Search Results (55,448)

Background/Objectives: MSS-CRC comprises a heterogeneous group of tumors generally considered “immune cold” due to limited neoantigen generation and T-cell exclusion or inactivation. Current evidence indicates that the composition of T and B immune cells within the tumor microenvironment represents a prognostically relevant factor, significantly associated with both tumor expression profiles and molecular subtypes. Methods: We conducted an exploratory analysis to identify prognostically relevant immune cell components in this group of tumors and to investigate corresponding differences in RNA-based bulk expression and high-resolution spatial transcriptomic profiles. Results: A total of 254 localized mismatch repair-proficient colorectal cancer cases were evaluated. Our findings revealed PD-L1 expression as a robust independent prognostic biomarker associated with favorable outcomes in this specific population. Bulk RNA expression analysis showed that PD-L1-negative tumors exhibited an expression profile consistent with abundant cancer-associated fibroblast infiltration, increased matrix stiffness, and impaired immune activation—features consistent with tumor progression and poorer clinical outcomes. In contrast, PD-L1-positive tumors displayed stromal programs enriched in immune activation and controlled remodeling, consistent with an immunologically active microenvironment. Spatial transcriptomics added an additional layer of evidence, revealing that epithelial to mesenchymal transition-related programs can dominate stromal niches in PD-L1-negative tumors, particularly within macrophage-enriched stromal regions. Conclusions: Our observations suggest an association between PD-L1 expression on immune cells and immune-activated versus mesenchymal-dominant states, potentially occurring within macrophage-enriched stromal niches. These results provide insight into the biological mechanisms underlying disease progression and highlight tumor-associated macrophages as a potential therapeutic target to overcome immune resistance, particularly in PD-L1-negative MSS-CRC tumors. Full article

Adaptive reuse of industrial heritage from a tourism perspective presents a complex design challenge requiring a balance between heritage preservation, functional innovation, and diverse stakeholder expectations. However, current practices often face issues such as ambiguous demand interpretation and a disconnect between design generation and systematic evaluation. Addressing these limitations, this paper proposes and illustrates a human–machine collaborative design paradigm that integrates generative AI into a closed-loop process of “demand analysis–intelligent generation–comprehensive evaluation.” The method first employs Kansei Engineering and the KANO model to qualitatively extract and quantitatively prioritise heterogeneous user needs, translating subjective perceptions into structured design constraints and optimisation objectives. Next, these needs are encoded as text prompts to drive targeted spatial exploration by the generative AI tool Nano Banana AI. Finally, the TOPSIS method is applied for multi-criteria performance evaluation and solution selection. A case study of Shanghai Libo Brewery suggests that this paradigm can enhance design efficiency and show potential to outperform traditional methods across dimensions such as historical preservation, public accessibility, ecological integration, social inclusivity, and formal innovation. The research offers a quantifiable and systematically documented intelligent design methodology for industrial heritage renewal, while acknowledging the exploratory nature of the generative phase. Furthermore, it provides a visitor-demand-driven innovation pathway for developing industrial heritage tourism destinations, thereby potentially enhancing cultural experiences and tourism appeal at heritage sites. This research illustrates a move from an experience-driven paradigm toward a data- and value-driven approach, contributing theoretical methodologies to the intersection of cultural tourism and artificial intelligence. Full article

Endophytic fungi (EF) inhabit internal plant tissue in a mutually beneficial symbiotic relationship with their host plant. EF synthesizes metabolites that are structurally similar or identical to those found in their host plants, which include alkaloids, flavonoids, terpenoids, phenolic compounds, polysaccharides, proteins, lipids, and organic acids. These molecules have promising therapeutic effects, such as antimicrobial, antioxidant, anti-inflammatory, and antitumor activities. Wound healing has earned attention in recent years because of its relation to chronic pathological diseases. This systematic review scanned the available scientific literature database about the wound-healing properties of EF biomolecules. Amongst 994 works, 24 were screened after abstract and full-text reading. The studies were published between 2014 and 2026, in twelve countries. In total, 16 studies presented in vivo assays, 11 studies presented in vitro assays, and 3 studies presented both assays. Most studies identified molecules, which include melanin, benzoic acid, terpenes, sesquiterpenes (purpurolide), extracellular polysaccharides, exopolysaccharides, carotenoids, fatty acids, proteins, pyrones, quinones, and hydrocarbon acids, among others. A meta-analysis was not conducted due to high heterogeneity across extracts, methodologies, and outcomes. All studies showed wound-healing properties from EF extracts. The findings suggest a positive effect of EF extracts on wound-healing properties and the need for standardized in vitro and in vivo protocols. Full article

Accurate and scalable wetland mapping remains challenging due to strong spatial heterogeneity and limited availability of reference data. Spatial transferability of classification algorithms offers a promising solution by enabling models trained in one region to be applied to other sites, but its effectiveness depends on the degree of domain shift, algorithm robustness, and data representation. In this study, we evaluate this ability for wetland mapping using multitemporal Sentinel-2 data across two wetland systems in France: the Camargue and the Étangs de la Champagne humide. Classification is performed for three main land-cover classes—open water, aquatic vegetation, and terrestrial vegetation—using one neural network (MLP), one deep-learning model (InceptionTime), and two machine-learning algorithms (Random Forest and XGBoost), and three feature configurations (spectral bands, spectral indices, and their combination). Results reveal that when models are trained on Camargue and applied to Champagne, the highest OA reaches 90% (using InceptionTime and XGBoost), when models are trained on Champagne and applied to Camargue, the highest OA reaches 84% (using InceptionTime and XGBoost), corresponding to a decrease of 6% in OA. Within the selected algorithms, InceptionTime and XGBoost achieve the highest OA in both transfer directions. Combining spectral bands and indices improves classification performance of InceptionTime and MLP by up to 8%, while XGBoost and RF perform better using band data (5% higher OA than the combination). Class-wise analysis highlights substantial differences in transferability. Terrestrial vegetation shows the highest and most stable performance across the tested configurations, with F1-scores up to 92%, followed by open water (F1 up to 88%), while aquatic vegetation remains the most challenging class to transfer, with F1-scores up to 85% depending on algorithm and configuration. Annual time series benefit aquatic vegetation, whereas shorter series covering only the vegetation growing season remain sufficient for more stable LC classes (terrestrial vegetation). InceptionTime and MLP show higher performance using annual time series, while RF and XGBoost perform better using short time series. Overall, these results highlight the potential of spatial transferability for wetland mapping within the context of the two studied sites, although further validation across a broader range of wetlands is required. Full article

Thermochemical/catalytic co-processing of biomass and solid wastes is a promising route for waste valorization, low-carbon energy recovery, and the co-production of fuels, chemicals, and carbon materials. Conventional pathways, including pyrolysis, gasification, liquefaction, and carbonization, provide the basic framework for mixed-feed conversion. Emerging routes, such as flash Joule heating, microwave-assisted conversion, plasma processing, supercritical water treatment, solar-driven systems, and machine-learning-assisted optimization, further expand opportunities for process intensification and selective upgrading. Owing to feedstock complementarity, including hydrogen donation from plastics, catalytic effects of ash minerals, and interactions among reactive intermediates, co-processing can enhance deoxygenation, hydrogen generation, aromatization, and carbon utilization. Major challenges remain, however, including feedstock heterogeneity, reactor scale-up, catalyst stability, and the limited transferability of laboratory-scale synergy to realistic waste streams. Future progress should therefore focus on continuous validation, mechanistic clarification, and integrated techno-economic, life-cycle, and data-driven assessments. Full article

Anti-A and anti-B antibodies are essential for monitoring adverse reactions in organ transplants and transfusions. However, their importance is also growing due to their involvement in the pathophysiology of various human diseases, such as infections, although this is currently the subject of heated debate. A characteristic heterogeneity in the titers and classes of anti-A and anti-B antibodies is observed among individuals. Several factors appear to be responsible, such as everyone’s specific immune profile, age, sex, microbiota composition, lifestyle, and health status. The immune profile, the result of a specific genetic predisposition and mediated and controlled by cytokines, shows a bidirectional relationship with ABO antigen expression, the gut microbiota, and the levels and class switching of anti-ABO antibodies. Associations between ABO groups and circulating levels of cytokines and chemokines further highlight this complex interaction. To better understand the role of the immune profile in this context, we evaluated, for the first time, the possible association between polymorphic variants in the regulatory regions of the genes encoding the cytokines IL-8, IL-1, IL-4, IL-6, IFN-γ, and IL-10 and anti-A and anti-B antibody titers and classes by group and in total. We also assessed the levels of these cytokines in each group and their correlations with anti-A and anti-B antibodies, as well as with age and associations with gender. Significant data were obtained that may contribute to a better understanding of the other roles of ABO antibody titers. Full article

The geological environment of southeastern Tibet is characterized by complex tectonics and high climatic sensitivity, and giant accumulation landslides pose significant threats to infrastructure and human safety. This study investigates the Pangcun giant accumulation landslide using SBAS-InSAR (2017–2024), UAV photogrammetry, field investigations, and wavelet coherence analysis to examine its deformation and driving mechanisms. The landslide exhibits continuous, slow deformation with clear spatial heterogeneity, divided into two zones, with the largest displacement occurring in the middle of Zone B. Field evidence is consistent with the InSAR results. Wavelet coherence analysis reveals a lagged response of displacement to precipitation at a timescale of about three months. The landslide’s evolution is controlled by unfavorable topography and fragmented materials, with precipitation as the primary trigger. Human activities (agricultural irrigation and slope-toe road excavation) and seismic disturbances also contribute to its progressive development. Full article

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R² of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R² of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs. Full article

Background: Total hip arthroplasty (THA) for the late sequelae of childhood septic hip arthritis is technically demanding, and infection-related risk remains incompletely defined. This systematic review and meta-analysis address the research question: “In adults undergoing THA after childhood septic arthritis of the hip, what is the incidence of post-THA infection, revision, and mechanical/neurologic complications?” We systematically reviewed and meta-analyzed outcomes after THA in patients with septic hip arthritis diagnosed at ≤18 years. Methods: PubMed, Web of Science, Scopus, and the Cochrane Library were searched from inception to 31 December 2025 (PRISMA). Eligible studies reported THA outcomes after childhood septic arthritis and met a Methodological Index for Non-Randomized Studies (MINORS) threshold (≥9). A random-effects meta-analysis of events per hip was performed. Results: Nine studies were included; eight contributed to the quantitative synthesis (343 hips). The pooled incidence of any post-THA infection was 1.55% (95% CI 0.38–3.48; I² = 23.8%; 5/343); when microbiology was available, no relapse due to the index organism was reported and events were classified as new infections. The pooled incidence of revision for any cause was 4.99% (95% CI 2.27–8.70; I² = 43.4%; 15/334). Non-infectious complications were clinically relevant, including intraoperative fracture (6.95%) and nerve palsy (4.84%). Evidence was limited by retrospective designs and heterogeneous reporting. Conclusions: THA after childhood septic hip arthritis demonstrates a low risk of postoperative infection, with relapse of the original pathogen appearing rare in carefully selected quiescent cases, but a clinically meaningful burden of mechanical and neurologic complications. These findings underscore the importance of careful preoperative assessment, meticulous surgical technique, and highlight the limitations of the current evidence. The protocol was registered in PROSPERO (ID: CRD420261298181). No external funding was received. Full article

Nonobstructive coronary artery disease (NOCAD) is increasingly recognized as a heterogeneous condition characterized by diverse pathophysiological mechanisms despite the absence of flow-limiting stenosis. We sought to establish a rule-based dominant imaging phenotype framework integrating functional, structural, and inflammatory dimensions derived from multiparametric coronary computed tomography angiography (CCTA). In this retrospective cohort of 485 patients with NOCAD, CT-derived fractional flow reserve (CT-FFR), quantitative plaque burden and high-risk plaque features, and perivascular fat attenuation index (FAI) were assessed. Using predefined percentile thresholds and hierarchical rules, patients were categorized into function-, structure-, inflammation-dominant, or low-risk phenotypes. During a median follow-up of 36 months, 56 patients (11.5%) experienced major adverse cardiovascular events (MACE). After multivariable adjustment, function dominance was associated with the highest risk (hazard ratio [HR] 4.054, 95% confidence interval [CI] 1.984–8.281; p < 0.001), followed by structure dominance (HR 3.129, 95% CI 1.410–6.944; p = 0.005), whereas isolated inflammation dominance did not show a statistically significant independent association with events, with wide confidence intervals indicating limited precision. These findings suggest a graded pattern of prognostic associations across functional and structural abnormalities in NOCAD and support a phenotype-oriented interpretation of CCTA metrics reflecting distinct biological axes of coronary pathology. Full article

Background/Objectives: Nutrition is essential to outcomes in critically ill children; however, optimal timing, route, and composition of feeding remain uncertain. Prior studies demonstrate considerable variability in study design, patient populations, and outcome measures, limiting comparability. This review synthesizes international pediatric intensive care unit (PICU) nutrition studies evaluating timing, route, and content of nutritional interventions and summarizes associated clinical outcomes and nutritional adequacy. Methods: A comprehensive scoping review was conducted using the PICOS framework. PubMed and Embase databases were searched for studies published between 2015 and 2025 enrolling critically ill children ≤21 years old admitted to PICUs. Eligible studies assessed timing (early vs. late enteral nutrition), nutritional composition, or feeding route (enteral vs. parenteral). Screening and full-text review were performed independently by two reviewers using Covidence, with discrepancies resolved by a third reviewer. Quality assessment used STROBE. The protocol was registered with PROSPERO. Results: Of 652 identified records, 30 studies met inclusion criteria. Studies were conducted primarily in the United States (27%), with additional contributions from Spain and Brazil (10% each) and several other countries. Study designs included randomized controlled trials (27%) and observational studies (73%). Interventions examined feeding route (14%), nutritional content (38%), and timing (48%). Frequently reported outcomes included feeding intolerance or adverse events, duration of mechanical ventilation, time to nutrition goals, PICU length of stay, mortality, and nutritional adequacy. Conclusions: The contemporary PICU nutrition literature demonstrates persistent heterogeneity in practice and outcomes. This review identifies ongoing gaps in timing, delivery, and adequacy of nutritional support. Full article

When the data at hand are suspected to stem from several latent subpopulations, Statisticians commonly speak of “unobserved heterogeneity”. While the presence and importance of this phenomenon is commonly acknowledged, there is relatively little guidance on how to carry out correct inferences under unobserved heterogeneities, even in relatively simple scenarios such as the linear regression model. In this work, bootstrap algorithms for the computation of standard errors are investigated in the context of a mixture-based regression approach which accounts for the clustered nature of the data. Of interest is both the accuracy of the standard errors (evidenced by confidence interval coverage rates) and the relative reduction in standard errors achieved in comparison to a naïve linear model fit. Simulations and a real data example are provided. Full article

Bioeconomy policy requires timely, economy-wide evidence; however, two persistent measurement constraints remain: official input–output (IO) tables are published with time lags, novel start-up and novel prospective or hybrid bio-based activities are rarely identified as separate sectors in national accounts. This study develops an applied framework that combines IO nowcasting with an accounting-consistent sector-embedding procedure under limited data availability. Using Ireland’s national IO system and an existing bioeconomy IO framework as the accounting backbone, we update the 2015 table to 2022 through calibration to macroeconomic control totals, providing a timely structural baseline. We then introduce a transparent method for constructing new bioeconomy sectors based on dominant input shares, import intensity, and output allocation, while preserving national accounting identities. The approach is demonstrated for aquaculture systems, anaerobic digestion scenarios, and plant-based protein value chains. Demand-driven Leontief multipliers reveal heterogeneity in domestic propagation effects across activities and development stages. The framework offers a resource-efficient and replicable tool for evaluating bioeconomy strategies under real-world data constraints. The paper finds that the bioeconomy is structurally heterogeneous rather than a single uniform sector. Aquaculture is strongly transport- and service-linked, anaerobic digestion is more manufacturing-oriented, and plant-based protein production combines agricultural and industrial inputs while showing relatively high import dependence. Full article

The increasing pressure on natural resources and the environment has intensified the need for sustainable cascade utilization in closed-loop supply chains (CLSCs). This study develops a game-theoretic framework to examine cascade utilization under both constant and heterogeneous quality restoration costs across three collection structures: centralized, manufacturer-led, and third-party collection. The results show that the relative performance of different structures depends on key economic conditions, including material recycling revenue and the comparative advantage of remanufacturing. No single structure dominates across all dimensions: a manufacturer-led collection tends to promote new product sales, while a third-party collection enhances remanufacturing and recovery levels, particularly under cost heterogeneity. Environmental performance, evaluated through collection quantity, cascade utilization efficiency, and an environmental impact indicator, also varies across structures, with cost heterogeneity shifting advantages toward the third-party collection. Policy analysis further indicates that both collection and remanufacturing subsidies increase recovery volumes but operate through distinct mechanisms. The collection subsidy expands return flows but may reduce cascade utilization efficiency by directing more low-quality products to recycling, whereas remanufacturing subsidy promotes higher-value reuse pathways and improves environmental performance. These findings highlight the importance of aligning collection structures and policy instruments under different cost conditions to enhance resource efficiency and support the circular economy and sustainable consumption and production objectives. Full article

To achieve the large-scale, high-value utilization of vanadium–titanium slag (VTS) in the metallurgical industry, this study replaces blast furnace slag (BFS) with VTS to construct a quaternary all-solid-waste cementitious system composed of VTS, BFS, steel slag (SS), and desulfurization gypsum (DG). It systematically investigates the effects of VTS content (0–60%) on the mechanical properties, leaching toxicity, and hydration heat behavior of the system. XRD, TG–DSC, and SEM–EDS techniques are employed to explore the influence of VTS on hydration behavior and microstructural evolution. The results show that when VTS replaces 30% of the BFS (A3, VTS:BFS:SS:DG = 3:3:3:1), the 28-day compressive strength reaches 31.33 MPa. The leaching concentrations of heavy metals in all specimens are far below the standards for drinking water quality. Hydration heat analysis reveals that the incorporation of VTS advances the acceleration period of hydration. The A3 specimen maintains a relatively high heat release rate in the middle and later stages (after 72 h), and its cumulative heat release is significantly higher than that of the system without VTS, revealing the “slow hydration” mechanism of VTS at later stages. The [SiO₄]–[AlO₄] bonds in VTS undergo a depolymerization–repolymerization process. In addition, an appropriate amount of VTS promotes the deposition of hydration products such as ettringite (AFt), C–S–H, and C–A–S–H gels through micro-filling effects and heterogeneous nucleation, thereby improving the microstructure of the system. However, excessive VTS (≥45%) significantly inhibits the hydration reaction and reduces gel formation due to the decrease in highly reactive BFS components and the increased TiO₂ content. This study provides new insights into the resource utilization of VTS in multi-solid-waste cementitious materials. In addition, VTS-based cementitious materials are suitable for practical scenarios with low early strength requirements, such as goaf backfilling. Therefore, future studies should further investigate the long-term sulfate resistance and carbonation resistance of these materials under real application conditions. Full article