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Purpose Inulin is a type of fermentable dietary fiber, which is non-digestible, and can improve metabolic function by modulating intestinal microbiota. This study aimed to evaluate the role of inulin in hyperuricemia and microbial composition of the gut microbiota in a mouse model of hyperuricemia established through knockout of Uox (urate oxidase) gene. Methods KO ( Uox -knockout) and WT (wild-type) mice were given inulin or saline by gavage for 7 weeks. The effect of inulin to combat hyperuricemia was determined by assessing the changes in serum UA (uric acid) levels, inflammatory parameters, epithelial barrier integrity, fecal microbiota alterations, and SCFA (short-chain fatty acid) concentrations in KO mice. Results Inulin supplementation can effectively alleviate hyperuricemia, increase the expressions of ABCG2 in intestine, and downregulate expression and activity of hepatic XOD (xanthine oxidase) in KO mice. It was revealed that the levels of inflammatory cytokines and the LPS (lipopolysaccharide) were remarkably higher in the KO group than those in the WT group, indicating systemic inflammation of hyperuricemic mice, but inulin treatment ameliorated inflammation in KO mice. Besides, inulin treatment repaired the intestinal epithelial barrier as evidenced by increased levels of intestinal TJ (tight junction) proteins [ZO-1 (zonula occludens-1) and occluding] in KO mice. Moreover, serum levels of uremic toxins, including IS (indoxyl sulfate) and PCS ( p -cresol sulfate), were reduced in inulin-treated KO mice. Further investigation unveiled that inulin supplementation enhanced microbial diversity and raised the relative abundance of beneficial bacteria, involving SCFAs-producing bacteria (e.g., Akkermansia and Ruminococcus ). Additionally, inulin treatment increased the production of gut microbiota-derived SCFAs (acetate, propionate and butyrate concentrations) in KO mice, which was positively correlated with the effectiveness of hyperuricemia relief. Conclusions Our findings showed that inulin may be a promising therapeutic candidate for the treatment of hyperuricemia. Moreover, alleviation of hyperuricemia by inulin supplementation was, at least, partially conciliated by modulation of gut microbiota and its metabolites.

The incidence of Metabolic dysfunction-associated steatotic liver disease (MASLD) is rising annually. Dietary intervention is a cornerstone of MASLD management. Flavonoids, which have anti-inflammatory properties, are thought to benefit MASLD. Using data from the National Health and Nutrition Examination Survey (NHANES) from 2007–2010 and 2017–2018, we conducted a large cross-sectional study. Weighted Logistic regression, Restricted Cubic Spline (RCS) models, and Weighted Quantile Sum (WQS) regressions were used to explore the relationship between Total flavonoid and subclass (Flavones, Flavan-3-ols, Flavonols, Flavonones, Isoflavones, Anthocyanins) intake and MASLD. Participants in the higher tertiles of Total flavonoids intake had 31–34% lower odds of MASLD compared to the lowest tertile intake in the fully adjusted models (Tertile2: OR 0.69, 95%CI 0.55–0.86, P = 0.002, Tertile 3: OR 0.66, 95%CI 0.52–0.84, P < 0.001). Increased intakes of Flavan-3-ols (Tertile 2 in Model 2: OR 0.65, 95%CI 0.49–0.87, P = 0.01), Flavanones (Tertile 3 in Model 2: OR 0.70, 95%CI 0.53–0.91, P = 0.01), and Isoflavones (Tertile 3 in Model 2: OR 0.65, 95%CI 0.52–0.83, P < 0.001) were significantly associated with 30–35% decreased odds of having MASLD. The RCS revealed a significant non-linear dose-response relationship between Total flavonoid, flavonols and MASLD. The WQS model showed that Flavones and Isoflavones had the largest negative contributions to MASLD risk. Our study demontrated a negative correlation between Total flavonoids and their subclasses and risk of MASLD, highlighting the importance of increasing dietary flavonoid intake in the prevention and treatment of MASLD.

Due to the presence of the blood–brain barrier (BBB), the delivery of general drugs into the brain tissue remains to be a tricky problem. For patients with brain metastases from breast cancer, drug delivery systems must overcome this physical barrier. Targeted nano vehicles arise as a promising alternative to deliver drugs to brain tissues successively. Herein, a dual targeting micelle drug delivery system loaded with paclitaxel (PTX) and lapatinib (LPTN) was developed for combinational therapy of brain metastases. In our study, it was shown the micelles modified with Angiopep-2 had high loading efficiency of paclitaxel and lapatinib (Ang-MIC-PTX/LP). In addition, Ang-MIC-PTX/LP could transport across the in vitro BBB model and accumulate in breast cancer cells. After intravenous injection, Ang-MIC significantly accumulated in the brain metastasis. Ang-MIC-PTX/LP could also extend the life span of brain metastasis mouse models. Overall, this study provided a promising method for treatment of brain metastases from breast cancer.

The effective drug treatment of rheumatoid arthritis (RA) is hindered by poor delivery efficiency to the diseased site and the serious side effects caused by wide-spread drug distribution. Traditional drug-targeting strategies, such as ligand modification, are complex, laborious, and inefficient. Inspired by the intrinsic relationship between platelets and RA, platelet-mimetic nanoparticles (PNPs) were developed for targeted drug delivery in RA. Through platelet receptor-mediated adhesion, an intact platelet membrane was coated onto poly (lactic-co-glycolic acid) nanoparticles, endowing the resulting PNPs with various functional receptors. By coating with platelet membranes, the nanoparticles were stabilized and had a better circulation profile, providing a benefit for passive targeting. In vitro binding of PNPs to inflamed endothelium, and in vivo accumulation in joints of a collagen-induced arthritis (CIA) mouse model of RA were significantly improved via P-selectin and GVPI recognition, indicating that the PNPs could effectively target to RA tissues through multiple mechanisms, similar to natural platelets. Moreover, FK506, a model drug, was loaded into the PNPs and used to treat RA. Pharmacodynamic studies demonstrated that the FK506-PNPs had a notable anti-arthritic effect in CIA mice. This study provides a new biomimetic targeting strategy with great potential for the treatment of RA.

In geotechnical analysis, the factor of safety (FOS) is crucial for slope stability assessment. Traditional methods often overlook the nuances of partial slope instability. Accurately determining geotechnical parameters for FOS in complex simulations is challenging and resource-intensive. The limit equilibrium method (LEM), considering unit weight, cohesion, and internal friction angle, is used to address this. This study focuses on the Jiuxianping landslide, analyzing its stability and failure behavior. Utilizing the Bayesian theorem, the study back-analyzes shear strength parameters, considering partial instability and uncertainties in the Janbu corrected method. The parameters’ posterior distribution is determined using the Markov Chain Monte Carlo (MCMC) method and Multivariate Adaptive Regression Splines (MARS) for efficient sampling. These parameters are then used for precise FOS calculation at the critical point of partial instability, corroborated by 2021 data from the Jiuxianping landslide. The study finds that while the entire slope remains stable, partial instability caused by long-term water erosion significantly lowers the FOS about 10.9%, highlighting its critical impact on overall slope stability.

Automatic insertion machines play an important role in the assembly of through-hole electronic components on printed circuit boards (PCBs). However, for irregular multi-pin components, fixed-position registration strategies may fail to recover feasible insertion poses under grasping errors, manufacturing tolerances, and transportation-induced pin perturbations. To address this issue, this paper proposes a maximum-clearance optimization model with chaotic initialization for PCB component pin registration. First, the Graham–Scan algorithm is employed to generate convex polygons to simulate the geometric layout of component pins. Subsequently, random perturbations following two-dimensional normal distributions are introduced to simulate pin deformation caused by manufacturing and transportation processes. Then, a maximum-clearance objective is formulated to determine a feasible insertion pose under bounded translational and rotational adjustments. Finally, the resulting registration model is first evaluated through a solver-level comparison among three general-purpose numerical optimizers—namely, SLSQP, genetic algorithm, and simulated annealing—all applied to the same maximum-clearance formulation. In addition, representative registration baselines are introduced in a supplementary cross-paradigm comparison to clarify the difference between alignment-oriented registration accuracy and clearance-oriented insertion feasibility. Experimental results under an industrially motivated simulation setting show that, when solved by SLSQP, the proposed model achieved a clearance-based surrogate success rate of 98.44% over a complete simulated dataset of 1,400,000 samples, while maintaining an average computation time of 0.0198 s per sample. These results suggest that the proposed method provides computational evidence for the potential usefulness of a clearance-oriented geometric registration framework under simulated PCB insertion conditions motivated by field investigation data from Dalian Rijia Electronics Co., Ltd., rather than direct validation on a fully instrumented production line.

The aim of this study was to explore the impact of Geriatric Nutritional Risk Index (GNRI) and body water component (BWC) on the survival of colorectal cancer (CRC) patients and whether the combined effect had a potential prognostic and predictive efficacy. We evaluated the accuracy of GNRI for malnutrition and estimated the predictive capacity of BWC for survival. Kaplan–Meier survival curves and cox regression analyses were used to examine the prognostic effects. A nutrition-water score (NWS) model was developed and evaluated the survival predictive power. GNRI and extracellular water-to-intracellular water ratio (ECW/ICW) were integrated, with the cut-off values of 103.5 and 63.7%. Lower GNRI and higher ECW/ICW were independent risk factors for poor prognosis in CRC patients. The combination of the two into the NWS model demonstrated a higher risk of death for patients with NWS ≥ 1 compared to those with NWS of 0. NWS showed a better predictive capability compared to GNRI and ECW/ICW, with the concordance index of 0.681. Our study demonstrates GNRI and ECW/ICW’s prognostic utility in CRC, with their combination improving survival prediction to help guide patient-centered treatment.

Aimed at identifying the health state of wind turbines (WTs) accurately by using the comprehensive spatio and temporal information from the supervisory control and data acquisition (SCADA) data, a novel anomaly-detection method called decomposed sequence interactive network (DSI-Net) is proposed in this paper. Firstly, a DSI-Net model is trained using preprocessed data from a healthy state. Subsequences of trend and seasonality are obtained by DSI-Net, which can dig out underlying features both in spatio and temporal dimensions through the interactive learning process. Subsequently, the trained model processes the online data and calculates the residual between true values and predicted values. To identify anomalies of the WTs, the residual and root mean square error (RMSE) are calculated and processed by exponential weighted moving average (EWMA). The proposed method is validated to be more effective than the existing models according to the control experiments.

Let G be a simple connected graph with edge set E(G) and vertex set V(G). The weighted Mostar index of a graph G is defined as w+Mo(G)=∑e=uv∈E(G)(dG(u)+dG(v))|nu(e)−nv(e)|, where nu(e) denotes the number of vertices closer to u than to v for an edge uv in G. In this paper, we obtain the upper bound and lower bound of the weighted Mostar index among all bicyclic graphs and characterize the corresponding extremal graphs.

Skeletal muscle mass assessment using computed tomography (CT) is crucial for evaluating nutritional status and prognosis in cancer patients. While the third lumbar vertebra (L3) level is widely accepted for this purpose, not all patients undergo abdominal CT scans. This study aimed to explore the potential of the third thoracic vertebra (T3) level as an alternative measurement site. This retrospective study included 257 patients with digestive system malignancies. Skeletal muscle area (SMA) and skeletal muscle index (SMI) were measured at both T3 and L3 levels using CT scans. Correlation analyses, linear regression models, and cox regression analyses were performed to evaluate the relationship between T3 and L3 measurements and their prognostic value. Strong correlations were observed between T3 and L3 measurements ( r  = 0.833 for SMA, r  = 0.747 for SMI). A multivariate linear regression model effectively predicted L3 SMA from T3 SMA (adjusted R ² = 0.829). Cox regression analyses revealed that lower T3 SMA and SMI were independently associated with increased mortality risk. Patients in the lowest quartile of T3 SMA had significantly higher mortality risk compared to those in the highest quartile (HR = 5.82, 95% CI: 1.86–18.16, P  = 0.002), after adjusting for confounders. Similar results were observed for T3 SMI and L3 measurements. T3 skeletal muscle measurements strongly correlate with L3 measurements and serve as independent prognostic factors in patients with digestive system malignancies. T3 measurements offer a viable alternative for assessing skeletal muscle mass and predicting prognosis when L3 measurements are unavailable.