Search Results (6,203)

Background: Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy, for which population-level evidence regarding prognostic factors and survival conditions is limited. The available data mostly represent single-institution series, limiting their applicability. This study, therefore, assesses clinicopathological features and determines independent predictive variables of overall survival (OS) in patients with ACC using a population-based cohort. Methods: This retrospective observational cohort study used data from the Surveillance, Epidemiology, and End Results (SEER) Program between 2000 and 2022, initially identifying 1176 patients with ACC. Adult patients (≥18 years) with histologically confirmed ACC were identified using ICD-O-3 histology code 8370/3 and primary site code C74.0. Cases with zero-month survival, missing survival data, or identified only through autopsy or death certificate were excluded. To ensure dataset harmonization, patients with missing or indeterminate tumor grade and unknown stage were also excluded. After applying these inclusion and exclusion criteria, the final analytic cohort consisted of 267 patients. Data on demographic factors, stage of the disease, and treatment (surgery, chemotherapy, radiotherapy) were extracted. OS was evaluated using the Kaplan–Meier method, and independent prognostic factors were identified using Cox proportional hazards regression analysis. Results: The median OS was 54 months [95% confidence intervals (CI): 36–85]. The estimated 1-, 3-, and 5-year OS rates were 77%, 57%, and 48%, respectively. Survival differed significantly according to tumor grade, stage, and surgical treatment. In multivariable Cox regression analysis, increasing age [Hazard ratio (HR): 1.03, 95% CI: 1.02–1.04; p < 0.001], high tumor grade (HR: 2.21, 95% CI: 1.43–3.41; p < 0.001), and distant-stage disease (HR: 3.24, 95% CI: 1.95–5.38; p < 0.001) were independently associated with an increased risk of mortality, whereas surgical treatment was associated with improved survival (HR 0.53, 95% CI 0.30–0.93; p = 0.028). Chemotherapy and radiotherapy were not significantly associated with mortality. Conclusion: In this SEER-based cohort of patients with adrenocortical carcinoma, older age, high tumor grade, and distant-stage disease were independently associated with worse OS, whereas documented receipt of surgery was associated with longer OS. Treatment-related associations should be interpreted cautiously in view of the inherent limitations of registry-based data. Further prospective multicenter studies are needed to confirm these findings. Full article

The expansion of organic farming in Europe increases the co-occurrence of medicinal and aromatic plant crops and pyrrolizidine alkaloid (PA)-producing weeds, raising serious contamination concerns. This study evaluated the risk of PA contamination in organically grown chamomile (Matricaria recutita L.) under field conditions in the North Vidzeme region of Latvia, with particular emphasis on vertical PA distribution in dominant weeds and on whether PA occurrence could be detected in chamomile plants growing adjacent to PA-producing weeds under field conditions. Three commercial fields were surveyed using systematic quadrat sampling to quantify weed density, biomass, and height. PA-producing weeds were segmented into 5 cm fractions, and pyrrolizidine alkaloids were quantified by LC-HRMS. Myosotis arvensis was the dominant species (up to 48,000 plants ha⁻¹), contributing the highest field-level PA load (up to 669.3 mg ha⁻¹), whereas Anchusa arvensis occurred at lower densities (≤2400 plants ha⁻¹) with a total PA load of 104.8 mg ha⁻¹. In both species, PA concentrations increased toward upper plant segments, while contamination hazard at harvest was determined by the amount of PA-bearing biomass in the harvest-relevant zone. No PAs were detected in chamomile samples collected within 10 cm of PA-producing weeds (<LOQ). Under the investigated conditions, contamination hazard was primarily associated with mechanical admixture during harvest rather than soil-mediated transfer. Full article

Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine strongly associated with aging, multimorbidity, and cardiovascular disease. Although prior studies have established its prognostic value in high-risk populations, its role in the general population remains less defined. The aim of this study was to determine if there is an association between plasma GDF15 levels, heart disease and mortality in a representative population-based cohort. We analyzed 1532 participants (mean age 55 years; 54.6% women) with available baseline plasma GDF15 concentrations. Participants were stratified according to an optimal cutoff of 1081 pg/mL, derived from ROC curve analysis for mortality. Associations with prevalent heart disease were assessed using multivariable logistic regression models adjusted for cardiovascular risk factors and NT-proBNP. Mortality was analyzed using Cox proportional hazards models, with model performance evaluated by C-index and time-dependent ROC curves. Individuals with GDF15 > 1081 pg/mL were older and exhibited a more adverse cardiometabolic profile with higher prevalence of comorbidities. Elevated GDF15 was independently associated with ischemic cardiomyopathy (OR 3.34, 95% CI: 1.38–8.11), particularly in men (OR 4.26, 95% CI: 1.40–12.96), but not in women. No independent associations were observed with arrhythmias, valvulopathy, or heart failure after adjustment for NT-proBNP. During a median follow-up of 6.2 years, 51 deaths occurred. Elevated GDF15 independently predicted all-cause mortality (HR 2.47, 95% CI: 1.19–5.13), though the effect was attenuated after adjustment for NT-proBNP. GDF15 improved model discrimination (ΔC-index = +0.01; LRT p = 0.011) and showed robust time-dependent predictive ability, with AUCs of 0.76, 0.82, and 0.85 at 2, 4, and 6 years, respectively. In this population-based cohort, elevated GDF15 identified individuals with an adverse health profile, was independently associated with ischemic cardiomyopathy in men, and predicted mortality. Although its incremental predictive value over NT-proBNP was modest, GDF15 could provide complementary biological information and may enhance multimarker strategies for cardiovascular risk stratification in the general population. Full article

The heavy metal pollution linked to extractive activities has attracted broad public attention. To examine the current state of heavy metal pollution in river sediments around iron tailing zones, this study was carried out to evaluate the distribution features, potential sources, and environmental hazards of heavy metals (HMs, Cr, Cd, Ni, Cu, Zn, Pb, As, and Hg) in the surface sediments of rivers in the Xiangshan area of Ma’anshan City. Results indicated that, except for Cr, the mean heavy metal concentrations exceeded the soil background levels in Anhui’s Huaihe River Basin. Variability in metal concentrations among the sediments was moderate, exhibiting an uneven spatial distribution. Significant positive correlations were detected between various HMs in the sediments, suggesting a common pollution source. Source analysis findings revealed that the HMs primarily originate from agricultural fertilization, mining, and smelting activities. Evaluation results from both the single-factor pollution index and the Nemerow comprehensive index indicated that the upstream section of the Caishi River is severely polluted by HMs. The potential ecological risk index evaluation results demonstrated that 85% of sediment samples from sampling points achieved a high comprehensive potential ecological risk level for HMs, with Cd, Cu, and Hg identified as the key contributors. The human health risk assessment demonstrated that both adults and children are subjected to carcinogenic risks from heavy metal exposure, with children exhibiting a higher risk level. This study offers valuable insights into managing heavy metal contamination in river sediments adjacent to iron tailings regions. Full article

This study presents a comprehensive investigation of OPE residues, distribution patterns, and dietary exposure risks in crustaceans from southeast China. OPEs were detected in over 90% of samples, with mean total concentrations (ΣOPEs) of 5.80 μg/kg wet weight (ww) in freshwater shrimp, 6.52 μg/kg ww in marine prawn, and 1.25 μg/kg ww in marine crab. Tributyl phosphate (TiBP), triethyl phosphate (TEP), and tris(2-chloroethyl) phosphate (TCEP) emerged as the dominant congeners, accounting for 68.1% of ΣOPEs, which indicates inputs from industrial emissions, plastic waste leaching, and aquaculture equipment. Spatial analysis revealed striking regional differences: coastal industrial cities (Zhoushan, Taizhou) exhibited ΣOPE levels up to 12-fold higher than inland mountainous areas (Quzhou, Lishui), while no significant temporal variations were observed. Human health risk evaluation, based on estimated daily intake (EDI) and target hazard quotient (THQ), demonstrated negligible non-carcinogenic risks for the general population (HI < 1), though children and frequent seafood consumers have slightly elevated exposure. These findings indicate the value of crustaceans as bioindicators for OPE contamination and require long-term monitoring of emerging OPEs and their synergistic effects with co-occurring pollutants. Full article

Beach nourishment is a widely adopted nature-based solution for coastal erosion; however, its design efficacy and morphodynamic resilience under extreme wave conditions remain inadequately quantified, posing challenges for coastal hazard assessment. This study integrates physical-model experiments and XBeach numerical simulations to investigate the hydrodynamic and morphodynamic behavior of nourished beaches subjected to typhoon-driven extreme wave conditions at a headland-bay beach on Meizhou Island, China. Physical-model experiments were conducted to examine shoreline response and sediment redistribution under extreme waves for three nourishment tests. XBeach simulations resolved wave-induced currents, water-level variations, and sediment transport processes, enabling continuous tracking of nearshore hydrodynamics and beach profile evolution for three nourishment tests during Typhoon Doksuri. Results indicate that nourishment geometry and groin configuration play a dominant role in wave breaking patterns, sediment transport pathways and erosion–deposition distributions. Groin positions strongly influence alongshore sediment transport. Relocating the groin to an accretional zone reduces lee-side erosion and promotes a more stable shoreline. Steeper nourishment foreshore slopes promote offshore wave shoaling and breaking, enhancing fast wave-energy dissipation, shifting erosion seaward and limiting landward erosion extent. Consistent responses from both experimental and numerical results demonstrate that nourishment stability under extreme wave conditions is better characterized by the combined effects of erosion extent, erosion length, erosion depth, erosion volume, and alongshore and cross-shore sediment redistribution. The integrated physical–numerical approach provides a practical framework for assessing beach nourishment stability during coastal hazard events and offers guidance for the design and evaluation of resilient beach nourishment in wave-dominated, typhoon-prone coastal regions. Full article

Climate change and rapid urbanization are intensifying flood risks in China, particularly in regions with complex terrain and dense populations. Traditional risk assessment methods often lack the flexibility to handle uncertainties in multi-dimensional risk systems. This study proposes a probabilistic flood risk assessment framework integrating Monte Carlo simulation with a composite indicator system from the perspective of disaster system theory. Taking Hunan Province as a case study, we constructed a hierarchical indicator system encompassing environmental susceptibility, hazard intensity, exposure vulnerability, and mitigation capacity. The analytic hierarchy process (AHP) and coefficient of variation (CV) methods were combined for indicator weighting, and Monte Carlo simulation was employed to quantify uncertainties and classify risk levels. Results reveal significant spatial heterogeneity in flood risk across the province, with high-risk areas concentrated in regions exhibiting intense rainfall, dense river networks, and insufficient mitigation infrastructure. The study provides a transferable, data-driven approach for spatially explicit flood risk zoning, offering evidence-based insights for land-use planning, resilient infrastructure development, and sustainable flood governance. This research contributes to the integration of probabilistic modeling into land system science, supporting disaster risk reduction and climate adaptation strategies aligned with SDG 11. This study also provides policy-relevant insights for regional flood governance by supporting risk-informed land-use planning, targeted infrastructure investment, and adaptive flood management strategies, thereby contributing to more resilient and sustainable land system development under increasing climate uncertainty. Full article

Background: In recent years, the severity of COVID-19 has diminished. However, some patients progressed to respiratory failure, necessitating intubation and mechanical ventilation. This study investigated the impact of variants of concern and vaccination status on mortality in mechanically ventilated patients. Method: We conducted a retrospective analysis of the medical records of intubated COVID-19 patients from 1 January 2021, to 31 December 2023. Patients who received at least one dose of a vaccine were classified as vaccinated, and variant types were classified based on the dominant variant reported by the Taiwan Centers for Disease Control. The primary outcome measured was time from intubation to all-cause in-hospital death. Result: A total of 254 patients were analyzed, comprising 65 patients infected with the Alpha variant and 189 with the Omicron variant. Clinical data, including variant type, vaccination status, and SOFA score at the time of intubation, were meticulously recorded. The overall mortality rate was 40%, with two epidemic surges occurring in 2021 and 2022. Infection with the Alpha variant was associated with a significantly higher risk of mortality (adjusted hazard ratio = 5.42 (2.78–10.7); p < 0.01). Key prognostic factors identified included age, body mass index, SOFA score, and serum bicarbonate levels. Conclusions: The overall mortality rate remained notably high. The study identified several factors associated with increased mortality risk, including older age, higher SOFA scores, Alpha variant infection, decreased serum bicarbonate levels, and lower BMI. However, vaccination status was not a significant prognostic indicator. Full article

Sustainable spatial development requires land-use allocation that aligns with reflects the environment’s biophysical capacity. However, rapid urbanization and agricultural expansion often result to spatial mismatches between land utilization and land capability, the reby increasing environmental degradation and disaster vulnerability. East Java Province, one of Indonesia’s most densely populated regions, has experienced significant land-use transformation driven by demographic pressure and economic development. This study aims to evaluate the environmental carrying capacity by assessing the spatial compatibility among land capability, existing land use, and the Provincial Spatial Plan (RTRWP) using a Geographic Information System (GIS)-based analytical approach. Land capability was determined based on key biophysical parameters, including slope gradient, soil texture, drainage conditions, erosion susceptibility, effective soil depth, and flood hazard. Spatial overlay analysis was employed to identify areas of conformity and mismatch between land capability and both current and planned land uses. The results indicate that only approximately 52% of the provincial area is utilised in accordance with its land capability. In comparison, the remaining 48% exhibits varying degrees of spatial mismatch. Erosion is identified as the dominant limiting factor, affecting more than 43% of the region, particularly in mountainous and hilly landscapes. Furthermore, over 60% of East Java falls within Land Capability Classes III–VII, indicating moderate to severe environmental constraints on limitations intensive land use. High levels of spatial mismatch are concentrated in the southern upland districts—such as Pacitan, Trenggalek, southern Malang, and Lumajang, which are highly susceptible to landslides, as well as in the northern lowland corridor, including the Surabaya–Gresik–Sidoarjo metropolitan region, which faces a significantly flood risk. These findings suggest that land-use practices exceeding environmental carrying capacity substantially amplify disaster risk. Therefore, integrating land capability assessment into spatial planning and zoning regulations is essential and for promoting ecosystem-based disaster risk reduction and achieving sustainable spatial development in East Java Province. Full article

Risk-based management of water quality in drinking water supply systems requires decision-support tools that extend beyond parameter-level hazard assessment and enable prioritization at the level of physical system objects. In this context, hazard assessment refers specifically to drinking water quality parameters and their possible operational and health-related implications, particularly in facilities serving sensitive user groups. This study proposes a class-based extension of the FPOR (Fuzzy Priority of Objects at Risk) framework to support object-level operational prioritization under conditions of limited data availability. Hazard importance is adopted from prior hazard prioritization using the Fuzzy Priority Index (FPI), while priority premises (PP) are represented as object classes reflecting typical functional and operational characteristics. Class-based profiles of local hazard relevance and object vulnerability are defined using expert-informed fuzzy representations and aggregated into FPOR scores to produce a relative ranking of priority premises classes. The results demonstrate how hazard prioritization can be systematically propagated to object-level decision units without reliance on site-specific monitoring data. The proposed framework provides a transparent and scalable basis for early-stage risk-based planning and supports the operational implementation of object-oriented management strategies in drinking water systems, while maintaining a clear conceptual separation from health risk assessment addressed in subsequent studies. Full article

Synthetic dyes discharged from the textile and dyeing industry present a significant environmental and health hazard due to their inherent toxicity, environmental persistence, and potential carcinogenicity. Microbial degradation has garnered significant interest as a cost-effective and eco-friendly strategy for dye wastewater treatment in recent years. The study systematically evaluated the decolorization performance, degradation pathways, and detoxification effects of three bacterial strains, including Rhodopseudomonas palustris gh32, Bacillus cereus HL7, and Bacillus safensis X64, on the dye indigo carmine (IC) and three azo dyes: reactive black 5 (RB5), direct black G (DBG), and direct blue 15 (DB15). The degradation mechanisms were elucidated through UV-Vis spectroscopy, UPLC-Orbitrap-HRMS analysis, and enzyme activity assays. Molecular docking simulations were employed to investigate the interactions between key redox enzymes (such as laccase, tyrosinase, and azoreductase) and the dye molecules. The results demonstrated that the strain-specific enzymatic systems effectively disrupted the dye structures. Significant detoxification effects were further confirmed through a series of bio toxicity assays involving Escherichia coli, Bacillus subtilis, plant seeds, and erythrocytes. The addition of Fe³⁺, sodium citrate, or yeast extract significantly enhanced both the decolorization efficiency and enzyme activity. This study provides an in-depth understanding of the bacterial dye degradation process at the mechanistic level, highlighting the potential of customized bacterial systems for eco-friendly dye wastewater treatment. It offers theoretical support for elucidating the mechanisms of bacterial dye degradation and advancing bioremediation technologies. Full article

Background/Objectives: Insulin resistance (IR) is increasingly recognized as a factor associated with metabolic syndrome and various cancers. The metabolic score for insulin resistance (METS-IR) has emerged as a reliable surrogate marker for assessing IR. This study evaluated the association between the METS-IR and the gastric cancer (GC) incidence using data from a nationwide South Korean cohort. Methods: Data were obtained from the National Health Insurance Service (NHIS) cohort. A total of 318,336 participants aged ≥40 years who underwent a nationwide health screening between 2009 and 2010 were included and followed until GC diagnosis, death, or 31 December 2019. The METS-IR was calculated and categorized into quartiles. Hazard ratios (HRs) for GC incidence were estimated using Cox proportional hazards models. The analyses were adjusted for confounders, including age, sex, socioeconomic status, lifestyle factors, and comorbidities. Results: Participants in the highest METS-IR quartile (Q4) exhibited a significantly higher crude incidence of GC (2.26 per 1000 person-years) than those in the lowest quartile (Q1: 1.97 per 1000 person-years). Adjusted HRs showed a modest but statistically significant increase in GC risk in Q4 (HR: 1.10; 95% confidence interval: 1.02–1.19; p = 0.01) compared to Q1. Kaplan–Meier analysis revealed that participants with higher METS-IR levels had significantly shorter GC-free survival times than those in the lower quartiles. Restricted cubic spline analysis revealed a nonlinear relationship between the METS-IR and GC risk, with higher METS-IR levels associated with an increased risk. Conclusions: An elevated METS-IR was associated with an increased GC risk, suggesting its potential utility in stratifying GC risk. The METS-IR may help identify high-risk individuals and support GC prevention. Full article

Safety monitoring in container hoisting operations within rail-road intermodal logistics parks is a critical task in industrial safety management. Such scenarios are characterized by complex environments, large variations in target scales, deformable object shapes, and frequent occlusions, which pose significant challenges to visual perception systems. Conventional single-task models suffer from inherent limitations in handling low recall rates for distant small targets and insufficient adaptability to geometric deformations, making them inadequate for high-precision, real-time safety warning applications. To address these challenges, this study proposes a unified visual analysis framework that integrates semantic segmentation and object detection to enhance the recognition performance of small and deformable targets in complex operational environments, enabling real-time perception and safety warning of key objects and hazardous regions within container yards. Specifically, we introduce FSD-YOLO, a fusion-based architecture composed of the following key components. First, a SegFormer-based semantic segmentation module is employed to achieve pixel-level delineation of different operational regions. Second, an improved object detection network is developed based on the YOLOv8n architecture, incorporating: (1) the integration of C2f modules in the shallow layers of the backbone to enhance high-resolution feature extraction; (2) the embedding of C2fDCN modules within the detection head to improve modeling capability for deformable objects via deformable convolution; (3) the adoption of CARAFE upsampling operators to optimize multi-scale feature fusion; and (4) a dynamic loss-weighting strategy for small objects, where loss weights are adaptively adjusted according to target area to increase training emphasis on small-scale targets. Finally, a decision-level fusion strategy is applied to combine segmentation and detection outputs, enabling real-time safety judgment based on semantic rules. Experimental results on a self-constructed container yard dataset demonstrate that the proposed detection model achieves an mAP50-95 of 0.6433 and an mAP50 of 0.9565, significantly outperforming the baseline YOLOv8n model (mAP50-95: 0.5394, mAP50: 0.8435), thereby validating the effectiveness of the proposed framework. Full article

Disaster resilience has become a key focus of risk reduction efforts, but measuring it remains complex due to differences in hazards, development paths, and data systems. This study modifies the Baseline Resilience Indicators for Communities (BRIC) approach, based on the Disaster Resilience of Place (DROP) framework, to evaluate community resilience in Serbia and highlight regional differences. An initial list of 186 indicators was created from international BRIC studies and resilience research, then tailored to Serbian conditions through contextual review and data checks. Indicators were normalized using min–max scaling (0–1), and indicators with negative orientation were inverted to ensure that higher values indicate greater resilience. Scores for each dimension were calculated as equally weighted averages across six areas: social, economic, social capital, institutional, infrastructural, and environmental. The overall BRIC index was derived as the average of these dimension scores. Z-scores facilitated the classification of resilience levels and the comparison between regions. The results show clear regional disparities: in the complete model, Belgrade has the highest resilience (BRIC = 0.557), while Southern and Eastern Serbia have the lowest (BRIC = 0.414). Patterns across dimensions show that Belgrade excels in social and economic capacity but lags in environmental indicators; Vojvodina has the strongest institutional and infrastructural capacity; and Šumadija and Western Serbia perform best in environmental indicators. Correlation analysis revealed multicollinearity, leading to the removal of 14 redundant indicators and the refinement to a set of 57. After this reduction, regional rankings change, with Vojvodina (BRIC = 0.530) and Šumadija and Western Serbia (BRIC = 0.522) emerging as higher-resilience regions, while Southern and Eastern Serbia remain the least resilient (BRIC = 0.456). The adapted BRIC-DROP model offers a clear, locally relevant tool for mapping resilience and guiding targeted policies in Serbia, enabling region-specific efforts to address structural resilience gaps. Full article

Due to the rapid process of urbanization and the threat of environmental hazards, the need to enhance the intelligence and resilience of urban infrastructure has emerged as a pre-eminent demand of sustainable urban development. This paper evaluates the smart-resilience of urban infrastructure in Kunming by creating a well-developed evaluation framework with reference to the DPSIR (Driving Force–Pressure–State–Impact–Response) model and using the Entropy Weight TOPSIS technique to measure infrastructure performance during the years 2020–2024. The study fills an existing gap in the literature regarding the integration of intelligence and resilience evaluation, as well as the dynamic obstacle diagnosis based on causal logic. It provides a transferable analytical framework and empirical evidence for the “smart-resilience” development of similar cities. The findings suggest that there is steady progress in infrastructure smart-resilience in Kunming, whereby the composite index grew from 0.330 to 0.597, which is equivalent to an average growth rate of about 16.0 per annum. In spite of this favorable tendency, there are a number of structural issues that remain unsolved. The driving force dimension is unstable with regard to long-term mechanisms of investment, and the responding dimension is lagging behind, indicating weaknesses in the governance capacity and inter-departmental coordination. Moreover, extreme weather events have become the major threat to infrastructure systems in the city, superseding traditional social and operational risks; consequently, the city has changed its risk profile. Obstacle factor analysis shows that state and response dimensions make up almost 60% of the total constraint level, which shows the significance of enhancing the effectiveness of management. The research findings are based on the proposal of specific policy actions, such as the creation of special infrastructure resilience funds, the enhancement of mechanisms relating to cross-departmental emergency responses, the implementation of risk-based engineering standards, and the creation of an integrated infrastructure data platform to facilitate efficient, resilient, and sustainable urban governance. Full article