Search Results (8,796)

Antimicrobial resistance (AMR) is one of the most significant threats to healthcare systems, particularly in low- and middle-income nations where infection prevention and control, antimicrobial stewardship, and laboratory surveillance might not be optimal. Acinetobacter baumannii is a high-risk nosocomial pathogen that has a strong capacity to develop extreme resistance phenotypes. Still, the degree to which extensively drug-resistant (XDR) and pandrug-resistant (PDR) phenotypes reflect the cumulative impact of antimicrobial pressure at unit and system levels in Iraqi hospitals is not fully described. This was a cross-sectional surveillance study that was a laboratory-based investigation done in public hospitals in the Governorate of Kirkuk between January 2024 and January 2025. The BD Phoenix system identified 80 non-duplicate A. baumannii isolates that were obtained in high-risk hospital units. The interpretation of antimicrobial susceptibility testing was done according to CLSI guidelines. Internationally recognized definitions were adjusted to local therapeutic availability to classify isolates as XDR or PDR. Unadjusted odds ratios and Fisher’s exact test were used to assess the associations between the PDR phenotype and the chosen clinical or unit-level variables. Among the 80 isolates, 60 (75%) were XDR and 20 (25%) were PDR. Burn units and wound-related infections were disproportionately represented by PDR isolates. There were significant associations between the PDR phenotype and burn unit admission, wound infection, exposure to invasive devices, long hospitalization (greater than 14 days), and previous exposure to broad-spectrum antibiotics. ICU admission and respiratory infection were not significantly related. Cefepime had in vitro activity only in a subset of XDR isolates. Extreme resistance phenotypes can be used as convenient sentinel measures of cumulative antimicrobial pressure and system-level stress in resource-limited environments. There is an urgent need to strengthen infection prevention and control, antimicrobial stewardship, and laboratory surveillance to preserve the remaining therapeutic options. Full article

Mediterranean meadows of Posidonia oceanica are chronically exposed to complex mixtures of environmental contaminants, including metals and trace elements derived from coastal urbanization, maritime traffic, and industrial activities. This study aimed to assess metal-related toxicity in P. oceanica by integrating multi-element burden analysis with a panel of oxidative stress biomarkers. Concentrations of a wide suite of elements were quantified in samples of internal (juvenile), intermediate, and external (adult) leaves, reflecting the ontogenetic structure of the plant. Oxidative responses were evaluated using five biomarkers [i.e., hydrogen peroxide (H₂O₂), lipid peroxidation (TBARS), superoxide dismutase (SOD), glutathione S-transferase (GST), and catalase (CAT)] measured on each leaf compartment. Biomarker data were standardized and integrated into a merged Stress Index summarizing overall physiological toxicity. Associations between individual elements, the sum of all measured elements (ΣallElements), the Stress Index, and single biomarkers were explored using Pearson correlation analysis. Juvenile leaves exhibited the highest Stress Index values, elevated H₂O₂ and TBARS, and marked activation of SOD and GST, indicating early oxidative toxicity. Intermediate leaves showed a trend toward increased CAT activity, not reaching statistical significance, along with minimal damage, suggesting effective detoxification, whereas adult leaves accumulated higher levels of Fe, Ni, and Pb, but displayed moderate stress responses. Overall, leaf-class structure strongly modulated both exposure and toxicological response. The integration of ΣAllElements with multi-biomarker indices provides a robust framework for diagnosing metal-related toxicity in P. oceanica under realistic multi-element exposure scenarios. Full article

Deep mining of geologically challenging deposits, such as narrow, steeply dipping orebodies, is increasingly pursued to meet the rising demand for mineral resources. However, the geotechnical stability of operations in such environments remains a persistent challenge. A paramount concern is the insufficiently understood mechanisms governing the surface subsidence and stability of underground excavations, which diverge significantly from those in flat or gently dipping deposits. This study bridges this gap through an integrated methodology applied to a deep cut-and-fill gold mine in China. We combined nine years (2016–2025) of SBAS-InSAR monitoring, utilizing 120 Sentinel-1 images corrected with precise orbit and atmospheric correction data, with a comprehensive three-dimensional (3D) numerical simulation. The results reveal a unique subsidence pattern: surface subsidence is highly localized, forming an elliptical basin directly above the orebodies, with a footwall movement angle exceeding 90°. Furthermore, the subsidence magnitude showed minimal progression despite increasing mining depth, with a maximum cumulative subsidence of only 9.3 mm. Numerical simulation confirmed these findings and demonstrated that underground shafts and tunnels remained stable under the sequential extraction of multiple orebody levels. This exceptional geotechnical response is attributed to a synergistic mechanism involving the intrinsic geomechanical advantages of the steeply dipping geometry, the low-disturbance nature of narrow-vein mining, and the crucial structural support provided by the backfilling. This study demonstrates the efficacy of cut-and-fill mining for ensuring operational safety and minimizing surface environmental impact in the deep mining of narrow, steeply dipping orebodies, providing critical insights for the sustainable exploitation of deep mineral resources. Full article

Moroccan installations of solar photovoltaic panels experience operational difficulties due to shading and vegetation-related soiling, which reduce energy output by 15–30%. Most monitoring systems depend upon a single vegetation index, which can reduce the accuracy of detecting even moderate anomalies. This paper presents a novel integration of multi-criteria hierarchical labeling with dual-branch deep learning for enhanced vegetation anomaly detection. We combined MODIS (2000–2015) and Sentinel-2 (2015–2025) images and NASA POWER weather records to study a 25-year vegetation record using multi-source satellite data in 5 of Morocco’s ecologically diverse zones. We introduced a three-class hierarchical labeling scheme (normal, moderate, severe) for dynamic vegetation models based on combined vegetation indices (NDVI, EVI, NDWI) and meteorological thresholds. The proposed dual-branch architecture uses independent data streams for unfused data, which include temporal multi-scale CNNs (TMSCNN) for spatiotemporal modeling and bidirectional LSTMs for weather-integrated vegetation data. Systematic ablation studies show improvements from using NDVI (68.98%) to multispectral indices (77.74%), meteorological integration (81.02%), and a final accuracy of 82.34% ± 0.88%. The moderate anomaly class exhibits lower precision (65%), demonstrating the challenge of operationalizing severity-based anomaly classification. This work integrates hierarchical, multi-criteria labeling and hybrid deep learning for solar photovoltaic vegetation monitoring. Full article

Microplastic (MP) and nanoplastic (NP) pollution has emerged as a pervasive and still insufficiently quantified pressure on terrestrial ecosystems, yet its consequences for wild herbivores remain incompletely understood. As key links between primary producers and higher trophic levels, wild herbivores occupy a critical ecological position and may serve both as exposed receptors and as biological vectors of plastic contamination. This manuscript presents a narrative review that synthesizes recent advances in understanding the physiological, behavioural, and ecological implications of MP and/or NP exposure in free-ranging herbivorous mammals, integrating evidence from field surveys, experimental studies, ecological modelling, and supportive mechanistic findings from livestock and experimental mammalian systems. Available evidence indicates that MPs and NPs are consistently detected in wild herbivores from both human-modified and protected landscapes, demonstrating widespread terrestrial exposure. Reported biological effects include oxidative stress, digestive dysfunction, inflammatory and immune responses, altered gut microbial communities, impaired nutrient assimilation, and organ-level damage, although much of the mechanistic evidence derives from controlled laboratory or livestock-based studies rather than direct wildlife investigations. Behavioural responses remain comparatively underexplored, particularly in large-bodied herbivores, with limited evidence for altered foraging, habitat use, and stress-related behaviours. At the ecosystem level, emerging studies suggest that herbivores may contribute to the landscape-scale redistribution of MPs and NPs through movement and faecal deposition, with potential downstream effects on soil processes, nutrient cycling, and plant–herbivore interactions. However, the current evidence base is constrained by major methodological and conceptual limitations, including the lack of standardized detection and reporting protocols, limited ecological realism in exposure studies, taxonomic and geographic biases, and poor resolution of long-term population-level and food-web consequences. Overall, the available literature indicates that MP and NP pollution represent a multifaceted and emerging risk to wild herbivores and the ecosystems they inhabit. Future research should prioritize standardized contamination-controlled monitoring, non-invasive faecal surveillance, ecologically realistic chronic exposure studies, and integrated conservation frameworks that recognize wild herbivores as sentinel species for terrestrial plastic pollution. Full article

The optimal management of patients with limited sentinel lymph node metastasis in breast cancer, particularly regarding whether to perform additional axillary surgery, continues to be an area of clinical uncertainty in routine practice. This multicenter retrospective cohort study aimed to evaluate adherence to ACOSOG Z0011 criteria and the oncological safety of omitting ALND in a Chinese population. We included 462 women with clinical stage T1–2N0 breast cancer who underwent breast-conserving surgery and were found to have 1–2 positive SLNs between January 2013 and December 2021. All patients received adjuvant radiotherapy and systemic therapy. Patients underwent either sentinel lymph node biopsy alone (SLNB; n = 274, 59.3%) or SLNB followed by ALND (n = 188, 40.7%). Propensity score matching (1:1) was applied to balance baseline characteristics, yielding 152 matched pairs. Disease-free survival (DFS) was the primary endpoint. No significant difference in DFS was observed between the SLNB alone and SLNB + ALND groups in either the overall cohort or the matched cohort. Multivariable Cox regression analysis confirmed that the type of axillary surgery was not independently associated with DFS in patients with 1–2 positive SLNs treated with breast-conserving surgery. Logistic regression analysis indicated that surgeons were more likely to perform ALND in patients with a higher SLN tumor burden; compared with micrometastasis, macrometastasis in 1–2 SLNs and a sentinel lymph node metastasis ratio greater than one-third were significantly associated with the selection of ALND. These findings suggest that omission of ALND was not associated with a statistically significant difference in DFS and provide real-world evidence supporting the applicability of Z0011-based axillary management in the Chinese population; however, given the observational design and potential for residual confounding, these results should be interpreted with caution. Full article

Subalpine grasslands represent highly sensitive ecosystems that are increasingly exposed to climate extremes, yet their long-term disturbance dynamics remain poorly documented. This study investigates climate-driven dieback of subalpine grasslands in Central Europe using a harmonized, multi-decadal satellite time series. We analyzed Landsat (TM, ETM+, OLI, OLI-2) and Sentinel-2 imagery spanning 1984–2024 to detect changes in grassland condition, supported by field-based validation, climatic indices, and geomorphological analysis. Several spectral indices related to non-photosynthetic vegetation were evaluated, with the Normalized Burn Ratio (NBR) providing the best discrimination of dead grassland. In spatially grouped cross-validation, NBR achieved very high accuracy for dead versus non-dead grassland, with AUC = 0.9996, precision = 1.00, recall = 0.82, and F1-score = 0.90 for Sentinel-2, and AUC = 0.9982, precision = 1.00, recall = 0.62, and F1-score = 0.76 for Landsat 9. Retrospective mapping revealed four dieback events since 2000: two short-term episodes with rapid within-season recovery (2000, 2003) and two long-term events characterized by persistent degradation and slow regeneration (2012, late 2018–2019). The largest short-term event, in 2003, affected 42.19 ha of total dieback and 96.95 ha including partially damaged or regenerating grassland. Dieback extent was negatively associated with water balance deficit, strongest for SPEI-12 (ρ = −0.548, p = 0.002), while winter frost under shallow-soil conditions likely contributed to long-term damage in 2012. Geomorphological analysis indicated that elevation, terrain curvature, and, to a lesser extent, wind exposure are the primary controls on dieback susceptibility, highlighting the importance of fine-scale environmental controls. Our results demonstrate the value of long-term, multi-sensor satellite observations for detecting and interpreting climate-driven disturbances in subalpine grasslands and provide a transferable framework to support monitoring and conservation of mountain ecosystems under ongoing climate change. Full article

The Eurasian lynx (Lynx lynx) is an apex predator and an important sentinel for trophically transmitted helminths acquired via predation on wild ungulates. On 2 March 2022, an adult male lynx that was road-killed in the Apuseni Mountains (Surducel hunting ground, Bihor County) was collected, frozen for biosafety, and a necropsy was performed. Taeniid cestodes were detected, with a total intestinal burden of nine adult specimens. Genetic analyses confirmed Taenia lynciscapreoli, and the obtained sequences were deposited in GenBank (PV843597, PV855065, PV844409). Phylogenetic inference based on cox1 assigned the Romanian isolate within the European cluster, distinct from the Chinese isolate, while showing genetic proximity to Taenia sp. (MW846305) that have been reported from a lynx in China. This study represents the first molecular identification of T. lynciscapreoli in the Eurasian lynx in Romania and, to our knowledge, the first record from Southeastern Europe. Full article

Estimating forest aboveground biomass (AGB) in mountainous forest ecosystems remains a significant challenge due to complex terrain, the high cost and limited applicability of traditional field-based methods. To address this issue, a remote sensing-based AGB estimation framework integrating intelligent optimization and machine learning was developed for Mount Tai in eastern China. Sentinel-2 multispectral data were selected to derive 105 candidate variables, including spectral bands, vegetation indices, texture features, and topographic factors, from which 17 key variables were selected using Pearson correlation analysis for model construction. A Support Vector Machine (SVM) optimized by the Pigeon-inspired optimization (PIO) algorithm was developed to adaptively determine optimal hyperparameters, and its performance was compared with that of Random Forest (RF) and standard SVM models. Among the three models, PIO-SVM produced the highest numerical accuracy. For the training dataset, it obtained an R² of 0.85 and an RMSE of 46.12 t/hm². For the testing dataset, it achieved an R² of 0.73 and an RMSE of 62.19 t/hm², compared with 0.72 and 66.25 t/hm² for the standard SVM model and 0.70 and 65.19 t/hm² for the RF model. The spatial distribution of AGB derived from the optimal model shows higher AGB values in the central and northern regions characterized by dense forest cover, in close agreement with field observations. Overall, the results suggest that PIO-based parameter optimization can improve SVM performance for AGB estimation in mountainous forests. This study provides a reliable and efficient framework for regional-scale monitoring of forest biomass and carbon sink dynamics. Full article

This study presents the development and implementation of a web-based geospatial platform for the quantitative assessment of land use and land cover change (LULCC) based on multispectral satellite images. The system operationalizes the Rao spectral diversity metric (Rao’s Q) to detect and quantify LULCC resulting from different environmental agents. The platform supports single-band (classic mode) or multi-band (multidimensional mode) processing. Its main functionalities include the interactive de-limitation of areas of interest (AOI) and calendar-based temporal selection, allowing analyses to be performed at discrete time points or at defined intervals. Among the tools available in the application are the automated calculation of Rao’s Q surfaces and maps of change between pairs of dates. Additionally, the platform allows the selection of several spectral indices, with the aim of supporting ecosystem monitoring and the characterization of the Earth’s surface. In the use case demonstration (Reykjanes Peninsula volcanic eruption of February 2024), the Rao’s Q method applied to Sentinel-2 SWIR imagery demonstrated strong performance in lava flow detection, with the multidimensional approach (bands 11 + 12) achieving the most balanced results (OA = 83.0%, PA = 84.0%, UA = 82.4%), while band 11 alone yielded the highest precision (UA = 97.4%). By integrating spatiotemporal analysis, spectral diversity metrics, and spectral indices into an accessible and extensible framework, the platform constitutes a robust tool for monitoring LULCC and assessing environmental impacts. Full article

Antimicrobial resistance (AMR) is a growing global concern, exacerbated by the overuse of antibiotics in livestock farming. Honey bees (Apis mellifera), widely used as bioindicators of environmental contamination, may also serve as sentinels for monitoring the environmental spread of antibiotic resistance genes (ARGs). This study investigated the presence of ARGs and the gut microbiota composition of honey bees sampled from 11 apiaries located in a region of Northwestern Italy characterized by intensive livestock farming. PCR and Sanger sequencing analyses revealed a widespread presence of tetracycline resistance genes—particularly tetB and tetC—as well as occasional detection of blaTEM, qnrB, and int1 genes. tetB and tetC were also identified in three bacterial colonies isolated from bee guts, notably in Hafnia spp. 16S rRNA gene sequencing of the gut microbiota revealed dominance of genera such as Bartonella, Snodgrassella, Gilliamella, Bombilactobacillus, and Lactobacillus. Some samples showed shifts in the microbial diversity. The findings confirm the potential of honey bees as bioindicators for environmental AMR surveillance and underscore the need for further research to elucidate correlations between ARG presence and microbial community structure in honey bees from various ecological contexts. Full article

Accurate crop classification in complex and heterogeneous agricultural landscapes is often challenged by mixed-pixel effects and spatial autocorrelation. This study proposes a prior-guided crop classification framework that integrates accessible Moderate Resolution Imaging Spectroradiometer (MODIS) optical and Sentinel-1 synthetic aperture radar (SAR) time-series data with explicit phenological and structural priors. By embedding physically meaningful constraints into temporal feature learning, the model shifts from purely data-driven learning toward biophysically interpretable discrimination between crop types and background classes. Performance was rigorously evaluated using spatial cross-validation (SCV) to ensure geographic independence. Results demonstrate that the prior-guided CNN achieves an overall accuracy (OA) of 98.66% and a Kappa of 0.9832, outperforming unguided deep learning and conventional machine learning models. Notably, the framework exhibits high spatial robustness, with a minimal performance gap between random and spatial validation (ΔOA = 0.0049). In addition to improving classification accuracy, integrating phenological features with SAR-based prior information enhances the stability of non-crop categories in fragmented scenarios, while leveraging readily available medium-resolution data to support large-scale applications. These findings demonstrate that embedding physically meaningful prior knowledge into multi-source time-series learning improves classification accuracy while enhancing spatial generalizability and interpretability. More broadly, the proposed framework offers a transferable paradigm for integrating domain knowledge with deep learning, providing a practical and scalable solution for crop mapping in heterogeneous agricultural landscapes using widely accessible medium-resolution data. Full article

Accurately monitoring the surface stabilization of waste dumps in open-pit coal mines is critical for hazard prevention and ecological reclamation. In arid and semi-arid regions, traditional optical remote sensing vegetation indices suffer from a systematic “response lag” in assessing physical stability due to the slow establishment of pioneer vegetation. To overcome this biological limitation, this study proposes a quantitative spatiotemporal monitoring framework based on time-series Interferometric Synthetic Aperture Radar (InSAR) coherence to detect early-stage geotechnical stabilization. Using Sentinel-1 imagery of the Balongtu coal mine, a sliding-window detection algorithm was developed to capture the physical transition of surface electromagnetic scattering mechanisms from active disturbance to stable consolidation. The main findings are as follows: (1) Statistical analysis identified a critical geophysical coherence threshold of 0.15, which effectively and objectively distinguishes active dumping disturbance zones from structurally stable areas. (2) The spatiotemporal evolution dynamics of the completed dump areas from 2017 to 2023 were successfully characterized, revealing that 87.6% of the open-pit areas achieved physical stabilization within three years post-mining, with a spatial distribution highly consistent with the objective operational rule of “mining first, dumping later”. (3) Accuracy assessment using 700 spatiotemporally balanced validation points—derived through strict visual interpretation of high-resolution optical imagery—demonstrated high algorithm reliability, achieving overall accuracies (OA) of 87.57% and 90.43% at half-yearly and annual monitoring intervals, respectively. By decoupling physical surface stabilization from optical greenness, this study provides a timely abiotic precursor indicator, offering scientific, quantitative decision support for precision ecological zoning and accelerated land turnover approval in mining areas. Full article

Leaf Area Index is a crucial biophysical variable, and its accurate estimation is essential for understanding vegetation dynamics. However, cloud cover significantly restricts optical remote sensing, hindering the generation of spatially continuous Leaf Area Index products. Remote sensing foundation models offer novel solutions to this challenge. This study presents an end-to-end framework based on the fine-tuned Prithvi foundation model for direct LAI retrieval from cloud-contaminated 30 m Harmonized Landsat and Sentinel-2 imagery. By mapping inputs directly to Hi-GLASS reference labels, the proposed architecture processes cloud contamination and vegetation signals simultaneously and circumvents the error propagation inherent in cascaded retrieval pipelines. Results demonstrate that the end-to-end LAI retrieval model significantly outperforms cascaded variants, achieving a superior R² (0.78) and lower RMSE (0.57). Furthermore, predictive accuracy exhibits a distinct U-shaped trajectory relative to the temporal mean cloud fraction, reaching an inflection point at 50–60% occlusion, which highlights the model’s implicit regularization capacity under severe atmospheric interference. This work establishes that direct feature learning with foundation models offers a more robust and streamlined pathway for generating continuous biophysical products from imperfect optical observations, prioritizing quantitative fidelity over artificial perceptual sharpness. Full article

Mast cells are heterogeneous, tissue-resident immune sentinels best known for their roles in allergy and peripheral inflammation. The discovery of mast cells within the meninges and brain parenchyma over a century ago raised enduring questions regarding their function in the central nervous system (CNS), their ontogeny, and distinction from peripheral counterparts. Brain mast cells are sparse and predominantly located in perivascular niches rather than forming dense aggregates, a feature that has made them difficult to study. Nevertheless, accumulating evidence implicates mast cells in diverse aspects of CNS physiology and pathology, including regulation of blood–brain barrier permeability and neurovascular function, as well as immune surveillance in contexts of infection and injury. The ability of mast cells to communicate with neighboring glial and neuronal networks suggests potential roles in modulating neural activity, development, and behavior, although this dimension remains incompletely understood. Much of the foundational literature predates advanced immunological tools, contributing to persistent misconceptions regarding the identity and significance of brain mast cells. In this review, we outline the history of research investigating this enigmatic aspect of mast cell biology, clarifying what is known, what remains speculative, and how emerging insights may help redefine the boundaries between classical immunology and neuroscience. Full article