Search Results (826)

Gastrointestinal (GI) cancers represent a significant global health burden, with high morbidity and mortality often linked to late-stage detection and metastatic disease. The progression of these malignancies is critically driven by angiogenesis, the formation of new blood vessels, and the surrounding dynamic tumor microenvironment (TME), a complex ecosystem comprising various cell types and non-cellular components. This comprehensive review, based on a systematic search of the PubMed database, synthesizes the existing literature to define the intertwined roles of angiogenesis and the TME in GI tumorigenesis. The TME’s influence creates conditions favorable for tumor growth, invasion, and metastasis, but sometimes induces resistance to current therapies. Available therapeutic strategies for inhibiting angiogenesis involve antibodies and oral tyrosine kinase inhibitors, while immune modulation within the tumor microenvironment is mainly achieved through checkpoint inhibitor antibodies and chemotherapy. Creative emerging strategies encompassing cellular therapies, bispecific antibodies, and new targets such as CD40, DLL4, and Ang2, amongst others, are focused on inhibiting proangiogenic pathways more profoundly, reversing resistance to prior drugs, and modulating the TME to enhance therapeutic efficacy. A deeper understanding of the complex interactions between components of the TME is crucial for addressing the unmet need for novel and effective therapeutic interventions against aggressive GI cancers. Full article

Glycyrrhiza uralensis (G. uralensis), a leguminous plant, is an important medicinal and economic plant in saline–alkaline soils of arid regions in China. Its main bioactive components include liquiritin, glycyrrhizic acid, and flavonoids, which play significant roles in maintaining human health and preventing and adjuvantly treating related diseases. However, the cultivation of G. uralensis is easily restricted by adverse soil conditions in these regions, characterized by high salinity, high alkalinity, and nutrient deficiency. This study investigated the impacts of four multistrain microbial inoculants (Pa, Pb, Pc, Pd) on the growth performance and bioactive compound accumulation of G. uralensis in moderately saline–sodic soil. The aim was to screen the most beneficial inoculant from these strains, which were isolated from the rhizosphere of plants in moderately saline–alkaline soils of the Hexi Corridor and possess native advantages with excellent adaptability to arid environments. The results showed that inoculant Pc, comprising Pseudomonas silesiensis, Arthrobacter sp. GCG3, and Rhizobium sp. DG1, exhibited superior performance: it induced a 0.86-unit reduction in lateral root number relative to the control, while promoting significant increases in single-plant dry weight (101.70%), single-plant liquiritin (177.93%), single-plant glycyrrhizic acid (106.10%), and single-plant total flavonoids (107.64%). Application of the composite microbial inoculant Pc induced no significant changes in the pH and soluble salt content of G. uralensis rhizospheric soils. However, it promoted root utilization of soil organic matter and nitrate, while significantly increasing the contents of available potassium and available phosphorus in the rhizosphere. High-throughput sequencing revealed that Pc reorganized the rhizospheric microbial communities of G. uralensis, inducing pronounced shifts in the relative abundances of rhizospheric bacteria and fungi, leading to significant enrichment of target bacterial genera (Arthrobacter, Pseudomonas, Rhizobium), concomitant suppression of pathogenic fungi, and proliferation of beneficial fungi (Mortierella, Cladosporium). Correlation analyses showed that these microbial shifts were linked to improved plant nutrition and secondary metabolite biosynthesis. This study highlights Pc as a sustainable strategy to enhance G. uralensis yield and medicinal quality in saline–alkali ecosystems by mediating microbe–plant–nutrient interactions. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

Hydrogen production is increasingly vital for global decarbonization but remains a water- and energy-intensive process, especially in arid regions. Despite growing attention to its climate benefits, limited research has addressed the environmental impacts of water sourcing. This study employs a life cycle assessment (LCA) approach to evaluate three water supply strategies for hydrogen production: (1) seawater desalination without brine treatment (BT), (2) desalination with partial BT, and (3) freshwater purification. Scenarios are modeled for the United Arab Emirates (UAE), Australia, and Spain, representing diverse electricity mixes and water stress conditions. Both electrolysis and steam methane reforming (SMR) are evaluated as hydrogen production methods. Results show that desalination scenarios contribute substantially to human health and ecosystem impacts due to high energy use and brine discharge. Although partial BT aims to reduce direct marine discharge impacts, its substantial energy demand can offset these benefits by increasing other environmental burdens, such as marine eutrophication, especially in regions reliant on carbon-intensive electricity grids. Freshwater scenarios offer lower environmental impact overall but raise water availability concerns. Across all regions, feedwater for SMR shows nearly 50% lower impacts than for electrolysis. This study focuses solely on the environmental impacts associated with water sourcing and treatment for hydrogen production, excluding the downstream impacts of the hydrogen generation process itself. This study highlights the trade-offs between water sourcing, brine treatment, and freshwater purification for hydrogen production, offering insights for optimizing sustainable hydrogen systems in water-stressed regions. Full article

It is crucial to clarify the grassland carrying capacity (CC) and the balance between grass and livestock under different scenarios for ecological protection and sustainable development in the Three River Headwaters Region (TRHR). This study focused on the TRHR and used livestock data, MODIS Net Primary Productivity (NPP) data, and artificial supplementary feeding data to analyze grassland CC and explore changes in the grass–livestock balance across various scenarios. The results showed that the theoretical CC of edible forage under complete grazing conditions was much lower than that of crude protein under nutritional carrying conditions. Furthermore, without increasing the grazing intensity of natural grasslands, artificial supplementary feeding reduced overstocking areas by 21%. These results suggest that supplementary feeding effectively addresses the imbalance between forage supply and demand, serving as a key measure for achieving sustainable grassland livestock husbandry. Despite the effective mitigation of grassland degradation in the TRHR due to strict grass–livestock balance policies and ecological restoration projects, the actual livestock CC exceeded the theoretical capacity, leading to overgrazing in some areas. To achieve desired objectives, more effective grassland management strategies must be implemented in the future to minimize spatiotemporal conflicts between grasses and livestock and ensure the health and stability of grassland ecosystems. Full article

Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km² on 9 September 2024 and a low of 0.00068 km² on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R² = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article

Coastal ecosystems are increasingly threatened by climate change and anthropogenic pressures, which can disrupt microbial communities and favor the emergence of pathogenic organisms. In this study, we applied metagenomic analysis to characterize fungal communities in sediment samples from an urban mangrove subjected to environmental stress. The results revealed a fungal community with reduced richness—28% lower than expected for similar ecosystems—likely linked to physicochemical changes such as heavy metal accumulation, acidic pH, and eutrophication, all typical of urbanized coastal areas. Notably, we detected an increase in potentially pathogenic genera, including Candida, Aspergillus, and Pseudoascochyta, alongside a decrease in key saprotrophic genera such as Fusarium and Thelebolus, indicating a shift in ecological function. The fungal assemblage was dominated by the phyla Ascomycota and Basidiomycota, and despite adverse conditions, symbiotic mycorrhizal fungi remained present, suggesting partial resilience. A considerable fraction of unclassified fungal taxa also points to underexplored microbial diversity with potential ecological or health significance. Importantly, this study does not aim to compare pristine and contaminated environments, but rather to provide a sanitary alert by identifying the presence and potential proliferation of pathogenic fungi in a degraded mangrove system. These findings highlight the sensitivity of mangrove fungal communities to environmental disturbance and reinforce the value of metagenomic approaches for monitoring ecosystem health. Incorporating fungal metagenomic surveillance into environmental management strategies is essential to better understand biodiversity loss, ecological resilience, and potential public health risks in degraded coastal environments. Full article

Antibiotic resistance genes (ARGs) in suburban freshwater ecosystems pose a growing public health concern by potentially reducing the effectiveness of medical treatments. This study investigated how rainfall influences ARG dynamics in Lake Katherine, a 62-hectare suburban lake in Columbia, South Carolina, over one year. Surface water was collected under both dry and post-rain conditions from three locations, and ARGs were identified using metagenomic sequencing. Statistical models revealed that six of nine ARG classes with sufficient data showed significant responses to rainfall. Three classes, Bacitracin, Aminoglycoside, and Unclassified, were more abundant after rainfall, while Tetracycline, Multidrug, and Peptide resistance genes declined. Taxonomic analysis showed that members of the Pseudomonadota phylum, especially Betaproteobacteria, were prevalent among ARG-carrying microbes. These findings suggest that rainfall can alter the distribution of ARGs in suburban lakes, highlighting the importance of routine monitoring and water management strategies to limit the environmental spread of antibiotic resistance. Full article

Integrated rice–crayfish (Oryza sativa–Procambarus clarkii) co-culture (RC) systems have gained prominence due to their economic benefits and ecological sustainability; however, the interactions between soil properties and microbial communities in such systems remain poorly understood. This study evaluated the effects of the RC systems on soil physicochemical characteristics and microbial dynamics in paddy fields of southern Henan Province, China, over the 2023 growing season and subsequent fallow period. Using a randomized complete design, rice monoculture (RM, as the control) and RC treatments were compared across replicated plots. Soil and water samples were collected post-harvest and pre-transplanting to assess soil properties, extracellular enzyme activity, and microbial community structure. Results showed that RC significantly enhanced soil moisture by up to 30.2%, increased soil porosity by 9.6%, and nearly tripled soil organic carbon compared to RM. The RC system consistently elevated nitrogen (N), phosphorus (P), and potassium (K) throughout both the rice growth and fallow stages, indicating improved nutrient availability and retention. Elevated extracellular enzyme activities linked to carbon, N, and P cycling were observed under RC, with enzymatic stoichiometry revealing increased microbial nutrient limitation intensity and a shift toward P limitation. Microbial community composition was significantly altered under RC, showing increased biomass, a higher fungi-to-bacteria ratio, and greater relative abundance of Gram-positive bacteria, reflecting enhanced soil biodiversity and ecosystem resilience. Further analyses using the Mantel test and Random Forest identified extracellular enzyme activities, PLFAs, soil moisture, and bulk density as major factors shaping microbial communities. Redundancy analysis (RDA) confirmed that total potassium (TK), vector length (VL), soil pH, and total nitrogen (TN) were the strongest environmental predictors of microbial variation, jointly explaining 74.57% of the total variation. Our findings indicated that RC improves soil physicochemical conditions and microbial function, thereby supporting sustainable nutrient cycling and offering a promising, environmentally sound strategy for enhancing productivity and soil health in rice-based agro-ecosystems. Full article

Turbidity is a crucial parameter for assessing the ecological health of aquatic ecosystems, particularly in shallow tropical lakes that are subject to climatic variability and anthropogenic pressures. Lake Chapala, the largest freshwater body in Mexico, has experienced persistent turbidity and sediment influx since the 1970s, primarily due to upstream erosion and reduced water inflow. In this study, we utilized Landsat satellite imagery in conjunction with near-synchronous in situ reflectance measurements to monitor spatial and seasonal turbidity patterns between 2023 and 2025. The surface reflectance was radiometrically corrected and validated using spectroradiometer data collected across eight sampling sites in the eastern sector of the lake, the area where the highest rates of horizontal change in turbidity occur. Based on the relationship between near-infrared reflectance and field turbidity, second-order polynomial models were developed for spring, fall, and the composite annual model. The annual model demonstrated acceptable performance (R² = 0.72), effectively capturing the spatial variability and temporal dynamics of the average annual turbidity for the whole lake. Historical turbidity data (2000–2018) and a particular case study in 2016 were used as a reference for statistical validation, confirming the model’s applicability under varying hydrological conditions. Our findings underscore the utility of empirical remote-sensing models, supported by field validation, for cost-effective and scalable turbidity monitoring in dynamic tropical lakes with limited monitoring infrastructure. Full article

Host genetic variability is relevant to understanding how parasites modulate natural selection in wild fish populations. Coastal lagoons are transitional ecosystems where knowledge lacks on relationships between genotypic diversity with parasitism. The aim of this study was to assess the effect of genetic diversity on host health and parasitological traits in fish inhabiting a Mediterranean lagoon. Black-striped pipefish Syngnathus abaster were collected in August 2023 and 2024 from the Mar Menor (Iberian lagoon, SE Spain). Genetic diversity was measured as Internal Relatedness (IR: a homozygosity index from microsatellite markers). Population frequency was lower for the medium IR level. For this same category, both health indices (external body condition and internal organs) indicated a worse status. Parasite prevalence, abundance and an index of life-cycle complexity (heteroxenous species) were greater for the medium level of genetic diversity. Such results are explained under a scenario of parasite-mediated disruptive selection: a higher disease pressure against the phenotypically intermediate individuals. Two contrasting strategies were detected to better control parasitism at the host genotypic level: (1) high homozygosity, and (2) high heterozygosity, which probably reflects better immuno-competence as a phenotypic trait. From an evolutionary perspective, parasites play a crucial role in shaping genetic diversity within host populations. Full article

Modern software solutions have a multiplicative effect on enhancing quality of life across various urban sectors, including the environment, education, public health, security, transportation, time efficiency, employment, and other key aspects of city living. This article addresses a specific issue concerning the organisation of leisure activities for both local residents and tourists, using the Chechen Republic as a case study. In response, the study aimed to develop a digital solution to address this challenge, with potential for integration into the Republic’s unified digital ecosystem. By employing system analysis methods, the authors identified the key objects and stakeholders involved in the problem domain. They also defined the software product’s functionality and classified user categories. Using Unified Modelling Language methods, a use case diagram was developed to illustrate the conceptual operation of the system. Furthermore, object-oriented design methods were applied to create a user interface prototype for the software product. As a result, a digital service was developed that enables users to create personalised leisure routes, taking into account individual goals, time constraints, traffic conditions, and the real-time status of urban infrastructure. The resulting software solution is both customisable and scalable. The article also presents selected examples of project development. Full article

Soil bacteria drive biogeochemical cycles and influence disease suppression, playing pivotal roles in sustainable agriculture. Using Illumina MiSeq sequencing, we assessed how six ridge-furrow film mulching patterns affect soil bacterial diversity in a continuous potato system. The Shannon index showed significantly higher diversity in fully mulched treatments (T2–T3) versus controls (CK), suggesting mulching enhances microbial community richness. This result suggests that complete mulching combined with ridge planting (T2) may significantly enhance bacterial proliferation in soil. The bacterial communities were predominantly composed of Acidobacteria, Pseudomonadota, Bacteroidota, Chloroflexota, and Planctomycetota. Among these, Acidobacteria showed the highest abundance, with ridge planting patterns favoring greater Acidobacteria richness compared to furrow planting. In contrast, Pseudomonadota exhibited higher abundance under half-mulching conditions than under complete mulching. At class level, Acidobacteria and Proteobacteria emerged as the most abundant groups, with Proteobacteria constituting 22.6–35.7% of total microbial populations. Notably, Proteobacteria demonstrated particular dominance under the complete mulching with ridge planting pattern (T2). At the genus level, Subgroup_6_norank represented the most dominant taxon among the 439 identified bacterial genera, accounting for 14.0–20.2% of communities across all treatments, with half-mulching ridge planting (T4) showing the highest relative abundance. Our findings demonstrate that different ridge-furrow film mulching patterns significantly influence soil microbial diversity. While traditional non-mulched (CK) and mulched flat plots (T1) exhibited similar impacts on bacterial community structure, other treatments displayed distinct taxonomic profiles. Complete mulching patterns, particularly ridge planting (T2), appear most conducive to microbial development, suggesting their potential to enhance soil biogeochemical cycling in continuous cropping systems. These results provide valuable insights for optimizing mulching practices to improve soil health in agricultural ecosystems. Full article

Honey bees play a vital role in ecosystem stability, and the need to monitor colony health has driven the development of IoT-based systems in beekeeping, with recent studies exploring both empirical and machine learning approaches to detect and analyze key hive conditions. In this study, we present an IoT-based system that leverages sensors to record and analyze the acoustic signals produced within a beehive. The captured audio data is transmitted to the cloud, where it is converted into mel-spectrogram representations for analysis. We explore multiple data pre-processing strategies and machine learning (ML) models, assessing their effectiveness in classifying queenless states. To evaluate model generalization, we apply transfer learning (TL) techniques across datasets collected from different hives. Additionally, we implement the feature extraction process and deploy the pre-trained ML model on a deep edge IoT device (Arduino Zero). We examine both memory consumption and execution time. The results indicate that the selected feature extraction method and ML model, which were identified through extensive experimentation, are sufficiently lightweight to operate within the device’s memory constraints. Furthermore, the execution time confirms the feasibility of real-time queenless state detection in edge-based applications. Full article

The escalating impacts and occurrence of wildfires threaten the public, economies, and global ecosystems. Physiologically declining or dead trees are a great portion of the fires because these trees are prone to higher ignition and have lower moisture content. To prevent wildfires, hazardous vegetation needs to be removed, and the vegetation should be identified early on. This work proposes a real-time fire risk tree detection framework using UAV images, which is based on lightweight object detection. The model uses the MobileNetV3-Small spine, which is optimized for edge deployment, combined with an SSD head. This configuration results in a highly optimized and fast UAV-based inference pipeline. The dataset used in this study comprises over 3000 annotated RGB UAV images of trees in healthy, partially dead, and fully dead conditions, collected from mixed real-world forest scenes and public drone imagery repositories. Thorough evaluation shows that the proposed model outperforms conventional SSD and recent YOLOs on Precision (94.1%), Recall (93.7%), mAP (90.7%), F1 (91.0%) while being light-weight (8.7 MB) and fast (62.5 FPS on Jetson Xavier NX). These findings strongly support the model’s effectiveness for large-scale continuous forest monitoring to detect health degradations and mitigate wildfire risks proactively. The framework UAV-based environmental monitoring systems differentiates itself by incorporating a balance between detection accuracy, speed, and resource efficiency as fundamental principles. Full article