Search Results (680)

Background and Objectives: Cutaneous squamous cell carcinoma (cSCC) displays heterogeneous metastatic potential, and the role of sentinel lymph node biopsy (SLNB) in clinically node-negative patients remains debated. To evaluate tumor thickness and histological grade as predictors of sentinel lymph node (SLN) metastasis in high-risk cSCC and to assess the performance of a simplified pathology-based predictive model. Materials and Methods: This retrospective single-center study included consecutive patients with high-risk cSCC and clinically N0 status who underwent SLNB. Associations were examined using univariate and multivariable logistic regression, ROC analysis with bootstrap internal validation (2000 iterations), and decision curve analysis. Results: Thirty-four patients were analyzed; 12 (35.3%) had SLN metastases. SLN-positive patients had greater tumor thickness (median 5.5 mm vs. 3.0 mm, p = 0.006) and higher frequency of G2–G3 histological grade (91.7% vs. 45.5%, p = 0.011). Histological grade was the strongest independent predictor in multivariable analysis (OR 14.61, 95% CI 1.63–131.12). The combined model demonstrated apparently high discrimination in this small cohort (AUC 0.91; bootstrap 95% CI 0.79–0.99), though this estimate should be interpreted with caution given the limited number of events. A 4.0-mm threshold yielded sensitivity 83.3% and NPV 86.7%. Conclusions: In this exploratory single-center study, tumor thickness and histological grade were complementary predictors of SLN metastasis in cSCC. These findings are preliminary and require validation in larger prospective cohorts. Full article

Satellite observations of methane are frequently compromised by extensive data gaps caused by cloud cover and aerosol contamination, limiting their utility for continuous regional monitoring. To reconstruct these spatiotemporal discontinuities, this study developed the Stacked Ensemble Reconstruction Framework for Methane (SERF-XCH₄). By integrating Sentinel-5P TROPOMI retrievals with 25 multi-source environmental covariates, we generated a spatiotemporally continuous, high-resolution (0.1°) monthly dataset (SERF-XCH₄-IM) for Inner Mongolia spanning 2019 to 2023. Comprehensive validation demonstrates that the framework achieves exceptional predictive fidelity with a Coefficient of Determination ($R^2$) of 0.93 and a Root Mean Square Error (RMSE) of 7.89 ppb, significantly surpassing the performance of individual base learners and traditional interpolation methods. Furthermore, spatial block cross-validation confirmed robust generalization capabilities ($R^2=0.90$) in data-void regions. To unravel the “black box” of the model, SHapley Additive exPlanations (SHAP) analysis was employed, revealing that temporal factors (contributing 63.9%), air temperature, and elevation are the dominant drivers governing XCH₄ variability. Spatiotemporal analysis further identified the Hulunbuir region as a significant growth “hotspot” with an annual increase rate exceeding 18.5 ppb/yr, a trend primarily driven by intensified emissions during the autumn and winter seasons. Consequently, this framework establishes a high-precision, interpretable paradigm for regional methane monitoring and geo-information reconstruction. Full article

Background/Objectives: Identifying the causes of sentinel lymph node biopsy (SLNB) failure in early-stage oral cavity squamous cell carcinoma (OCSCC) is essential for refining surgical protocols and optimizing patient selection. This study aimed to evaluate the diagnostic performance, predictors of false-negative (FN) results, and long-term oncological outcomes of SLNB in patients with early-stage OCSCC. Methods: A retrospective, single-centre cohort study was conducted on 220 patients with cT1–cT2 N0 M0 OCSCC who were surgically treated between 2017 and 2024. Preoperative lymphatic mapping was performed using ^99mTc-nanocolloid and SPECT/CT. All sentinel lymph nodes (SLNs) underwent an ultrastaging protocol involving serial sectioning and immunohistochemistry. Diagnostic accuracy, survival outcomes, and clinicopathological predictors of FNs were analysed. Results: The SLN identification rate was 99.1%. Metastatic involvement was detected in 49 patients (22.3%), preventing 77.7% of the cohort from undergoing unnecessary neck dissection. Bilateral lymphatic drainage was observed in 55.9% of floor of the mouth tumours. During a median follow-up of 36 months, the diagnostic performance showed a sensitivity of 81.7%, a negative predictive value of 93.6%, and an overall accuracy of 95.0%. Analysis of the 11 FN cases showed that both depth of invasion (DOI) (6.0 mm vs. 3.0 mm; p = 0.010) and maximal tumour dimension (25 mm vs. 15 mm; p = 0.0008) were significant predictors of diagnostic failure. The five-year overall survival rate was significantly superior in patients with negative SLNs compared to the SLN-positive group (82% vs. 61%; p < 0.001). Conclusions: SLNB is an accurate and reliable staging tool for early-stage OCSCC, providing personalised lymphatic mapping that harmonizes oncological efficacy with the avoidance of overtreatment. However, an increased DOI and a larger tumour size significantly raise the risk of FN events, indicating the need for close postoperative surveillance in these high-risk subgroups. Full article

The Tianshan–Pamir mountain region, serving as the core “water tower” for countries in Central Asia east of the Aral Sea, is a critical bulwark for sustaining downstream socioeconomic systems. However, constrained by complex topography and harsh climatic conditions, this region suffers from a severe scarcity of long-term, continuous hydrological observation data. This study focuses on a typical data-scarce mountainous area, coupling UAV and satellite imagery-based (e.g., Landsat/Sentinel) flow inversion with a hybrid spatial regionalization method—integrating spatial proximity, basin similarity, and regression-based hydrograph reconstruction—to quantitatively estimate long-term discharge time series. The results indicate that, for the validation of instantaneous discharge inversion, the Nash–Sutcliffe efficiency coefficient (NSE) at 29 river cross-sections was consistently greater than 0.80, with the coefficient of determination (R²) reached 0.94 (p < 0.01). Subsequently, for the long-term discharge series reconstructed using the regionalization method, the NSE values at three representative verification sites—each corresponding to a distinct basin type—were 0.88, 0.84, and 0.86, respectively. These findings exhibit higher precision compared to direct temporal upscaling, confirming the reliability of the regionalization method across varying temporal scales. An analysis of monthly discharge trends from 1989 to 2020 revealed a decreasing trend in the discharge of glacier-dominated rivers, with an average rate of change of −2.89 ± 2.54% (p < 0.05); the Pamir Plateau experienced the largest decline (−4.89 ± 6.58%), which is closely linked to large-scale glacial retreat within the basins. Conversely, the discharge of non-glacier-dominated rivers showed an increasing trend, with a multi-year average rate of change of +0.32 ± 8.43% (n.s.), primarily driven by shifts in precipitation and vegetation cover. This research introduces a new approach for hydrological monitoring in data-scarce regions and provides essential data and methodological support for water resource management decisions in arid zones. Full article

Antimicrobial resistance (AMR) emerges and circulates across interconnected human, animal, food, and environmental reservoirs; however, food fermentation systems remain underexplored as indicators of local AMR pressure, even though artisanal dairy fermentations may function as natural sentinels of AMR. In this study, we used an artisanal dairy fermentation chain as a One Health model to investigate whether environmentally exposed lactobacilli can reflect stage-associated shifts in resistance. A total of 1.085 isolates representing 16 Lactobacillus species were recovered from the same artisanal dairy matrix at two fermentation stages: day 5 (“Tyrovolia”; n = 518) and day 30 (“Kopanisti”; n = 567). Susceptibility to 14 antibiotics was evaluated by broth micro-dilution, and L. acidophilus was further screened for selected resistance genes. Overall resistance increased significantly from 69.88% (362/518) at day 5 to 77.25% (438/567) at day 30 (p = 0.0059), while multidrug resistance rose from 37.57% to 60.73% of resistant isolates (p < 0.001). Across the 224 species–antibiotic combinations examined, 129 (57.58%) showed an increased upper MIC limit at day 30, and resistance increased significantly for 9 of the 14 antibiotics tested, with the largest rises observed for metronidazole (RR = 7.67), chloramphenicol (RR = 5.74), quinupristin/dalfopristin (RR = 4.11), vancomycin (RR = 2.78), and trimethoprim (RR = 2.43). In contrast, erythromycin and oxytetracycline resistance declined significantly at the ripened stage. In L. acidophilus, 21 resistance genes were detected in 14/70 day-5 isolates and 19 genes in 13/71 day-30 isolates, but marked genotype–phenotype discordance was observed, including matrix-dependent expression patterns for tetM, ermB, and blaTEM. Collectively, these findings show that environmentally exposed artisanal dairy fermentations can enrich resistance phenotypes and may serve as sensitive sentinels of AMR dynamics at the human–animal–environment interface. Full article

Objectives: This study aimed to investigate the usefulness and safety of sentinel lymph node (SLN) mapping in comparison to other types of lymph node dissection in patients with uterine corpus cancers. Methods: Retrospective data from 161 patients subjected to uterine corpus cancer staging with SLN mapping with indocyanine green (ICG) dye were collected. Results: SLN procedure was associated with a complication rate of 0%, a median number of dissected lymph nodes of 2 (range 0–13), and a median hospitalization following surgery of 5 (range:2–23) days. Systemic lymphadenectomy and one-sided pelvic lymph node resection were associated with the highest percentage of complications (12% and 25%; p = 0.0030), while the post-surgery course was uneventful for the selective lymphadenectomy group and SLN. Complication rates were the highest in patients with obesity and severe obesity (5.1% and 9.1%, respectively). The number of lymph nodes resected dropped numerically with increasing BMI. Successful ICG injection and SLN mapping were significantly more frequent in SLN procedures. Conclusions: Our study showed that SLN mapping was characterized by a low complication rate and short hospitalization following surgery, and obesity appeared to be related to a higher complication rate. Tailored surgical strategies and individualized patient selection are crucial for the success of SLN mapping; therefore, factors associated with successful SLN mapping with ICG need further exploration. Full article

Silicon-based treatments applied with UAV technology were evaluated over two consecutive rice-growing seasons (2024–2025) under Mediterranean field conditions. Silicon and silicon–manganese applications significantly reduced the Pyricularia infestation index (PII) by up to 77% at 35 DAS compared to the control (p < 0.01). Grain yield increased from 1717 kg ha⁻¹ in control plots to 4328 kg ha⁻¹ under silicon treatment and 3958 kg ha⁻¹ under silicon–manganese treatment. In contrast, Sentinel-2 spectral bands (B4 and B8) and vegetation indices (NDVI, RVI, NDRE, IRECI) were mainly influenced by interannual variability rather than treatment effects. While canopy reflectance showed high residual variability at later growth stages, agronomic and sanitary parameters consistently responded to silicon-based applications. These results indicate that foliar silicon, particularly when combined with manganese, improves Pyricularia suppression and yield stability under variable environmental conditions, although satellite-derived vegetation indices were more sensitive to year effects than to treatment differences. Full article

Background: Breast cancer is the most common malignancy in women, and sentinel lymph node (SLN) biopsy is essential for accurate nodal staging while avoiding unnecessary axillary dissection. Aim: This study aimed to compare SLN detection rates between two dual-tracer techniques: indocyanine green plus patent blue (ICG + PB) and technetium-99m plus patent blue (99mTc + PB), and to identify factors associated with detection failure for each tracer. Methods: All clinically node-negative breast cancer patients undergoing SLN biopsy between January 2014 and December 2024 were retrospectively evaluated. SLN detection was considered successful when at least one node was identified intraoperatively and confirmed histologically. Multivariate analysis assessed clinical and tumor-related predictors of failure. Results: A total of 269 procedures (258 patients) were analyzed, including 152 ICG + PB and 117 99mTc + PB procedures. Detection rates were comparable between groups (95.4% vs. 94.9%, p = 0.96), with no significant differences in the number of SLNs retrieved or nodal positivity. Multivariate analysis identified increasing patient age as the only independent predictor of PB failure, while no variables were associated with ICG failure. Tumor location in the upper-inner quadrant was the sole predictor of 99mTc failure. Conclusions: ICG + PB and 99mTc + PB provide equivalent and high SLN detection rates. ICG appears to be a robust, radiation-free alternative with no identifiable predictors of failure, supporting its role as an effective mapping strategy, particularly in centers aiming to optimize workflow and patient safety, despite the limited available data on its efficacy. Full article

This study develops an integrated geospatial framework to examine the spatial-temporal relationship between urban land-use change and tropospheric ozone dynamics within a mid-sized functional urban system, using Bolu, Türkiye, as a case study. Mid-sized urban systems remain underrepresented in air-quality and land-use research despite increasing environmental pressures under ongoing urbanization. The spatial framework was defined to encompass the central urban area and its surrounding peri-urban and transportation-influenced transition zones. Future land-use patterns were estimated to 2030 using the MOLUSCE model, while tropospheric ozone indicators were derived from Sentinel-5P observations for the 2020–2024 period and descriptively extended to 2030 using the Theil–Sen slope estimator. A fishnet-based spatial regionalization approach enabled consistent comparison between ozone trends and urban expansion intensity, quantified using the Urban Expansion Intensity Index (UEII). The integrated framework provides a spatially coherent basis for understanding land–atmosphere interactions in mid-sized urban systems. Full article

The sugarcane industry in South Africa is ranked among the top 15 producers worldwide and plays a significant role in supporting the nation’s socioeconomic development, producing approximately 2.3 million tons annually. Harvesting is largely labour-intensive and commonly involves the pre-harvest burning of sugarcane. This widespread practice is associated with (a) local air quality deterioration driven by pollutants such as carbon monoxide (CO), black carbon (BC), and sulphur dioxide (SO₂) and (b) adverse public health outcomes, including respiratory and cardiovascular diseases. This study aims to assess the air quality across KwaZulu-Natal and compare inland and coastal sugarcane-growing regions during the May–August 2023 harvest season. The CO and SO₂ concentrations are obtained from Sentinel-5P, while the BC data are sourced from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The Air Quality Index (AQI) is calculated using the CO, SO₂, PM_2.5, and NO₂ data from the Copernicus Atmosphere Monitoring Service (CAMS). The findings consistently indicate higher pollutant concentrations in inland regions, suggesting more concentrated burning activities and lower atmospheric dispersion relative to coastal areas. Overall, the results highlight the greater prevalence of poor air quality in inland sugarcane regions compared with coastal zones. Full article

Background/Objectives: Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is the sentinel implant-associated malignancy, illustrating how long-lived biomaterials can reshape local tissue–immune ecology. Although textured (high-surface-area) implants show the strongest epidemiologic association, the rarity of disease despite widespread exposure suggests additional host modifiers. We synthesize evidence supporting a gene–environment (G × E) framework and critically appraise emerging host-susceptibility signals (including BRCA1/BRCA2 and HLA associations). Methods: We conducted a narrative, evidence-based synthesis of peer-reviewed epidemiologic and registry studies, peri-implant niche biology (biofilm/foreign-body response and cytokine milieu), tumor genomic profiling, and current guidelines/regulatory communications, prioritizing primary studies for key claims. Results: Textured exposure dominates risk attribution, whereas absolute-risk estimates vary with denominators, exposure ascertainment, and follow-up duration. Mechanistic studies support a chronically inflamed capsule niche. Genomic analyses repeatedly converge on JAK/STAT pathway activation with frequent co-alterations in epigenetic regulators and recurrent copy-number changes, consistent with stepwise evolution under sustained selection. Immune-evasion features—including frequent PD-L1 expression and CD274 (9p24.1) copy-number alterations—provide a plausible checkpoint route, while host-susceptibility signals remain preliminary and require multi-center, multi-ancestry replication. Conclusions: BIA-ALCL is a multistep, context-dependent lymphoma in which implant-mediated inflammation intersects with host susceptibility to enable somatic evolution and immune escape. Clinically, prevention currently relies on exposure mitigation, standardized risk communication, and symptom-driven evaluation; precision prevention will require integrative cohorts linking verified device exposure, immunogenetics, microenvironment profiling, and tumor multi-omics. Full article

Air pollution remains a major public health and environmental challenge in China, driven by complex non-linear interactions among anthropogenic activities, meteorological conditions, and topographic features that go beyond simple linear relationships. This study presents a comprehensive spatio-temporal assessment of key air pollutants (CO, NO₂, SO₂, and PM_2.5) and their relationships with Total Column Ozone (TCO) across China’s provinces from 2019 to 2023. Multi-source high-resolution satellite data from Sentinel-5P/TROPOMI, the China High PM_2.5 dataset, MODIS, and ERA5-Land reanalysis were integrated. A tiered analytical framework was applied, combining linear Pearson correlations, non-linear Spearman rank correlations, and interpretable XGBoost machine learning with SHAP values. Results reveal a distinct seasonal “seesaw” pattern, with primary pollutants peaking during winter stagnation and TCO reaching maximum levels in late winter and spring. Non-linear analyses uncover critical threshold effects, including exponential increases in PM_2.5 and SO₂ when surface temperatures drop below 0 °C, very strong SO₂-TCO coupling (ρ = 0.93), and significant pollutant trapping in low-elevation regions (CO-elevation ρ = −0.82). These findings support the development of precision environmental policies with dynamic, temperature-threshold-based emission controls and topography-specific strategies to effectively mitigate air pollution in China. Full article

Monitoring environmental changes over time requires images with extensive historical depth. However, high spatial resolution images often lack such depth. This study investigates the impact of spatial resolution on image classification. Thus, Landsat 8 and Sentinel-2 images acquired between October and December 2020 were processed and classified using Random Forest regression on Google Earth Engine (GEE). This method allows for continuous land cover maps, required for robust assessment of land cover dynamics in patchy landscapes. A total of 1719 training samples were collected from the Collect Earth Online (CEO) platform to train the model. In addition to the spectral bands, vegetation indices were considered to optimize classification results. The study revealed statistical differences in land cover areas estimated by the two sensors. These differences are statistically significant at p < 0.001, although they are small. Validation results showed that the RMSE from Sentinel-2 is slightly lower than that from Landsat 8, with this difference significant at p < 0.05. Therefore, spatial resolution influences the accuracy of image classification. Nevertheless, given the observed differences between the two sensors, which ranged from 0.03% to 3.94% across land covers, Landsat imagery remains suitable for producing reliable land cover maps in heterogeneous landscapes. Full article

The US Southwest is one of the driest and hottest regions, with a recent upsurge in land surface temperature (LST). Further, with land-use changes and global warming, anthropogenic pollution also significantly contributes to the rise in surface temperatures. While the impact of pollution on LST has been studied only in specific urban regions, insights from a broader, more diverse topography remain limited. This research incorporates LST with land cover parameters (NDBI, MNDWI, NDBSI, SAVI, WET), surface albedo, air pollutants (NO₂, SO₂, O₃, CO), aerosol particles, urban nighttime light, and digital elevation model to evaluate the non-linear spatial dependence of these variables for the summer (from June to August 2025) and winter (from December 2024 to February 2025) seasons in the US southwest. All multi-resolution inputs were harmonized by projecting to WGS84 and applying a ~11 km fishnet sampling grid commensurate with the coarsest-resolution dataset (Sentinel-5P), ensuring each sample captures a unique pixel value across all layers. AutoML was applied to benchmark learning algorithms, and we found that CatBoost, Extra Trees, LightGBM, HistGradientBoosting, and Random Forest were among the optimal models for predicting LST. After tuning these models using Bayesian optimization, we achieved a mean R² of 0.86 during summer and 0.84 during winter. After developing the hyperparameter-optimized model, explainable AI, e.g., SHAP, was employed to understand the complex nonlinear dynamics and top contributing features. Landcover variables had a more dominant impact on the spatial distribution of summer LST, while winter LST was more influenced by pollutant parameters. Partial Dependency Plot and Accumulated Local Effect were further incorporated to examine the marginal effects of the top-contributing features on spatial LST prediction. By extending the study area to the entire US Southwest, this study effectively captures urban–rural contrasts, climate- and land-cover–dependent pollutant responses, and regional climatic influences. It presents explicit spatial dependencies among LST, pollutants, land cover, topography, and nighttime activity that will aid future researchers and policymakers in effectively developing sustainable thermal planning for urban activities. Full article

This study proposes a feature-level fusion method that integrates Differential Interferometric Synthetic Aperture Radar (D-InSAR) and Unmanned Aerial Vehicle photogrammetry (UAV-P) for monitoring mining-induced subsidence basin (MSB). The method begins by extracting key subsidence characteristics based on the patterns of coal-mining-related surface displacement; the centimeter-level subsidence boundary is determined from D-InSAR data, while the meter-scale deformation at the subsidence center is derived from UAV-P. These extracted features are then used to invert the parameters of the probability integral method (PIM). The subsidence basin predicted by the inverted parameters serves as a criterion to select the superior dataset between the D-InSAR and UAV-derived results. Finally, the selected subsidence data are fused to generate a composite subsidence map. The proposed method was applied to the 2S201 panel in the Wangjiata Coal Mine using eight Sentinel-1A images and two UAV surveys. The fusion results were evaluated for their regional and overall accuracy against 30 ground control points measured by total station and GPS. The results demonstrate that the fusion method not only accurately extracts large-scale deformations in the mining area, with a maximum subsidence of 2.5 m and a root mean square error (RMSE) of 0.277 m in the subsidence center area, but also precisely identifies the subsidence boundary region with an accuracy of 0.039 m. The fused subsidence basin exhibits an overall accuracy of 0.182 m, which represents a significant improvement of 83.6% and 27.8% over the results obtained using D-InSAR and UAV alone, respectively. This method effectively reconstructs the complete morphology of the mining-induced subsidence basin, confirming its feasibility for practical applications. Full article