Search Results (903)

Queuing is almost inevitable in tourist service experiences, but most tourists are reluctant to wait. Drawing on prospect theory, this study examined how comparative framing influences tourists’ waiting intention. Across three scenario-based experiments, the research found that, compared with non-comparative framing, comparative framing can effectively enhance tourists’ waiting intention. Perceived waiting costs play a mediating role in the impact of the comparative framing on waiting intention. Additionally, the queuing settings play a moderating role, and the mediating effect is stronger in physical queues than in virtual queues. This research shifts the analytical focus from objective waiting time to the framing of waiting-time information, reveals a psychological cost assessment mechanism based on reference points, and enriches the theoretical explanation of tourists’ immediate decision-making in tourism services. It also provides practical references for optimizing service information and queue management during peak hours. Full article

This study aims to investigate the predictive value of pre-treatment multi-phasic contrast-enhanced computed tomography (CECT) radiomic features for treatment resistance in patients with rat sarcoma virus (RAS)-mutated colorectal liver metastases (CRLMs) receiving bevacizumab-based chemotherapy. Seventy-three samples with RAS-mutated CRLMs receiving bevacizumab-combined chemotherapy regimens were evaluated. Radiomic features were extracted from arterial phase (AP), portal venous phase (PVP), AP-PVP subtraction image, and Delta phase (DeltaP, calculated as AP-to-PVP ratio) images. Three groups of radiomics features were extracted for each phase, including peritumor, core tumor, and whole-tumor regions. For each of the four phases, a two-sided independent Mann–Whitney U test with the Bonferroni correction and K-means clustering was applied to the remnant features for each phase. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was then applied for further feature selection. Six machine learning algorithms were then used for model development and validated on the independent testing cohort. Results showed peritumoral radiomic features and features derived from Laplacian of Gaussian (LoG) filtered images were dominant in all the compared machine learning algorithms; NB models yielded the best-performing prediction (Avg. training AUC: 0.731, Avg. testing AUC: 0.717) when combining all features from different phases of CECT images. This study demonstrates that peritumoral radiomic features and LoG-filtered pre-treatment multi-phasic CECT images were more predictive of treatment response to bevacizumab-based chemotherapy in RAS-mutated CRLMs compared to core tumor features. Full article

Automated assessment of coronary artery (CA) lesions via Coronary Computed Tomography Angiography (CCTA) is essential for the diagnosis of coronary artery disease (CAD). However, current deep learning approaches confront several challenges, primarily regarding the modeling of long-range anatomical dependencies, the effective decoupling of plaque texture from stenosis geometry, and the utilization of clinically prevalent mixed-grained annotations. To address these challenges, we propose a novel mixed-grained hierarchical geometric-semantic learning network (MG-HGLNet). Specifically, we introduce a topology-aware dual-stream encoding (TDE) module, which incorporates a bidirectional vessel Mamba (BiV-Mamba) encoder to capture global hemodynamic contexts and rectify spatial distortions inherent in curved planar reformation (CPR). Furthermore, a synergistic spectral–morphological decoupling (SSD) module is designed to disentangle task-specific features; it utilizes frequency-domain analysis to extract plaque spectral fingerprints while employing a texture-guided deformable attention mechanism to refine luminal boundary. To mitigate the scarcity of fine-grained labels, we implement a mixed-grained supervision optimization (MSO) strategy, utilizing anatomy-aware dynamic prototypes and logical consistency constraints to effectively leverage coarse branch-level labels. Extensive experiments on an in-house dataset demonstrate that MG-HGLNet achieves a stenosis grading accuracy of 92.4% and a plaque classification accuracy of 91.5%. The results suggest that our framework not only outperforms state-of-the-art methods but also maintains robust performance under weakly supervised settings, offering a promising solution for label-efficient CAD diagnosis. Full article

Knee osteoarthritis prognostic tools often target structural progression or surgery and require imaging or biomarker inputs that are not routinely available. Using Osteoarthritis Initiative data, we developed a fully clinical nomogram to estimate both the probability of long-term pain non-resilience (clinically important worsening) and, by complement, maintenance of acceptable pain in radiographic knee osteoarthritis. We included participants with radiographic knee osteoarthritis and complete worst-knee WOMAC pain scores at baseline, 24 and 48 months; non-resilience was defined as a ≥9-point increase on the 0–100 WOMAC pain scale over 4 years. A six-predictor Firth logistic regression model (age, body mass index, Kellgren–Lawrence grade, baseline pain, 0–24-month pain change and Center for Epidemiologic Studies Depression Scale score) was fitted and translated into a point-based nomogram. Among 2365 eligible participants, 527 (22.3%) were non-resilient. The model showed good performance, with optimism-corrected AUC 0.74 and Brier score 0.15, and decision-curve analysis indicated positive net benefit versus treat-none across 1–15% thresholds and small gains versus treat-all. Early pain worsening and higher depressive symptoms were the strongest predictors of non-resilience. This six-variable, clinic-ready nomogram provides a simple, well-calibrated tool for prognostic counseling and risk stratification in radiographic knee osteoarthritis and requires external validation before wider clinical use. Full article

Background: The Remnant Cholesterol Inflammatory index (RCII) has been proposed as a marker of insulin resistance and systemic inflammation. However, its associations with incident stroke, incident heart disease, and all-cause mortality among individuals with cardiovascular–kidney–metabolic (CKM) syndrome stages 0–3 remain uncertain. Methods: This longitudinal cohort study used data from the China Health and Retirement Longitudinal Study (CHARLS). The remnant cholesterol inflammatory index (RCII) was calculated as [RC (mg/dL) × hs-CRP (mg/L)]/10. Outcomes included incident stroke, incident heart disease, and all-cause mortality. Covariates were prespecified based on established risk factors. Cox proportional hazards models and restricted cubic spline (RCS) analyses were used to evaluate associations between RCII and each outcome. Long-term RCII patterns were identified using k-means clustering. Robustness was assessed using subgroup and sensitivity analyses. Results: The final study involved 6994 participants in the stroke and heart disease cohort and 7245 participants in the all-cause mortality cohort, all within CKM syndrome stages 0–3. Higher baseline RCII was associated with increased risks of stroke (HR = 1.55, 95% CI: 1.14–2.12) and all-cause mortality (HR = 1.67, 95% CI: 1.37–2.04) compared with the lowest quantile. Cumulative RCII showed a stronger association with all-cause mortality (HR for Q3 = 2.18, 95% CI: 1.54–3.11). RCS analysis suggested a J-shaped, non-linear association between cumulative RCII and all-cause mortality. (p for non-linearity < 0.05). K-means clustering further indicated that, relative to the reference group, cluster 2 (high-to-higher) had the highest risk of incident heart disease, whereas cluster 3 (high-to-moderate) had the highest risk of all-cause mortality. Conclusions: Higher RCII levels were associated with higher risks of stroke, heart disease, and all-cause mortality among individuals with CKM stages 0–3. RCII may serve as a promising biomarker for early risk stratification in clinic and prevention efforts in this population. Full article

Following the normalization of the COVID-19 pandemic, the focus of wastewater-based epidemiology (WBE) must be broadened from SARS-CoV-2 to encompass surveillance of other major infectious diseases, particularly for pathogens where conventional clinical monitoring systems exhibit inherent surveillance gaps. In this study, we conducted a continuous two-year WBE study (January 2023 to December 2024) across three high-population-density cities in Guangdong, China to establish epidemiological baselines for enteric diarrheal viruses. We analyzed monthly raw wastewater samples from major treatment plants using advanced molecular methods, including digital PCR (ddPCR) for viral load quantification and targeted high-throughput sequencing (tNGS) for genotypic analysis. Our findings revealed diverse circulation patterns among the monitored enteric viruses. Astrovirus (AstV) had the highest detection rate (100%), reflecting its broad endemic distribution, while Norovirus genogroup II (NoV GII) exhibited relatively high viral loads (median 4 × 10⁴ copies/mL) and presented explosive seasonal peaks (significant upward trend in spring.), highlighting its epidemic potential. Furthermore, distinct spatiotemporal patterns were observed, with Sapovirus showing a significant summer peak in Foshan city, contrasting with the winter/spring peaks in the other cities. The tNGS results demonstrated similar sensitivity to RT-PCR in virus detection, and sequencing analyses uncovered the co-circulation and periodic shifts in dominant viral genotypes, such as the emergence of multiple NoV and AstV lineages. This longitudinal WBE surveillance successfully established critical baseline data and demonstrated significant regional heterogeneity in viral circulation, providing essential, complementary data to inform public health strategies for preventing diarrheal outbreaks in urban settings. Full article

Phospholipids provide both structural and functional varieties for neuro cells, and their dysregulation in brain has been related to pathogenesis of cognitive impairment. The reflection of these phospholipid alterations in the blood might serve as biomarkers for the early recognition of cognitive decline risk preceding clinical symptoms and provide potential targets for intervention. In this cohort study, detailed phospholipid molecular profiles including 229 species were quantified. A total of 209 participants aged 60–80 years (including 138 women and 73 men) were followed for one year, during which 32 participants developed significant cognitive decline, defined as a decrease of three or more points in the Montreal Cognitive Assessment score. A biomarker panel of eight phospholipid molecular species related to cognitive decline was identified by Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression between cases and non-cases. Among these, four biomarkers, including PE(O-40:5), LPC(18:3), PI(38:2) and PA(39:4), were further proved to be significantly associated with the risk of cognitive decline through a logistic regression model, indicating that the degradation of phospholipids and the accumulation of ether phospholipid and PI might participate in the process of cognitive decline in early stage. By adding the eight phospholipid biomarkers to a reference model that included demographics, lifestyle, hypertension, fasting blood glucose and blood lipid parameters, the AUC value of the predictive model improved from 0.743 to 0.866, which provided a possible auxiliary screening tool for the early detection of cognitive impairment in the elderly. Full article

Acute kidney injury (AKI) is a critical clinical syndrome characterized by a rapid decline in renal function, frequently resulting from ischemia, nephrotoxicity, or sepsis. It represents a major global health burden due to its high morbidity and mortality and its strong association with progression to chronic kidney disease. In this study, we identified a novel small-molecule TLR4 inhibitor, DB03476, via structure-based virtual screening targeting the intracellular TIR domain of murine Tlr4. Molecular dynamics simulations confirmed that DB03476 stabilizes Tlr4 without altering its global conformation. In a murine ischemia–reperfusion-induced AKI model, DB03476 administration significantly attenuated renal inflammation, macrophage infiltration, and apoptosis and suppressed the TLR4/MyD88/NF-κB pathway. Moreover, DB03476 exhibited cross-species efficacy by binding conserved residues in human TLR4 with high affinity. Functional validation using human kidney organoids confirmed its protective effects against inflammatory challenge. These results demonstrate DB03476 as a promising therapeutic agent for AKI through selective inhibition of TLR4-mediated inflammatory responses. Full article

Accurate quantification of Deoxynovalienol (DON) in wheat is critical for food safety, but current methods suffer from poor reproducibility due to inconsistent operational parameters across the sampling and analysis workflow. To address this issue, this study focused on truck-loaded bulk wheat and conducted a comprehensive analysis covering the entire process from sampling to laboratory testing. By examining parameters at each stage—test portion, laboratory sample, composite sample, and primary sample—and applying the Monte Carlo simple random sampling principle, the variability associated with the full-process parameters for DON detection in wheat was systematically analyzed. The errors introduced at each step were evaluated, leading to the development of a representative measurement procedure for DON in truck-loaded bulk wheat. The results indicate that for truck-loaded bulk wheat, sampling should be conducted using a random distribution method with no fewer than 11 sampling points, each providing a primary sample of at least 500 g. The composite sample should be homogenized three times using a cone-and-quartering divider before subsampling. The laboratory sample should weigh no less than 750 g and be ground to a particle size of 1 mm. After thorough mixing of the ground sample, 5 g should be accurately weighed for analysis. This measurement procedure introduces a total relative error of 12.9%. The proposed protocol for DON detection in truck-loaded wheat offers a practical approach that minimizes error contribution from each parameter while maintaining low economic and time costs, ensuring feasibility for field implementation. Full article

Enhancing low-light images is crucial in computer vision applications. Most existing learning-based models often struggle to balance light enhancement and color correction, while images typically contain different types of information at different levels. Hence, we proposed a multi-scale interactive network with color attention named MSINet to effectively explore these different types of information for lowlight image enhancement (LLIE) tasks. Specifically, the MSINet first employs the CNN-based branch built upon stacked residual channel attention blocks (RCABs) to fully explore the image local features. Meanwhile, the Transformer-based branch constructed by Transformer blocks contains cross-scale attention (CSA) and multi-head self-attention (MHSA) to mine the global features. Notably, the local and global features extracted by each RCAB and Transformer block are interacted with by the fusion module. Additionally, the color correction branch (CCB) based upon self-attention (SA) can learn the color distribution information from the lowlight input for further guaranteeing the color fidelity of the final output. Extensive experiments have demonstrated that our proposed MSINet outperforms state-of-the-art LLIE methods in light enhancement and color correction. Full article

The relationship between the oral microbiome and autoimmune diseases (ADs) has attracted considerable research interest. This study employed two-sample Mendelian randomization (MR) to investigate causal relationships between oral microbiota and six ADs, including rheumatoid arthritis (RA), type 1 diabetes (T1D), inflammatory bowel disease (IBD), multiple sclerosis (MS), systemic lupus erythematosus (SLE), and Sjögren’s syndrome (SS). Using genome-wide association study data from oral microbiome features and ADs, we applied inverse-variance weighted estimation complemented by sensitivity analyses and reverse MR to assess robustness and reverse causation. Analysis of 309 tongue dorsum and 285 salivary microbial features identified four tongue dorsum and five salivary taxa with genome-wide significant causal effects. Specific microbial taxa from both oral niches demonstrated protective or risk-enhancing effects for RA, T1D, IBD, and MS, while no causal associations were found for SLE or SS. These findings establish the causal role of specific oral microbiota in autoimmune pathogenesis and highlight priority candidates for further investigation as potential microbial biomarkers. Full article

The GI.3 norovirus is the most detected and recombinant-rich genotype within genogroup I, yet the mechanistic basis for its epidemiological success remains poorly understood. This study integrates Bayesian evolutionary analysis with in vitro enzymology to investigate the link between RdRp function and the evolutionary dynamics of GI.3 NoV. We analyzed 831 GI.3 sequences, finding that prevalent strains (GI.3[P3] and GI.3[P13]) exhibited significantly higher evolutionary rates in both the RdRp and VP1 genes than non-prevalent strains (GI.3[P10] and GI.3[P14]). While the RdRp gene displayed a strong molecular clock signal, the VP1 gene’s evolution was more complex, showing cluster-specific trends. Functionally, the RdRps from prevalent strains demonstrated superior enzymatic activity and substrate affinity (K_m: GI.3[P13] = 0.092 mM; GI.3[P3] = 0.176 mM) compared to non-prevalent strains (K_m: GI.3[P14] = 0.273 mM). Notably, GI.3 RdRp required higher manganese ion concentrations for optimal activity than previously reported for GII strains, suggesting a potential biochemical constraint. Our findings demonstrate a clear correlation between RdRp enzymatic efficiency, evolutionary rate, and strain prevalence. We propose that a highly active RdRp may potentially accelerate VP1 evolution and confer a replicative advantage, underpinning the dominance of specific GI.3 lineages. This work provides crucial experimental evidence linking viral polymerase function to evolutionary and epidemiological outcomes. Full article

Background: Depression and anxiety are prevalent mental health disorders among children and adolescents, with diet quality emerging as a modifiable risk factor. However, evidence regarding the association between comprehensive diet quality and mental health in school-aged children remains limited. Methods: This cross-sectional study included 400 Chinese children aged 8–12 years. Diet quality was assessed using the low-burden Diet Quality Questionnaire (DQQ), from which three Global Diet Recommendations (GDRs) scores were derived: GDR-Healthy, GDR-Limit, and total GDR. Depression and anxiety symptoms were evaluated using the Children’s Depression Inventory (CDI) and the Social Anxiety Scale for Children (SASC), respectively. Log-binomial regression models were used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) for the associations between GDR scores and mental health symptoms (depression, anxiety, comorbidity). Subgroup analyses stratified by age and sex were conducted to explore heterogeneity. Results: Higher total GDR scores were associated with lower risks of depressive symptoms (RR = 0.90, 95% CI: 0.84–0.96), anxiety symptoms (RR = 0.93, 95% CI: 0.88–0.99), and their comorbidity (RR = 0.88, 95% CI: 0.79–0.97) after adjustment for age, sex, zBMI, physical activity, region of residence, only-child status and parental education. The GDR-Healthy score was independently associated with lower risks of depression symptoms (RR = 0.89, 95% CI: 0.83–0.96) and comorbidity (RR = 0.87, 95% CI: 0.79–0.95), while no significant associations between GDR-Limit score and mental health disorders were observed. Subgroup analyses indicated that the association was consistent across sex and age subgroups. Conclusions: Better diet quality and particularly higher intake of health-protective foods is associated with lower risks of depression, anxiety, and their comorbidity symptoms in Chinese school-aged children in this cross-sectional study. These findings support the integration of diet quality monitoring and nutritional interventions into public health strategies to promote mental health in children. Full article

Poly Ether-Ether Ketone (PEEK) is used in modified atmosphere packaging (MAP) for fruit and vegetable preservation, but raises health concerns. This study investigated the effects of PEEK food migrations on liver cell mitochondrial damage. Food simulants (95% ethanol, 10% ethanol, and 4% acetic acid) were used for migration tests according to guideline recommendations, and liver cells were treated with PEEK food migrations for 24 h. Results showed decreased cell viability, increased reactive oxygen species (ROS), reduced mitochondrial membrane potential (MMP), mitochondrial DNA copy number (mtDNAcn), and down-regulated PGC-1α/Nrf2 pathway-related genes (Sirt1, PGC-1α, NRF1, Nrf2, TFAM). Furthermore, these alterations were reversed, and mitochondrial damage was alleviated by the addition of the PGC-1α activator ZLN005. In conclusion, high PEEK concentrations induce mitochondrial toxicity in liver cells via the PGC-1α/Nrf2 pathway, posing health risks and necessitating safe dosage limits in food packaging. Full article