Search Results (1,015)

Introduction: Lung cancer remains the leading cause of cancer-related mortality among men in Poland. Prognosis is generally poor, largely due to late diagnosis at advanced stages and the aggressive biological nature of the disease. Aim: This study aimed to evaluate the effectiveness of various treatment modalities and determine their impact on overall survival in male patients diagnosed with small-cell (SCLC) and non-small-cell lung cancer (NSCLC). Methods: This retrospective cohort study analyzed 1431 men (mean age: 61.5 years) treated at the Katowice Oncology Center in Poland between 2002 and 2012. Overall survival was assessed using the Kaplan–Meier method and multivariable Cox proportional hazards regression. Evaluated prognostic factors included clinical stage, surgical intervention (partial or total lung resection), first-line treatment regimen, and the number of treatment cycles. Results: Survival probabilities declined progressively with advancing clinical stage for both SCLC and NSCLC. Patients who underwent surgical resection demonstrated significantly longer survival compared to non-surgically treated patients (p < 0.001). Furthermore, combined radiochemotherapy yielded superior therapeutic outcomes compared to chemotherapy alone. In the non-surgical NSCLC cohort, first-line treatment with platinum derivatives combined with gemcitabine resulted in the highest 1-year survival rate compared to other pharmacological schemes. Discussion: The high mortality observed within the first 12 months post diagnosis reflects the late-stage presentation common during the study period. The findings align with established oncological principles, confirming that surgical resection and multimodal therapies offer the greatest survival advantages for eligible patients. Conclusions: Survival rates for both SCLC and NSCLC are overwhelmingly dictated by early diagnosis and the feasibility of surgical resection. Improving long-term outcomes depends heavily on implementing effective lung cancer screening programs to detect the disease at operable stages and utilizing optimized combined treatment protocols. Full article

Emergency Alert and Warning Systems (EAWSs) are essential components of sustainable disaster risk reduction, providing communities with timely information to prepare for and respond to impending hazards. In the Philippines, one of the world’s most disaster-prone countries, earthquakes, typhoons, and other natural hazards occur frequently. However, national statistics from 2018 indicated that only 40% of Filipinos considered themselves well prepared for disasters, while 31% reported being slightly prepared or not prepared at all. This study investigates the perceived effectiveness of EAWSs in enhancing disaster awareness and preparedness among Filipino residents. Guided by the Theory of Planned Behavior (TPB), the research develops an integrated framework to examine behavioral, technical, and perceptual factors influencing preparedness intentions. Data were collected from 200 respondents through a structured survey. Structural Equation Modeling (SEM) was employed to identify significant linear relationships among the constructs, while an Artificial Neural Network (ANN) analysis was subsequently applied to capture nonlinear patterns and rank the relative importance of key predictors. Unlike previous studies that rely solely on SEM or descriptive approaches, the combined SEM–ANN framework enables a more comprehensive understanding of both causal relationships and complex behavioral dynamics influencing disaster preparedness. The findings reveal that behavioral intention, system reliability, message clarity, and trust in EAWS substantially affect individuals’ preparedness behavior and risk mitigation actions. These results underscore the importance of strengthening EAWS design and communication strategies to support long-term disaster resilience. The study provides practical insights for national agencies, local governments, and policymakers on refining emergency communication systems and developing sustainable, evidence-based disaster preparedness initiatives. Full article

The use of satellite Differential Synthetic Aperture Radar Interferometry (DInSAR) has transformed the analysis of landslide dynamics by enabling detailed spatiotemporal monitoring of slow and subtle ground deformations. DInSAR enables comprehensive geomorphological characterization and identification of triggering factors. Retrospective applications of DInSAR provide valuable insights into past events and support causal analysis linked to rainfall episodes or piezometric fluctuations. Moreover, integration with numerical modeling enhances predictive capabilities and facilitates the calibration of geotechnical parameters. DInSAR is also instrumental in assessing infrastructure impacts and in the generation of susceptibility, hazard, vulnerability, and risk maps, which are key for land-use planning and risk management. Nevertheless, this technique has inherent limitations that must be carefully considered when interpreting results. Future developments, driven by the integration of artificial intelligence and enhanced computing capacities, are transforming the landscape of InSAR applications in landslide studies. These advancements, combined with upcoming satellite missions, are expected to significantly improve measurement accuracy, temporal resolution, and overall operational potential, paving the way for more robust quasi-early warning systems for landslide prevention. In this work, an overview of the current applications, future trends, and challenges of DInSAR in landslide studies is presented, with particular emphasis on the practical dimension of landslide studies and on the exploitation of DInSAR outcomes to support risk management and mitigation strategies. Full article

Rainfall-induced landslides represent one of the most recurrent geohazards affecting the transportation network of southwestern Calabria (Italy). This study provides an integrated assessment of landslide occurrence and road damage along the Costa Viola by combining detailed geomorphological mapping, multi-temporal analyses, historical documentation (1950–2025), and GIS-based spatial data processing. A total of 261 landslides were mapped, affecting approximately 19% of the study area. Slides constitute the dominant movement type (66.7%), followed by complex landslides, flows, and falls. Landslide distribution is strongly controlled by geological and morphometric factors: more than 80% of mapped phenomena occur in highly fractured granitic and gneissic rocks, over 70% lie within 500 m of faults, and more than 90% are located within 300 m of streams. Slope gradient (25–55°) and local relief (350–550 m) further contribute to slope instability patterns. The historical dataset documents 237 landslide-induced road damage events over 75 years, with a marked increase in occurrence since the early 2000s. Most damage events affected the SS18 road and frequently corresponded to reactivations of pre-existing landslides, highlighting the long-term persistence of slope instability and the seasonal influence of intense autumn–winter precipitation. Overall, the results demonstrate that landslide hazard in the Costa Viola is governed by the interplay between structural, lithological, geomorphic, and climatic factors, compounded by anthropogenic modifications along road corridors. The combined landslide inventory and historical database provide a robust basis for risk mitigation, identification of critical road sectors, and future susceptibility and predictive modelling to support effective territorial planning. Full article

Objective: This study aimed to evaluate the prognostic value of the SUVmax–IPI composite score, generated by integrating the maximum standardized uptake value (SUVmax) derived from metastatic ⁶⁸Ga-PSMA PET/CT imaging with the inflammatory prognostic index (IPI), in predicting overall survival in patients with metastatic prostate cancer. Materials and Methods: This retrospective, single-center cohort study included 146 patients diagnosed with metastatic prostate adenocarcinoma between 2009 and 2025. Among them, 125 patients with available PET/CT imaging were included in the SUVmax–IPI analysis. The composite score was calculated by multiplying the metastatic SUVmax value by the IPI. The optimal cut-off value was determined using receiver operating characteristic curve analysis. Overall survival was evaluated using the Kaplan–Meier method and compared using the log-rank test. Independent prognostic factors were identified using multivariable Cox proportional hazards regression analysis with a forward (stepwise) selection approach. Results: Using the predefined cut-off value (82), the median overall survival was 125 months in patients with SUVmax–IPI ≤ 82 and 19 months in those with SUVmax–IPI > 82 (log-rank p = 0.001). In the forward multivariable Cox regression model, SUVmax–IPI > 82 remained independently associated with worse overall survival after adjustment for ALP, AST, PSA nadir, and androgen deprivation modality (hazard ratio [HR]: 7.92; 95% confidence interval [CI]: 2.97–21.10; p < 0.001). Conclusions: The SUVmax–IPI composite score, integrating PSMA PET/CT-derived metabolic tumor activity with systemic inflammatory burden, is independently associated with overall survival in metastatic prostate cancer. These findings suggest that combining metabolic and inflammatory parameters may enhance prognostic stratification beyond conventional clinical and biochemical markers. Full article

Sustainable groundwater management is essential for water security and human health protection. Fluoride contamination is a serious concern for the sustainable drinking water supply in many parts of Pakistan, including Balochistan, where arid climate conditions and geological formations support the enrichment of fluoride. The toxic nature of fluoride contamination has resulted in negative health impacts on the local population. Conventional geostatistical techniques are usually ineffective to delineate the nonlinear relationships that affect the distribution of fluoride. This study aims to develop a machine learning-driven spatial modelling framework for classifying the spatial distribution of fluoride contamination in groundwater across the study area. The model will help to understand the spatial variability of fluoride contamination and its controlling factors, essential for effective mitigation and early warning systems. Physiochemical elements were used as predictive features in this study, utilizing a unified feature importance framework combining hydrogeochemical analysis, spatial distribution assessment, and ensemble SHAP-based interpretation to identify consistent predictors. Model performance was evaluated using a nested cross-validation framework, followed by validation on an independent geology-informed spatial holdout test set to ensure realistic generalization. Among machine learning models, the Logistic Regression (LR), Support Vector Classifier (SVC), XGBoost (XGB), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbours (KNN) were evaluated. Support Vector Classifier (SVC) demonstrated a high predictive performance. On the independent spatial holdout dataset, SVC achieved an overall accuracy of 0.75 and an area under the receiver operating characteristic curve (AUC) of 0.821. In addition to classification, a human health risk assessment was conducted using chronic daily intake (CDI) and hazard quotient (HQ) calculations for children and adults, identifying several high-risk water supply schemes. The prediction maps successfully delineated high-risk fluoride points across specific areas, offering a tool for sustainable groundwater management. This study helps to achieve a Sustainable Development Goal (Clean Water and Sanitation, SDG#6) and promotes long-term sustainable planning in water-stressed areas by integrating spatial machine learning mapping and health risk assessment. Full article

Wildfires are a major natural hazard causing extensive ecological damage and endangering human survival. Previous studies on wildfires in China have mostly focused on specific regions or individual drivers, with limited systematic assessments at the long-term and national scales. The spatiotemporal patterns of wildfires and their multiple driving mechanisms under China’s diverse climatic regimes remain insufficiently understood. To bridge this gap, we combined MCD64A1 burned area data (2001–2023) with multi-source natural (meteorological, vegetation, and topographic) and anthropogenic factors, using random forest models at both the national and regional scales to examine the spatiotemporal patterns, dominant drivers, and response mechanisms of wildfires in China. The results revealed that: (1) Spatially, wildfires were concentrated in northeastern and southern China, which accounted for 86.20% of the total burned area. Temporally, northern wildfires were primarily a spring-dominated fire regime, with peak activity in March and April, whereas southern wildfires were winter-dominated, peaking in February. (2) At the national scale, elevation was the key topographic factor influencing wildfire occurrence (relative importance = 0.49), with low-elevation and gentle-slope areas being more fire-prone. At the regional scale, the driving factors exhibit spatial differentiation, forming a spatial pattern of topography-dominated and climate-dominated. (3) Partial dependence plot analysis revealed nonlinear and threshold responses. Fire probability increases rapidly when the soil moisture is below 20 mm, while extremely high land surface temperatures in arid regions suppress fire occurrence due to fuel limitations. This study enhances the understanding of spatially heterogeneous wildfire drivers in China and provides a scientific basis for region-specific wildfire prevention and management strategies. Full article

Background: This study aims to evaluate the real-world efficacy and safety of dalpiciclib in patients with hormone receptor-positive (HR+) advanced breast cancer and explore the impact of different clinical characteristics on treatment outcomes. Methods: This was a two-center, retrospective cohort study involving 76 patients treated with dalpiciclib between January 2022 and June 2024 at two affiliated hospitals of Anhui Medical University in China. Data on progression-free survival (PFS), adverse events, and key clinical factors were collected and analyzed. Kaplan–Meier estimates were used for statistical analysis. Results: The median PFS (mPFS) for the entire cohort was 12.00 months (95% CI: 10.09–13.91 months). Patients receiving dalpiciclib as first-line therapy had significantly better outcomes (mPFS: 17.00 months, 95% CI: 9.19–24.81 months) than those receiving later-line therapy (p < 0.001). Patients with prior exposure to cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) and those with endocrine resistance had poorer outcomes. Multivariate Cox proportional hazards regression analysis confirmed that earlier treatment line (HR for second-line vs. first-line: 3.89, p = 0.015; HR for third-line or later vs. first-line: 5.56, p = 0.006) and prior CDK4/6i treatment (HR = 3.42, p = 0.040) were independent predictors of PFS. The most common adverse events were hematologic toxicities, including leukopenia (76.6%) and neutropenia (72.4%), mostly grade 1–2. No febrile neutropenia cases were reported, indicating a manageable safety profile. Conclusions: Dalpiciclib combined with endocrine therapy is associated with favorable efficacy and safety in real-world settings, with early-line treatment and lower tumor proliferative activity associated with better outcomes. While findings suggest potential for clinical application, further large-scale prospective studies are needed to validate its effectiveness in different patient subgroups and optimize treatment strategies. Full article

Combine harvesters are core modern grain production equipment with high reliability, critical for food security. Yet their metal parts suffer severe grain-induced wear during operation, directly reducing efficiency, increasing grain loss, and raising maintenance costs and environmental burdens. This paper clarifies the grain-induced wear source characteristics and the dominant mechanisms and hazards for combine harvester metal surfaces, as well as summarizes the research progress of four key protection strategies: wear-resistant materials, surface engineering, structural and parameter optimization, and maintenance and remanufacturing. Based on the latest research data, the working principles, performance advantages and application scenarios of various protective technologies were analyzed. Current research faces several challenges: insufficient systematic wear data for multiple crops, unclear multi-factor coupled wear mechanisms, limited low-cost and long-lasting protective technologies, and the absence of online wear monitoring techniques. Finally, the directions for future research focus, such as the systematic research on the wear characteristics of multiple crops, the deepening of the wear mechanism of multi-factor coupling, the development of green, low-cost and long-term protection technologies, and the development of online wear monitoring and active control systems, are explored, providing theoretical support and technical reference for the transformation of wear control in combine harvesters, from passive maintenance to active protection throughout the entire life cycle. Such future work supports the high-quality development of agricultural mechanization and ensures food security. Full article

The risks associated with hazardous materials (HazMat) transportation exhibit typical characteristics of chain-like distribution, spatiotemporal regularity, and individual heterogeneity. A personalized trip-chain feature spectra recognition framework for HazMat vehicles is proposed to enhance the capability to assess and analyze individual risks using vehicle positioning data. The proposed framework addresses the challenges of deriving personalized risk feature maps arising from missing real-time trajectory data, complex sub-trip-chain segmentation, and the extraction of personalized risk feature representations. An improved conditional Wasserstein Generative Adversarial Network (WGAN) model is initially developed to impute trajectories with missing positional data, and it can robustly reconstruct trajectories with large-scale missing segments by integrating a multi-head self-attention mechanism and a gradient penalty. A two-layer clustering algorithm, K-Means-multiplE-THreshOlds-adaptive-DBSCAN (KMETHOD), which combines an adaptive mechanism with threshold rules, is subsequently designed to identify the dwell time and related spatial attributes of dwell points along vehicle trips. A BERT-based model is incorporated to filter Points of Interest (POIs) around dwell points, which enables the extraction of their detailed location semantics and trip characteristics and thus supports trip chain identification and segmentation. A threshold-activated multilayer trajectory feature-map method (TAFEM) is constructed to generate feature maps for each trip chain. The Liquefied Natural Gas (LNG) transportation trajectory data from Guangdong Province is selected to evaluate the effectiveness of the proposed methods. The experimental results demonstrate that the proposed framework can effectively identify trip chains and generate their corresponding feature maps. The trajectory imputation model achieved the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Dynamic Time Warping (DTW) of 2.34–3.33, 6.05–7.74, and 0.74–1.21, respectively, across different missing-rate scenarios, outperforming other benchmark models. The identification accuracy of dwell-point duration and location reaches 98.35%. The BERT-based method achieves a maximum accuracy of 92.83% in origin–destination (OD) point recognition, effectively capturing comprehensive trip-chain information. TAFEM accurately characterizes the spatiotemporal distribution and potential causal factors of personalized HazMat transportation safety risks, providing a reliable foundation for risk identification and safety management strategies. Full article

Background: Vandetanib and cabozantinib are the approved first-line antiangiogenic multikinase inhibitors (aaMKIs) for metastatic medullary thyroid carcinoma (MTC); however, real-world data on their comparative efficacy, optimal sequencing, and outcomes beyond the first-line setting remain limited. We report multicenter real-world outcomes from a large Turkish cohort. Methods: In this retrospective multicenter cohort study, we analyzed data from 24 oncology referral centers across Türkiye. Patients with histologically confirmed metastatic MTC who received systemic therapy between December 2011 and December 2024 were included. The primary endpoint was progression-free survival (PFS), assessed separately for first-line (PFS1) and second-line (PFS2) therapy. Overall survival (OS) and prognostic factors were evaluated using Kaplan–Meier and Cox proportional hazards analyses. Results: A total of 115 patients were included (median age 47.4 years; 63.5% male). In the first-line setting, vandetanib (47.8%) and cabozantinib (30.4%) were the most frequently used agents. Median PFS1 was 40.8 months with vandetanib and was not reached with cabozantinib; both were significantly superior to chemotherapy (median PFS1 4.9 months; log-rank p < 0.001). In the second-line setting, median PFS2 was not reached with cabozantinib and was 32.5 months with vandetanib. Sequential use of cabozantinib and vandetanib across the first two lines was associated with a median time to second progression of 114 months, compared with 39 months in patients receiving any other TKI combination (p = 0.003). Second-line use of cabozantinib or vandetanib was independently associated with improved OS (HR 0.40, 95% CI 0.16–0.98; p = 0.046). On multivariate analysis, younger age (HR 0.16, 95% CI 0.03–0.72; p = 0.017) and bone metastasis (HR 0.29, 95% CI 0.11–0.73; p = 0.009) were independent prognostic factors for OS. Conclusions: In this real-world cohort of patients with metastatic MTC, cabozantinib and vandetanib demonstrated durable efficacy across treatment lines, substantially outperforming alternative TKIs and chemotherapy. Sequential use of both approved aaMKIs was associated with prolonged disease control. These findings suggest a potential association between access to both agents and improved outcomes. They are consistent with their central role in treatment sequencing, particularly in settings with limited access to selective RET inhibitors. Given the retrospective design and small subgroup sizes, these results should be interpreted as exploratory and hypothesis-generating. Full article

Modern underground hard coal mines encounter increasing natural hazards as mining depth increases, including, in particular, significant rises in both methane and thermal hazards. Thermal threats are common in Polish mines, especially in areas where the primary rock temperature exceeds 40 °C. To provide an energy source for cooling systems and reduce methane emissions from ventilation air, a system based on a catalytic reactor combined with an absorption chiller was developed. Field tests conducted at the experimental mine Barbara in Mikołów (Poland) indicate that a COP based on methane chemical energy can reach a level of 0.3–0.4. An application analysis was conducted based on the results of cross-sectional forecasts of climatic conditions (thermal conditions forecasts). The results indicate the potential for using this installation as a supporting component of mine cooling systems. An important factor that may limit the efficiency of the installation is the volume flow of the exhaust air stream. It is estimated that, in countries where, as in Poland, air temperature is the primary criterion for assessing thermal safety, the results of the analysis would be similar. Full article

Background: First-line chemoimmunotherapy (I + C) is the standard of care for advanced non-squamous non-small cell lung cancer (NSCLC) without oncogenic mutation. Bevacizumab has been shown to enhance the efficacy of chemotherapy in non-squamous NSCLC, yet its added value when combined with I + C (I + C + B) remains unclear. To address this gap, we conducted a real-world comparative study and a network meta-analysis to evaluate I + C + B versus I + C in this setting. Methods: This retrospective study included patients with advanced EGFR/ALK-negative non-squamous NSCLC treated with first-line I + C + B or I + C. Propensity score matching (PSM) was employed to balance baseline characteristics between groups. Efficacy endpoints were progression-free survival (PFS) and overall survival (OS). Subgroup analyses examined outcomes by PD-L1 expression, age, metastases, and chemotherapy, among other factors. In parallel, a network meta-analysis of four randomized trials (n = 2026) indirectly compared I + C + B against I + C for PFS, OS, and safety outcomes. Results: A total of 277 patients were included, with 167 (60.3%) receiving I + C + B and 110 (39.7%) receiving I + C. Before PSM, the I + C + B regimen significantly prolonged PFS versus I + C (hazard ratio [HR] = 0.69, 95% CI 0.52–0.92, p = 0.010), with this benefit maintaining post-matching (HR = 0.70, 95% CI 0.49–0.99, p = 0.045). However, OS did not differ significantly between groups in either the pre-PSM (HR = 0.93, 95% CI: 0.67–1.30; p = 0.665) or matched analyses (HR = 0.84, 95% CI: 0.54–1.29; p = 0.421). Subgroup analyses suggested greater PFS benefit from I + C + B among PD-L1-negative, older patients, those with brain metastases or multiple metastatic sites, and in patients receiving specific chemotherapy doublets. The network meta-analysis confirmed a PFS advantage for I + C + B over I + C (HR = 0.84, 95% CI: 0.71–0.98) without an OS benefit (HR = 0.95, 95% CI: 0.79–1.14). Toxicity was higher with I + C + B; rates of grade 3–5 adverse events, serious adverse events, and treatment discontinuation were all significantly increased compared to I + C. Conclusions: In the first-line treatment of advanced EGFR/ALK-negative non-squamous NSCLC, adding bevacizumab to I + C improved PFS but did not translate into an OS gain. Although PFS benefits were observed in certain subgroups, these were accompanied by significantly increased treatment-related toxicities. Our findings suggest that no clear subgroup has been identified where the benefit outweighs the risks, necessitating extreme clinical caution. Full article

To address the rock burst safety hazards encountered during coal seam mining in coal pillar areas under complex geological conditions and ensure sustainable and stable mine production, this study investigates the coal pillar area of a ventilation shaft in a mining area. Through an integrated approach incorporating field investigation, laboratory testing, numerical simulation, and engineering analogy, systematic research was conducted on rock burst mechanisms, geological modeling, and risk assessment. The results indicate that rock bursts in this coal pillar area represent tectonic-type disasters dominated by tectonic stress and induced by multi-factor coupling, with the coal seam exhibiting weak burst proneness. Based on a refined three-dimensional geological model constructed from borehole data, combined with mesh optimization and FDEM (Finite-Discrete Element Method) numerical simulations, precise delineation of rock burst hazard zones was achieved. These findings provide theoretical foundations and technical paradigms for safe mining operations in coal pillar area as under similar complex geological conditions, contributing to the sustainable development of coal resources through enhanced safety, extended mine service life, and optimized resource utilization. Full article

Disaster Risk Management (DRM) for cultural heritage is increasingly recognized as a global priority, yet methodological harmonization and conceptual inconsistencies continue to hinder its effective implementation. This study develops and tests an integrated framework for Disaster Risk Assessment (DRA) applied to the Arian Baptistery of Ravenna—part of the UNESCO World Heritage property Early Christian Monuments of Ravenna since 1996. By combining elements from the ICCROM ABC Method, the IPCC/UNDRR conceptual models, and the QuiskScan model associated with the Nara Grid for value assessment, the research identifies the essential data, definitions, and conditions required to prepare a coherent risk knowledge base. The workflow includes five main steps: context analysis, stakeholder mapping, value assessment, terminological alignment, and risk components systematization. Results demonstrate that effective DRA depends not only on technical assessment of hazards but also on the integration of social, institutional, and governance factors that shape vulnerability. The study also proposes a hybrid hazard framework combining ICCROM’s Ten Agents of Deterioration with the UNDRR 2025 List of Hazards, expanding the concept of “dissociation” to include governance failures and socio-political risks. The Arian Baptistery thus serves as both a case study and a methodological laboratory, offering a replicable model for organizing knowledge, harmonizing terminology, and bridging disciplinary divides in cultural heritage risk management. Full article