory and coagulation pathways that drive immunothrombosis and contribute to adverse clinical outcomes. While individual cytokines and endothelial biomarkers have been associated with disease severity and mortality, the prognostic relevance of combined inflammatory and endothelial signatures remains incompletely characterised. To identify inflammatory cytokines and markers of endothelial activation associated with mortality in patients with severe COVID-19 requiring supplemental oxygen. This retrospective observational study included 73 consecutive adults admitted to a dedicated supplemental oxygen unit with severe COVID-19. Plasma concentrations of IL-1α, IL-1β, IL-6, IL-8, IL-10, TNF-α, von Willebrand factor (VWF) antigen and propeptide, ADAMTS13 antigen and activity, and ADAMTS13 autoantibodies were measured on admission using ELISA-based assays. Associations with mortality were assessed using non-parametric analyses, age-adjusted logistic regression, multivariable logistic regression, and receiver operating characteristic (ROC) curve analysis. Increasing age was independently associated with mortality. After adjustment for age, higher IL-1α concentrations were associated with increased odds of death, whereas a higher IL-6/IL-10 ratio was independently protective. In multivariable models, including non-ratio variables, ADAMTS13 autoantibody levels remained independently associated with mortality. In ratio-based multivariable analysis, both the ADAMTS13 activity/autoantibody ratio and the IL-6/IL-10 ratio were independently protective, while age was no longer significant. IL-10 and ADAMTS13 autoantibodies demonstrated moderate discriminative performance for mortality prediction (AUC ~0.70). A combined biomarker model incorporating IL-1α, IL-8, IL-10, and ADAMTS13 autoantibodies yielded very high predicted mortality probabilities. Our findings highlight IL-1α and ADAMTS13 autoantibodies as independent predictors of mortality in severe COVID-19, reflecting the interplay between inflammatory and endothelial pathways. Biomarker ratios capturing immune and endothelial balance—particularly the ADAMTS13 activity/autoantibody ratio—may enhance risk stratification and support integrated prognostic models. Full article

This paper concentrates on fractional-order fuzzy cellular neural networks (FOFCNNs) with bounded uncertainties and transmission delays. To better capture real-world dynamic behaviors, the fuzzy AND and OR operators are employed to construct drive-response systems. For the fixed-time synchronization task of the systems, a novel multi-module feedback controller incorporating three functional terms is designed. These terms aim to eliminate delay effects, ensure fixed-time convergence, and reduce parameter conservativeness. Leveraging the properties of fractional-order operators and our multi-module control scheme, new synchronization criteria of the studied drive-response systems can be established within a predefined time. An upper bound on the settling time is derived, depending on the system size and control parameters, but independent of the initial conditions. A significant corollary is derived for the case of no uncertainties under the nonlinear controller. Numerical experiments discuss the impact of uncertainties and delays on synchronization, and confirm the validity of the results presented in this study. Full article

Objectives: Respiratory syncytial virus (RSV) is a leading cause of hospitalization for acute lower respiratory tract infections (ALRIs) in children, with bacterial coinfection complicating diagnosis and often driving antibiotic overuse. This study aimed to develop and validate a clinical prediction model using common laboratory biomarkers to enable early, accurate identification of clinically significant bacterial coinfection in children with RSV infection. Methods: A single-center, retrospective cohort study was conducted at Fujian Children’s Hospital, enrolling 518 hospitalized children with RSV infection, which was confirmed via targeted next-generation sequencing (tNGS). Patients were randomly divided into a training set (n = 363) and a test set (n = 155) at a 7:3 ratio. The primary outcome, bacterial coinfection, was defined by a composite reference standard integrating etiological evidence from tNGS with clinical, inflammatory, and imaging data, and adjudicated by a blinded expert panel. LASSO regression identified independent predictors, followed by multivariable logistic regression modeling. Model performance was assessed via discrimination (AUC), calibration (Hosmer–Lemeshow test), and clinical utility (Decision Curve Analysis) in both sets. Results: Neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and serum amyloid A (SAA) were selected as predictors. The model achieved an AUC of 0.832 (95% CI: 0.788–0.875) in the training set and 0.811 (95% CI: 0.737–0.885) in the test set, with well-calibrated predictions (p > 0.05). Decision curve analysis demonstrated net clinical benefit across 10–80% threshold probabilities. A nomogram was developed for practical application. Conclusions: This study established a model integrating NLR, CRP, and SAA. It offers a reliable tool for the early detection of bacterial coinfection in RSV-infected children, enabling targeted antibiotic stewardship and improving clinical outcomes. Full article

Internal wall grinding of pipes constitutes a critical pretreatment procedure in the anti-corrosion repair operations of Prestressed Concrete Cylinder Pipes (PCCP). To address the limitations of low efficiency and poor safety associated with traditional manual internal wall grinding in PCCP anti-corrosion repair, this study presents the design of a support-wheel-type internal wall grinding robot for pipes. The robot’s structure comprises a walking support module and a grinding module: the walking module employs four sets of circumferentially equally spaced (90° apart) independent-support wheel groups. Through an active–passive collaborative adaptation mechanism regulated by pre-tensioned springs and lead screws, the robot can dynamically conform to the inner wall of the pipe, ensuring stable locomotion. The grinding module is connected to the walking module via a slewing bearing and is equipped with three roller-type steel brushes. During operation, the grinding module revolves around the pipe axis, while the roller brushes rotate simultaneously, generating a composite three-helix grinding trajectory. Mathematical models for the robot’s obstacle negotiation, bend traversal, and grinding motion were established, and multi-body dynamics simulations were conducted using ADAMS for verification. Additionally, a physical prototype was developed to perform basic functional tests. The results demonstrate that the robot’s motion characteristics are highly consistent with theoretical analyses, exhibiting stable and reliable operation, excellent pipe traversability, and robust driving capability, thus meeting the requirements for internal wall grinding of PCCP pipes. Full article

Background: In-hospital cardiac arrest (IHCA) constitutes a high-impact clinical event, associated with substantial mortality, frequent neurological and functional impairment. There is a pressing need for primary IHCA studies that evaluate risk predictors, given the inherent challenges of IHCA data collection, previously unharmonized reporting frameworks, and the predominant focus of prior investigations on other domains. Among potential contributors, the “off-hours effect” has consistently been linked to poorer IHCA outcomes. Accordingly, we sought to examine whether in-hospital mortality after IHCA varies according to the time and day of occurrence within a tertiary academic center in Northern Greece. Methods: We conducted a single-center observational cohort study using a prospectively maintained in-hospital resuscitation registry at AHEPA University General Hospital, Thessaloniki. All adults with an index IHCA between 2017 and 2019 were included, and definitions followed Utstein-style recommendations. Results: Multivariable logistic regression adjusted for organizational, patient, and process-of-care factors demonstrated that afternoon/night arrests, weekend arrests, heart failure comorbidity, and need for mechanical ventilation were independent predictors of higher in-hospital mortality. Conversely, arrhythmia as the cause of IHCA and arrests occurring in the intensive care unit or operating room were associated with improved survival. Subgroup analyses confirmed consistent off-hours differences, with weekend events showing reduced 30-day and 6-month survival and worse functional status at discharge. Afternoon/night arrests were more frequent, characterized by longer response intervals and lower survival at both time points. Conclusions: Organizational factors during nights and weekends, rather than patient case mix, drive poorer IHCA outcomes, underscoring the need for targeted system-level improvements. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is initiated by the viral spike proteins, which are key structural components that mediate host cell binding and entry and alter downstream signaling through multiple interactions with endothelial surface receptors. Endothelial dysfunction is a central consequence of COVID-19, contributing to vascular inflammation, barrier disruption, thrombosis, and multi-organ injury affecting the pulmonary, cardiovascular, cerebral, and renal systems. Emerging evidence demonstrates that spike protein-mediated effects, independent of productive viral infection, disrupt endothelial homeostasis through angiotensin-converting enzyme 2 (ACE2) dysregulation, integrin engagement, altered calcium signaling, junctional protein remodeling, oxidative stress, and pro-inflammatory and pro-apoptotic pathways. This review is intentionally focused on spike (S) protein-driven mechanisms of endothelial dysfunction; pathogenic vascular effects attributed to other SARS-CoV-2 structural proteins, including the nucleocapsid (N) protein, are beyond the scope of this discussion. In this review, we synthesize current experimental and translational data detailing the molecular mechanisms by which the SARS-CoV-2 spike protein drives endothelial dysfunction across multiple organ systems and discuss potential therapeutic strategies aimed at preserving endothelial integrity in acute COVID-19 and its long-term vascular sequela. Full article

The increasing heterogeneity of wheeled mobile robot (WMR) architectures, including differential-drive, Ackermann, omnidirectional, and reconfigurable platforms, poses a major challenge for defining a unified, scalable kinematic representation. Most existing formulations are tailored to specific mechanical layouts, limiting analytical coherence, cross-platform interoperability, and the systematic reuse of modeling, odometry, and motion-related algorithms. This work introduces a generalized kinematic modeling framework that provides a mathematically consistent formulation applicable to arbitrary WMR configurations. Wheel–ground velocity relationships and non-holonomic constraints are expressed through a concise vector formulation that maps wheel motions to chassis velocities, ensuring consistency with established models while remaining independent of the underlying mechanical structure. A parameterized wheel descriptor encodes all relevant geometric and kinematic properties, enabling the modular assembly of complete robot models by aggregating wheel-level relations. The framework is evaluated through numerical simulations on four structurally distinct platforms: differential-drive, Ackermann, three-wheel omnidirectional (3,0), and 4WD. Results show that the proposed formulation accurately reproduces the expected kinematic behavior across these fundamentally different architectures and provides a coherent and consistent representation of their motion. The unified representation further provides a common kinematic backbone that is directly compatible with odometry, motion-control, and simulation pipelines, facilitating the systematic retargeting of algorithms across heterogeneous robot platforms without architecture-specific reformulation. Additional simulation studies under realistic physics-based conditions show that the proposed formulation preserves coherent kinematic behavior during complex trajectory execution and supports the explicit incorporation of geometric imperfections, such as wheel mounting misalignments, when such parameters are available. By consolidating traditionally separate derivations into a single coherent formulation, this work establishes a rigorous, scalable, and architecture-agnostic foundation for unified kinematic modeling of wheeled mobile robots, with particular relevance for modular, reconfigurable, and cross-architecture robotic systems. Full article

The gut immune microenvironment and the liver engage in intricate information exchange via the gut–liver axis. The disruption of these interactions plays a pivotal role in the formation and exacerbation of pathological damage to the liver. The gut immune microenvironment is not an independent layer of the gut barrier; rather, it permeates and regulates all other barrier functions, serving as the core coordinator. Disruption of the immune microenvironment in the gut–liver axis drives progression across the full disease spectrum—from steatosis to hepatitis, fibrosis, and even liver cancer—through the continuous influx of immune-stimulatory signals that overwhelm the liver’s intrinsic immune regulatory mechanisms. Dysfunction of innate immunity components, amplification of inflammatory factors and key cellular signaling pathways, activation of adaptive immune T cells, and systemic effects mediated by liver-derived inflammatory factors collectively form a disordered immune microenvironment. This damages the intestinal barrier and exacerbates liver disease via the gut–liver axis, leading to further intestinal injury, thus establishing a self-reinforcing vicious cycle. Current therapeutic strategies based on modulating the gut–liver axis microenvironment remain limited, yet studies have demonstrated that suppressing gut immune cells, cytokines, and signaling pathways can help delay liver disease progression. Hopefully, future combined, precise, and cutting-edge gut immunotherapies will provide more effective strategies for liver disease treatment. Full article

This paper presents a stochastic regression model for predicting the coefficient of friction (COF) in automotive dry clutches with organic linings. The influence of temperature, normal load, and slip speed on COF behaviour is investigated based on a large set of clutch wear-characterization data, collected using a custom-designed disc-on-disc tribometer that replicates realistic clutch-engagement cycles. The proposed model calculates both the expected value and standard deviation of the COF. The COF expectation model takes temperature, normal load, and slip speed as inputs, and it has a cubic polynomial form selected through a feature-selection method. The COF standard deviation model is fed by the same three inputs or alternatively the COF expectation input, and it is parameterized using the maximum likelihood method. The overall model is validated on an independent characterization dataset and an additional dataset gained through separate experiments designed to mimic real driving conditions. Full article

This study investigated the influence of awareness, knowledge, and risk perceptions on environmental attitudes and behaviours in Türkiye, specifically in the context of climate change, using structural equation modelling (SEM). Data were collected from all 81 provinces covering the seven geographical regions of the country. The results revealed that awareness and risk perception have the strongest direct impact on pro-environmental behaviour. Environmental attitudes also demonstrated a significant positive effect, though the findings suggest that high awareness and risk perception can directly drive action even independently of attitude. Uniquely, this study fills a critical gap in the developing country literature by demonstrating that in Türkiye, perceiving the risk translates directly into action, contrasting with the ‘value-action gap’ often observed in Western contexts. Practically, the findings suggest that policymakers should prioritize risk-communication strategies and disaster-preparedness drills over passive information campaigns to effectively stimulate pro-environmental behaviours. Full article

This review addresses the pivotal challenge in distributed-drive electric vehicle (DDEV) dynamics control: how to optimally distribute braking and steering forces during combined maneuvers to simultaneously enhance lateral stability, safety, and energy efficiency. The over-actuated nature of DDEVs presents a unique opportunity for precise torque vectoring but also introduces complex coupled dynamics, making vehicles prone to instability such as rollover during aggressive steering–braking scenarios. Moving beyond a simple catalog of methods, this work provides a structured synthesis and evolutionary analysis of chassis control methodologies. The problem is first deconstructed into two core control objectives: lateral stability and longitudinal braking performance. This is followed by a critical analysis of how integrated control architectures resolve the inherent conflicts between them. The analysis reveals a clear trajectory from independent control loops to intelligent, context-aware coordination. It further identifies a paradigm shift from the conventional goal of merely maintaining stability toward proactively managing stability boundaries to enhance system resilience. Furthermore, this review highlights the growing integration with high-level motion planning in automated driving. By synthesizing the current knowledge and mapping future directions toward deeply integrated, intelligent control systems, it serves as both a reference for researchers and a design guide for engineers aiming to unlock the full potential of the distributed drive paradigm. Full article

Milling force is a parameter affecting wood processing quality, tool life, and energy consumption, and its variation is influenced by the multi-factor coupling of cutting parameters and tool geometric factors. This study systematically investigates milling forces during the processing of pine wood (Pinus sylvestris var. mongholica Litv.) using a hybrid modeling approach combining principal component analysis (PCA) and multiple linear regression (MLR). Firstly, PCA was employed to reduce the dimensionality of the tool rake angle (γ), helix angle (λ), cutting depth (h), feed per tooth (U_z), and triaxial milling forces (F_x, F_y, F_z); this eliminated the multicollinearity among variables and extracted the integrated features. Subsequently, an MLR model was constructed using the principal components as independent variables to quantitatively evaluate the contribution of each factor to milling forces. The results support the conclusion that PCA successfully extracted the first four principal components (cumulative variance contribution rate: 92.78%), with PC1 (49.16%) characterizing the comprehensive milling force effect and PC2 (15.03%) primarily reflecting the characteristics of the tool geometric parameters. The established MLR model demonstrated a high significance (R²: F_x = 0.915, F_y = 0.907, F_z = 0.852). The cutting depth exerted a significant positive driving effect on the triaxial milling forces via PC1 (each 1 mm increase in depth increased the PC1 score by 0.64 units, resulting in increases of 27.2%, 26.6%, and 21.8% for F_x, F_y, and F_z, respectively). The helix angle significantly suppressed F_y through PC2 (β = −0.090, p < 0.001), whereas the rake angle exhibited a weak negative effect on F_x via PC3 (β = −0.015). Parameter optimization identified the combination γ = 25°, λ = 30°, h = 0.5 mm, and U_z = 0.1 mm∙z⁻¹ as optimal, which reduced the triaxial milling forces by 62.3% compared to the experimental maximum. This study provides a theoretical foundation and novel parameter optimization strategy for the efficient, low-damage processing of wood materials. Full article