Search Results (2,301)

Metabolic dysfunction-associated steatohepatitis (MASH) is a progressive liver disease characterized by lipid metabolism dysregulation, insulin resistance, inflammation, and potential progression to severe liver conditions. Xiaochaihutang (XCHT), a traditional Chinese medicine, has been explored for its therapeutic potential. This study aimed to systematically investigate the role and mechanisms of XCHT in the treatment of MASH. Network pharmacology and ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) were used to identify active components and pathways. In vitro models using free fatty acids (FFAs) and an in vivo methionine-choline-deficient (MCD) diet-induced MASH mouse model were employed to evaluate hepatoprotective effects, metabolic improvements, and gut microbiota modulation. XCHT reduced FFA-induced injury, decreased aspartate aminotransferase (AST)/alanine aminotransferase (ALT) levels, inhibited lipid accumulation (72% reduction in triglyceride, TG), and alleviated oxidative stress (67% decrease in oxygen species, ROS; 92% restoration of glutathione, GSH). It also modulated gut microbiota, increasing beneficial bacteria, and activated the AMP-activated protein kinase (AMPK)/acetyl-CoA carboxylase (ACC) pathway (2.2-fold increase in phosphorylated AMPK, p-AMPK). Quercetin, kaempferol, β-sitosterol, and baicalin were predicted as key active components. XCHT ameliorates MASH through multi-target regulation of the AMPK pathway and gut–liver axis, supporting its potential as a complementary treatment for metabolic liver diseases. Full article

As a core component enabling multi-axis precision machining in CNC machine tools, the CNC rotary table’s precision state directly affects machining quality and production efficiency. Real-time and accurate prediction of its precision remaining useful life (PRUL) is of great significance for optimizing maintenance costs, ensuring production safety, and extending equipment service life. To this end, a digital twin framework for PRUL prediction of CNC rotary tables was first established, defining a five-dimensional digital twin model. Then, based on the Meta-action theory, an initial precision model of the CNC rotary table was constructed. Time-varying errors were quantitatively characterized using thermal error simulation and the wear model, and combined with the Wiener process model, theoretical PRUL prediction within the virtual mirror was realized. Finally, the virtual mirror data and physical entity data were fused to generate corrected model parameters, yielding a real-time PRUL prediction model for the CNC rotary table driven by digital twin data. The case study results demonstrate that the proposed method, which integrates the mechanistic model with real-time data, achieves an improvement of at least 16% in both prediction accuracy and stability compared to methods based solely on either virtual mirror data or monitoring data. Full article

This study evaluated dose-dependent effects of dietary N-carbamylglutamate (NCG) on muscle growth, nutrient composition, and metabolic profiles in Danzhou chickens. A total of 480 one-day-old female Danzhou chicks were randomly assigned to diets supplemented with 0, 400, 800, or 1200 mg/kg NCG for 35 days and slaughtered at 35 days of age. NCG at 400–800 mg/kg improved growth performance, increased crude protein and free amino acids (arginine, methionine), and altered fatty acid composition in a muscle-specific manner, with increased breast-muscle MUFA and decreased leg-muscle arachidonic acid and DHA; total PUFA did not differ. Histological analysis showed that NCG increased leg-muscle fiber cross-sectional area and Feret diameter, accompanied by a reduction in fiber density. Mechanistically, NCG altered the mRNA expression of IRS2, IGF1, MyoG, AKT1, and FOXO1 in a manner consistent with modulation of the IGF-1/IRS2/PI3K/Akt/FoxO1 axis. Metabolomics suggested an association with purine metabolism; however, this finding is exploratory and does not establish causality. Integrated multi-omics revealed crosstalk between purine and lipid metabolism. Among the doses tested, 800 mg/kg produced the largest response for several endpoints; however, no formal dose-optimization model was fitted, and the optimal dose remains to be determined. The 1200 mg/kg dose did not further improve most endpoints, indicating a nonlinear dose response. This study provides the first multi-omics evidence demonstrates that NCG modulates muscle growth, nutrient composition, and metabolic profiles in indigenous chickens through coordinated regulation of anabolic signaling, purine metabolism, and lipid remodeling. Direct meat-quality measurements were not performed; therefore, effects on eating quality, technological quality, oxidative stability, and consumer preference remain to be established. Full article

Age-related macular degeneration (AMD) is a leading cause of visual impairment. Beyond the classical pathogenetic pathways, increasing evidence suggests that epigenetic regulation and the gut–retina axis may substantially contribute to AMD development and progression. This review synthesizes current evidence on two increasingly recognized and interconnected domains of AMD pathogenesis: epigenetic regulation and gut microbiota dysbiosis. Epigenetic processes, such as DNA methylation, histone modifications, and non-coding RNAs, can influence gene function involved in oxidative stress responses, complement regulation, angiogenesis, and inflammation, while several related markers have emerged as potential diagnostic and prognostic biomarkers. Moreover, growing evidence suggests a functional gut–retina axis, in which alterations in the gut microbiota impair intestinal barrier function and facilitate the systemic passage of bacteria and microbial metabolites, potentially affecting retinal inflammation and immune responses. Furthermore, several microbial metabolites, including short-chain fatty acids, lipopolysaccharide, bile acids, tryptophan derivatives, and trimethylamine-N-oxide, may have either protective or harmful effects on retinal and choroidal homeostasis, with altered circulating and fecal concentrations reported in patients with AMD compared with controls. These findings support AMD as a systemically and molecularly interconnected disorder, while further longitudinal, mechanistic, and multi-omics studies are required to establish causality, define interactions between epigenetic and microbiome-related pathways, and clarify their potential utility as biomarkers and therapeutic targets. Full article

Background/Objectives: Understanding the biological role of the receptor activator of nuclear factor-κB ligand (RANK), its ligand (RANKL), and the decoy receptor osteoprotegerin (OPG) is vital for clarifying the pathogenesis of gnathic giant cell-containing lesions and directing targeted clinical treatments. Methods: Following PRISMA 2020 guidelines and a registered PROSPERO protocol [CRD420261461026], we searched PubMed, Scopus, and Web of Science databases for human tissue-level or clinical denosumab studies (2003–2026). Quality was appraised using adapted NOS and JBI checklists. A fixed-effect inverse-variance meta-analysis pooled continuous RANK parameters between aggressive and non-aggressive CGCG subtypes. Results: Eleven primary tissue-marker studies (259 specimens; two previously considered studies were excluded as unpublished, non peer-reviewed sources) and 10 non-overlapping clinical cohorts/case reports (58 patients total: 55 from 7 multi-patient cohorts + 3 single-case reports [Hameed, O’Connell, Kawamura]). Mononuclear stromal cells represent the pathogenetic driver, secreting RANKL to recruit RANK-expressing mature giant cells. Meta-analysis (k = 2 studies) showed that raw membrane RANK-positive cell percentages did not differ significantly between aggressive and non-aggressive phenotypes (pooled Hedges’ g = 0.25, 95% CI −0.29–0.80, p = 0.36), while non-aggressive lesions showed a borderline, non-significant trend toward higher staining intensity-distribution (SID) scores (g = 0.55, 95% CI −0.00–1.11, p = 0.0506). Because this 95% CI crosses zero, and only two studies (k = 2) were pooled, this represents a non-significant trend rather than a statistically confirmed difference between aggressive and non-aggressive CGCG; both pooled estimates are, therefore, hypothesis-generating only. In clinical cohorts, denosumab achieved objective clinical and radiological tumor responses in all reported cases, enabling downstaging from disfiguring resection to conservative curettage. However, post-cessation recurrence was frequent, ranging from 37% in adults to 100% in pediatric cohorts, with pediatric patients at risk of rebound hypercalcemia. Conclusions: The RANK/RANKL/OPG axis, driven by RANKL-secreting mononuclear stromal cells, underlies osteolytic activity in gnathic giant cell lesions. Denosumab is an effective neoadjuvant bridge to jaw-preserving surgery but is not curative; a RANKL-independent, OPG-resistant SOFAT pathway has been hypothesized as one possible contributor to treatment failure, although this proposal is derived from a single external study and requires independent validation before any clinical inference is drawn. Close post-treatment metabolic and radiographic surveillance is warranted in pediatric patients. Prospective studies are needed to define optimal weaning protocols. Full article

Multiaxial fatigue damage remains a critical barrier to the reliable design of metallic engineering components, particularly under complex non-proportional loading paths. Conventional critical-plane and energy-based models rely heavily on empirical assumptions and struggle to capture intricate path-dependent damage evolution, leading to limited accuracy and generalizability. To overcome these challenges, we propose a novel data-driven framework that integrates heterogeneous information sources through a three-branch parallel neural architecture. Specifically, a deep neural network (DNN) extracts static material intrinsic properties; a bidirectional gated recurrent unit (BiGRU) enhanced with a multi-head self-attention mechanism captures long-term temporal dependencies and adaptively weights key stages in loading histories; and a one-dimensional convolutional neural network (1D-CNN) extracts local strain fluctuation patterns. The three streams are fused to produce a comprehensive damage representation for end-to-end life prediction. The output of the model is the base −10 logarithm of fatigue life, i.e., $lg\left(Nf\right)$. Evaluated on a public multiaxial fatigue dataset comprising 40 metals and 1167 samples, the proposed model achieves a coefficient of determination ($R^2$) of 0.9052, achieving promising performance on a fixed data split ($R^2$ = 0.9052); ablation studies demonstrate the complementary nature of the three feature extraction branches, though 5-fold cross-validation suggests that the performance gain requires further validation with larger datasets. Systematic ablation studies confirm that the multi-source fusion strategy and the attention mechanism are pivotal to the performance gain. This work offers a robust and effective data-driven solution for high-accuracy fatigue life assessment, and points toward future integration of physical knowledge to further enhance extrapolation and transparency. Full article

Weakly textured closed pipelines—including energy and buried drainage conduits—require timely inspection of cracks, corrosion, joints and related defects. Weak illumination, sparse texture and repetitive cylindrical geometry degrade visual odometry and RGB-D SLAM, especially along the pipe axis, while image-level detectors rarely provide the axial distance, circumferential angle and pipe-frame 3D coordinates needed for maintenance. This paper presents the Structure-Constrained Pipe Joint Estimator (SC-PipeJE), an RGB-D framework that jointly estimates robot poses and 3D damage locations under weak texture. SC-PipeJE improves YOLOv8-seg for structural landmarks and damage masks, fits cylinders and centerlines from local point clouds, and optimizes a sliding window that couples RGB-D odometry with continuous geometric residuals, discrete landmarks and multi-frame damage factors so that damage observations also refine pose. On a primary RGB-D corpus of 5468 annotated frames and multi-structure sequences, the detector reaches mAP@0.5:0.95 of 0.7194 and Mask AP of 0.6980. Absolute trajectory error is 0.0343 m (67.6% lower than RTAB-Map under the same RGB-D protocol), and multi-frame damage localization yields 32.88 mm mean 3D error (46.6% lower than single-frame back-projection) at about 20 FPS. Quantitative pose and 3D damage results are reported on the laboratory RGB-D Corpus A; a complementary CCTV subset (Corpus B) is used only for qualitative appearance stress checks and is not mixed into the quantitative protocol. Ablation and robustness studies show that continuous geometry, discrete landmarks and multi-frame damage factors provide complementary observability when appearance cues are unreliable. Full article

Climate non-stationarity and intensifying extreme rainfall challenge flood-risk assessment in cold-region old industrial cities, where renewal must work within an established built fabric. Conventional assessments apply fixed indicator weights and thresholds derived from a single design event, although the factors controlling inundation and their response positions can shift with rainfall intensity. This study examines 208 urban green-space plots within Shenyang’s Third Ring Road, combining plot-level field sampling, GIS-based spatial indicators, and maximum inundation depths simulated for 1-, 3-, 10-, 30-, and 50-year return periods. Four analyses were applied along a common scenario axis: indicator screening defined the factor set; multi-group structural equation modeling characterized shifts in latent-variable paths; random forests with permutation importance ranked observed factors; partial dependence analysis located response breakpoints; and XGBoost-SHAP described sample-level contributions. All predictive results are reported from 30 repeated train–test splits with uncertainty intervals. Screening reduced 59 candidate indicators to 13 key factors. The diagnostic evidence indicates a mechanism-shift interval between the 10- and 30-year return periods: the structural path from spatial-pattern resistance to inundation becomes significant from the 10-year scenario, the total effect of surface-cover pressure first exceeds that of vertical storage capacity in the same scenario group, and the response breakpoint of impervious surface ratio tightens from about 58% to about 33% within this interval. Predictive importance is dominated by grey infrastructure density, green infrastructure density, and catchment radius in all scenarios. Renewal priorities therefore vary with rainfall intensity: internal storage and infiltration under regular rainfall, joint surface-cover and catchment control within the shift interval, and coordinated grey–green system responses under extreme scenarios. These findings are diagnostic and predictive, and retrofit benefits require experimental or quasi-experimental validation. The framework offers a methodological reference for further local calibration in comparable cold-region old industrial cities. Full article

Background/Objectives: Medication-taking behaviour during pregnancy may be associated with treatment-related beliefs, emotional concerns, information sources, and clinician–patient communication. We examined these cross-sectional associations and the preliminary measurement properties of the Pregnancy Medication Adherence Model (PMAM). Methods: This cross-sectional study included 355 pregnant women with prescribed treatment recruited from nine general practices in Dolj County, Romania. Q25 was analysed on its five-category scale and using an operational threshold of ≥4. Internal consistency, principal component analysis, an ordinal exploratory factor analysis based on polychoric correlations, regression modelling, and review-stage sensitivity analyses addressing criterion overlap were performed. Results: Overall, 255 participants (71.8%) scored ≥4 on Q25. The four multi-item domains had Cronbach’s α values of 0.897–0.935. Parallel analysis and principal-axis factoring with oblique rotation supported four factors; salient loadings were 0.797–0.925 and no secondary loading reached 0.30. In the original adjusted binary model, cognitive understanding, emotional reassurance, clinician–patient communication, and lower endorsement of information-seeking and social-influence items were associated with Q25 ≥ 4. After Q23 was removed from the communication score and Q19 and Q24 were omitted, these four associations persisted (adjusted ORs 1.90–2.55). Conclusions: The findings identify concurrent associations between medication-related perceptions and a single self-reported treatment-behaviour item. PMAM remains an exploratory framework; external psychometric evaluation and prospective assessment against defined medication-specific adherence and clinical outcomes are required before clinical use. Full article

Physics-Informed Machine Learning has emerged as a powerful paradigm for fatigue and fracture analysis, combining the data-driven flexibility of neural networks with the consistency of physical laws. This scoping review maps the extent, range, and nature of research on PIML applied to fatigue life prediction, crack growth and propagation, multiaxial fatigue, damage mechanics, prognostics and health management, and structural health monitoring, covering the literature published from 2018 up to August 2026. A comprehensive search strategy integrated the core terminology of the field with broader synonym terms, complemented by forward and backward citation searching and manual screening of the most productive venues. The combined strategy retrieved 124 records, of which 76 primary research articles satisfied the inclusion criteria and were charted and characterized in this review. The included studies are characterized through the lens of a three-bias taxonomy—observational, learning, and inductive—which organizes how physical knowledge enters the learning pipeline: through data-level constraints, loss-function modifications, and structural architectural changes, respectively. The review maps the architectural landscape of the field, from standard multi-layer perceptron-based PINNs to specialized variants including sequential attention models, physics-informed Kolmogorov–Arnold networks, geometry-aware finite encodings, graph neural networks, and probabilistic Bayesian formulations. The mapped literature indicates that PIML methods are reported to improve generalization from sparse and noisy experimental data, achieve computational gains over conventional simulation, and quantify prediction uncertainty. Persistent challenges nonetheless emerge across the mapped literature, including high training costs, sensitivity to hyperparameter and loss-weighting choices, limited transferability across problem configurations, and the black-box nature of deep models. The review identifies knowledge gaps and concludes with an agenda for future research, emphasizing generalizable and adaptive architectures, multi-scale and multi-physics formulations, and standardized benchmarks. Full article

Automated feeding in cage-based rabbit production is challenging because cage geometry and limited workspace require a compact manipulator with sufficient reach, low mass, and reliable task-level positioning. This study developed a lightweight five-degree-of-freedom manipulator integrated with a vision-guided positioning framework for cage-based rabbit feeding applications. The prototype achieved an effective task reach of 1.20 m with a total mass of 12.37 kg. The design incorporated lightweight carbon-fiber-reinforced polymer (CFRP) long-span links, integrated actuators selected according to joint torque requirements, task-constrained kinematics, closed-form inverse kinematics, and Cartesian trajectory planning. A red–green–blue and depth (RGB-D) localization interface used four ordered feed-port keypoints and a horizontal-plane constraint to provide three-dimensional upper-tier task targets for the manipulator. All representative task points were numerically reachable under the defined software joint limits, and the maximum planned joint velocity was 0.525 rad s${}^{-1}$. Component-level linear-static finite-element analysis predicted no yielding in the four analyzed aluminum components under the simplified conservative 5 kg verification load case. Static loading of the assembled arm produced mean downward displacements of 3.0–20.0 mm at the J5 flange under 1–5 kg loads. Laboratory evaluation demonstrated three-dimensional repeatability radii of 1.68–4.63 mm. Across four upper-tier locations, the mean Euclidean feed-port localization error ranged from 9.01 to 20.47 mm, with all 80 trials satisfying the $\pm 20$ mm-per-axis criterion. The mean tool center point (TCP)-equivalent end-point error ranged from 18.23 to 37.15 mm. These laboratory results support the feasibility of the developed manipulator for upper-tier vision-guided positioning in rabbit-feeding geometry, while indicating that multi-point calibration and spatial compensation are needed to further reduce system-level positioning error. Validation was limited to laboratory testing; no feed was discharged and no rabbit-house operation was evaluated. Full article

The long-term reliability of selective laser melting (SLM) equipment is constrained by the latent degradation and coupled faults of multiple underlying hardware systems under continuous high-load operations. Transitioning from passive defect detection to proactive prognostics and health management (PHM) is crucial to overcome bottlenecks in industrial applications. Accurately inferring future multi-system hardware states faces three major challenges: feature alignment difficulties under variable-length printing cycles, graph topology collapse under strong industrial noise, and dimensional conflicts along with error cascading divergence during the joint optimization of microscopic physical trajectories (continuous regression) and macroscopic system anomalies (discrete classification) in end-to-end prediction. To address these issues, this paper proposes a decoupled spatiotemporal graph autoregressive network (DS-GAN). First, a multi-scale feature pooling and degradation gating injection mechanism is constructed to align highly variable-length high-frequency sequences and adaptively integrate macroscopic health priors. This approach achieves feature decoupling under physical boundary constraints. Second, a restricted residual graph evolution mechanism is introduced to regulate dynamic coupling drift based on static physical topologies, effectively suppressing feature divergence in the spatial dimension. Finally, a heterogeneous multi-task autoregressive decoder based on homoscedastic uncertainty is designed; this decoder helps reduce the impact of accumulated errors along the time axis during multi-step forecasting. Long-sequence forward inference on a real SLM continuous printing dataset demonstrates that DS-GAN achieves an overall accuracy of 97.48% for multi-system anomalies while strictly limiting the global false alarm rate to 1.85%. Furthermore, quantitative results reveal the physical inertia mechanism within the prediction horizon, demonstrating that the model maintains high fidelity for physical trajectories with a Macro-RMSE of 0.062, even under a maximum predictive horizon of 20 s. This study provides a reliable theoretical and engineering framework for dynamic coupling correlation analysis and proactive fault warning of multi-systems in complex industrial equipment. Full article

Background: Hemolytic jaundice, characterized by excessive bilirubin production and hepatic dysfunction, currently lacks effective therapeutic strategies targeting its underlying mechanisms. Methods: This study evaluated the therapeutic potential of the aqueous extract of Inonotus obliquus (IO) in ameliorating hemolytic jaundice through modulation of the gut–liver axis. A murine model of hemolytic jaundice was induced by phenylhydrazine (PHZ) administration, and IO was orally administered as the intervention. Antibiotic depletion and FMT verified gut microbiota dependence. Liver injury, serum bilirubin, gut microbiome, and metabolites were assessed. Results: IO treatment significantly alleviated PHZ-induced hemolytic jaundice in mice. Notably, antibiotic-mediated depletion of the gut microbiota abolished the hepatoprotective effects of IO, whereas fecal microbiota transplantation (FMT) from IO-treated donors conferred marked amelioration of hyperbilirubinemia in recipient mice. Integrative multi-omics analysis identified Limosilactobacillus reuteri and Lactobacillus johnsonii as the predominant microbial species altered by IO. Furthermore, IO upregulated the expression of farnesoid X receptor (FXR) and pregnane X receptor (PXR) in both hepatic and intestinal tissues and also increased the expression of bile salt export pump (BSEP) and multidrug resistance-associated protein 2 (MRP2). These alterations were correlated with an increased excretion of bile acids and bilirubin into the intestinal lumen, thereby mitigating hepatic injury. This process may be associated with the modulation of gut microbiota. Conclusions: These findings collectively provide evidence of a significant correlation between IO treatment and the mitigation of hemolytic jaundice, achieved by modulating the enterohepatic circulation of bile acids and bilirubin through the regulation of gut microbiota. Our results highlight the potential of IO as a microbiota-directed nutritional strategy for the management of jaundice. Full article

This paper presents ROTOTILLER (Rolling Optimization of Tasks and Online Trajectories Integrating Local Lookahead Extended Routing), an extension of the extended-SPADES framework for integrated task allocation and fleet navigation in dynamic logistics environments. The approach preserves the coupling between multi-robot task assignment and Covariant Hamiltonian Optimization for Motion Planning CHOMP-based motion planning while introducing rolling-CHOMP, a fleet-level rolling-window optimizer that incrementally updates trajectories in response to new tasks, map changes, and execution disturbances. For task allocation, ROTOTILLER estimates robot–station and station–station travel costs as weighted shortest paths on a Breadth-First Search Medial-Axis Skeleton (BFS-MAS) topological graph extracted from a SLAM-derived occupancy map. In a hospital-like benchmark, this topological cost space reduced average path-computation time from 298.3 ms to 2.08 ms per query, a 143× speedup, while increasing the estimated path length by approximately 21%. In Gazebo simulations with fleets of up to six differential-drive robots, rolling-CHOMP reduced average task-completion time relative to batch multi-CHOMP across the evaluated task-distribution and dynamic-obstacle scenarios by an average of $33.7\%$ for six-robot fleets, while maintaining the measured inter-robot clearance. The system is implemented in ROS 2 and released as an open-source package with reproducible benchmarks. Full article

Ultrasonic guided waves acquired using permanently bonded piezoelectric transducers enable large-area monitoring of composite structures. For practical structural health monitoring; however, both the location and the condition of a structural change are critical. This paper presents a data-driven multi-task framework for a carbon-fiber-reinforced polymer plate using a circular PZT array. To generate a large, precise, and repeatable experimental dataset without permanently damaging the specimen, paired magnets were placed on opposite surfaces of the plate and moved using a two-axis positioning system over a uniform grid. Four magnet-pair configurations were used to form four balanced experimental classes. For each case, ultrasonic guided-wave signals from all actuator–sensor paths were concatenated and processed by a shared convolutional neural network and long short-term memory architecture. The model simultaneously estimates the spatial coordinates through two regression branches and identifies the magnet-pair perturbation configuration through a classification branch. An automated hyperparameter search was used to select the principal architectural and training parameters. Performance was evaluated using five-fold training/validation cross-validation. The selected configuration achieved a classification accuracy of $0.9875\pm 0.005$ and a macro F1-score of $0.9876\pm 0.0048$. The mean normalized Euclidean localization error was $0.0228\pm 0.0009$, and $99.43\%$ of the de-normalized localization errors were at or below $20\mathrm{mm}$. The shared architecture therefore performs simultaneous perturbation-configuration classification and two-dimensional localization from the same multi-path guided-wave measurements under the investigated controlled perturbation conditions. Full article