Search Results (46)

Background/Objectives: Clazosentan reduces cerebral vasospasm and delayed cerebral ischemia following aneurysmal subarachnoid hemorrhage (aSAH). However, concerns that clazosentan may cause cerebral edema and increase intracranial pressure (ICP) have discouraged its use in patients with poor-grade aSAH. Methods: This single-center retrospective study included 59 patients with aSAH who underwent continuous direct ICP monitoring, of whom 23 received clazosentan and 24 received fasudil. Most clazosentan-treated patients had poor-grade aSAH (WFNS 4–5, 95.7%). The primary outcome was the change in mean daily ICP in the clazosentan group between the pre-treatment and full treatment periods, with a secondary analysis restricted to the first 7 days and stratified by the presence of intracerebral hematoma (ICH). Cerebral perfusion pressure (CPP) changes were assessed as secondary outcomes, and the fasudil group served as an exploratory comparator. Results: Among 21 clazosentan-treated patients available for primary analysis, median ICP did not change significantly between the pre-treatment and treatment periods (6.4 vs. 8.4 mmHg; median ΔICP +0.5 mmHg, 95% CI −1.74 to +2.64; p = 0.708), a finding consistent in the first 7 days and across subgroups by ICH status. In contrast, CPP increased significantly between the pre-treatment and treatment periods (69.1 vs. 84.5 mmHg; median ΔCPP +14.0 mmHg, 95% CI +8.93 to +17.44; p < 0.001). Conclusions: In this retrospective single-center cohort, no statistically significant ICP elevation was observed during clazosentan administration under standard neurosurgical management. Although the small sample size limits statistical power and a false-negative result cannot be excluded, these findings may help inform clinical decision-making regarding clazosentan use in patients with poor-grade aSAH. Full article

Elevated potentially toxic element (PTE) contents in soils can pose important environmental concerns, particularly in regions where natural metal enrichment associated with ultramafic and mineralized geological settings overlaps with potential anthropogenic inputs. This study evaluates the distribution, enrichment, and probable controls of As, Ni, Co, Cu, Cr, Pb, and Zn in 35 geo-referenced soil samples from the Ortaklar region, Türkiye, with the aim of providing a geochemical basis for environmental monitoring and sustainable land management. PTE contents were determined by ICP-MS (inductively coupled plasma mass spectrometry), and their degree of enrichment was evaluated using the geo-accumulation index (I_geo), contamination factor (CF), and pollution load index (PLI), supported by correlation analysis and multivariate statistical approaches. Mean contents decreased in the order Ni (1972 mg kg⁻¹) > Cr (924 mg kg⁻¹) > Cu (290 mg kg⁻¹) > Zn (188 mg kg⁻¹) > Co (122 mg kg⁻¹) > As (15.9 mg kg⁻¹) > Pb (6.8 mg kg⁻¹). Ni exhibited the highest degree of enrichment and was the dominant contributor to the overall PTE load, while Cr, Cu, and Co also showed substantial enrichment. Mean I_geo values indicated moderate to heavy enrichment for Ni (2.65) and As (2.36), and moderate enrichment for Cr (1.45) and Co (1.02). The mean PLI value of 6.99 indicates a substantial cumulative enrichment of PTEs relative to the adopted reference values. Strong associations among Ni, Cr, and Co are consistent with a dominant geogenic contribution from ultramafic/serpentinitic parent materials and their weathering products, whereas the distributions of As, Cu, and Zn suggest more complex controls involving lithology, mineralization, and potentially localized agricultural or other anthropogenic inputs. However, these statistical relationships indicate probable controls rather than definitively establishing elemental sources. The elevated total PTE contents and contamination indices identify the Ortaklar soils as requiring continued geochemical monitoring; nevertheless, they do not constitute direct evidence of human-health risk because metal speciation, mobility, bioavailability, and exposure pathways were not investigated. The findings provide a regional geochemical framework for distinguishing natural PTE enrichment from anthropogenic influences and for guiding future mineralogical, bioavailability, and spatial investigations in ultramafic and mineralized soil environments. Full article

Background: Intracranial pressure (ICP) monitoring remains the cornerstone of neurocritical care in severe traumatic brain injury (TBI), yet its prognostic value as a standalone metric is limited. The Pressure Reactivity Index (PRx), a continuous measure of cerebrovascular reactivity derived from ICP and arterial blood pressure, may offer additional or complementary prognostic information. This systematic review aimed to compare the prognostic performance of PRx-derived metrics versus standard ICP monitoring for mortality and functional outcome in patients with TBI. Methods: A systematic search of PubMed, Web of Science, and Scopus was conducted for studies published between January 2000 and December 2025. Studies were eligible if they included adult TBI patients with continuous multimodal monitoring and reported comparative prognostic data for PRx- and ICP-based metrics. Risk of bias within the studies was appraised via the QUIPS tool, and the GRADE system was used to rate the strength of the evidence. Due to methodological heterogeneity, findings were synthesized narratively. Results: Nine studies were included. Applying a maximum-cohort estimation to account for overlapping registries, the pooled sample comprised a minimum of 1240 unique patients. In the majority of included studies, direct within-cohort head-to-head comparisons demonstrated that specific PRx-derived metrics—such as the individualized ICP threshold (iICP), Longest Continuous Duration of Autoregulatory Impairment (LCAI), Lower Limit of Reactivity (LLR), and time-integrated burdens (%Time > Threshold)—yielded stronger prognostic discrimination compared to standard ICP thresholds for both mortality (PRx: AUC 0.747–0.648 and ICP: AUC 0.660–0.614) and functional outcome. When added to established predictive models, PRx-derived metrics provided clinically meaningful incremental improvements in prognostic accuracy, with descriptive incremental AUC gains ranging from +0.039 to +0.170 across the six studies reporting model augmentation. Due to heterogeneity in baseline models, PRx-derived metrics, and patient populations, these findings are presented strictly as a descriptive range. Conclusions: PRx and PRx-derived cerebrovascular reactivity metrics-namely iICP, LCAI, LLR, and time-integrated burdens of autoregulatory failure—show potential to offer additive prognostic value beyond standard ICP monitoring in severe TBI. However, because current evidence is strictly observational and likely influenced by institutional confounders, it cannot currently support definitive clinical recommendations. Further prospective, multicenter studies utilizing standardized thresholds are necessary to confirm these associative findings and isolate their true prognostic value. Full article

Ketosis is one of the most economically significant metabolic disorders affecting periparturient dairy cows, causing production losses and predisposing animals to secondary complications. Current blood-based diagnostics are invasive and provide limited insight into the underlying metabolic perturbations. This study employed an integrated three-platform metabolomics approach to characterize milk metabolite alterations in ketotic Holstein dairy cows and to evaluate milk-based biomarker panels for early ketosis detection. Milk samples from 20 healthy control (CON) cows and 6 ketotic cows were collected at 2 weeks postpartum and analyzed by direct injection/liquid chromatography–tandem mass spectrometry (DI/LC-MS/MS), proton nuclear magnetic resonance (¹H-NMR) spectroscopy, and inductively coupled plasma mass spectrometry (ICP-MS). Ketosis was confirmed by serum β-hydroxybutyrate concentrations ≥ 1400 μmol/L. Principal component analysis, partial least squares-discriminant analysis, and receiver operating characteristic (ROC) curve analyses were applied. All three platforms discriminated ketotic cows from healthy cows, with clear cluster separation validated by 2000 permutation tests (p < 0.05). DI/LC-MS/MS identified 16 significantly altered metabolites (p < 0.05), with butyrylcarnitine (C4), phosphatidylcholine 30:0 (PC 30:0), ether-linked phosphatidylcholine O-38:3 (PC O-38:3), and citrulline identified as the top discriminatory biomarkers (AUC = 0.920; 95% CI: 0.85–0.98; sensitivity = 91.7%; specificity = 93.3%). ICP-MS revealed significantly reduced selenium (Se, p = 0.017), manganese (Mn, p = 0.045), and chromium (Cr, p = 0.037), as well as elevated cobalt (Co, p = 0.014) in ketotic milk (AUC = 0.870). ¹H-NMR detected no individually significant metabolites; however, multivariate analysis distinguished groups (AUC = 0.890), with succinate (numerical fold change: +5.77×; p = 0.059), methanol (−1.94×; not significant), and acetate (+2.88×; not significant) as top VIP contributors. The combined multi-platform biomarker panel (joint classification using top VIP features from all three platforms, without formal data fusion) achieved superior diagnostic performance (AUC = 0.970; 95% CI: 0.93–1.00; sensitivity = 95.0%; specificity = 96.7%). These findings identify coordinated perturbations in glycerophospholipid metabolism, acylcarnitine profiles, amino acid homeostasis, antioxidant mineral status, and energy metabolism during early ketosis, and suggest that milk metabolomics is a promising non-invasive approach for precision dairy health monitoring, pending validation in independent cohorts. We acknowledge the small ketotic group size (n = 6) as a limitation; therefore, these findings should be considered discovery cohort observations requiring prospective validation before clinical translation. Full article

Terrestrial laser scanning (TLS) provides dense point clouds for landslide monitoring, yet occlusion, heterogeneous point density, and seasonal vegetation introduce noise and unstable deformation boundaries in multi-temporal change detection. To overcome the limitations of the multiscale model-to-model cloud comparison (M3C2) method under dominant downslope tangential motion and vegetation disturbance, we propose a block-wise ICP method to retrieve 3D displacement vectors. The scene is partitioned into local sub-blocks; rigid registration is performed within each sub-block, and the estimated translation is assigned to the sub-block center. A two-stage matching and quality control procedure removes under-constrained sub-blocks, enabling the direct retrieval of 3D displacement vectors and interpretable boundaries. Applied to the Longxigou landslide in Wenchuan using RIEGL VZ-2000i surveys on 1 November 2023 and 23 May 2024, the proposed method produces a more continuous displacement field and clearer boundaries than M3C2. For a tower target, manual measurements indicate a displacement of 0.41–0.63 m; our estimates are within 0.33–0.40 m, whereas M3C2 mostly falls between −0.25 and 0.25 m. In a seasonal vegetation change scene, we detect a canopy envelope expansion of approximately 0.20–0.40 m, while M3C2 shows scattered canopy responses that hinder boundary interpretation. A sensitivity analysis indicates a block-scale trade-off between boundary stability and peak preservation, motivating adaptive multi-scale blocking and uncertainty quantification. Full article

Sediment management is defined as the strategic monitoring and control of erosion, transport, and deposition processes to maintain environmental and infrastructural stability. Terrestrial laser scanning (TLS) has emerged as a critical high-precision technology for monitoring sediment dynamics, erosion processes, and geomorphic change detection across diverse environments, including riverine, coastal, watershed, and infrastructure-related landscapes. While the field of TLS technology has seen significant advancements in recent years, including improvements in data accuracy, enhanced operational performance, artificial intelligence (AI), machine learning-based processing, and integration with other remote sensing tools such as unmanned aerial vehicles (UAVs) and satellite light detection and ranging (LiDAR), the study has focused on these developments. These advancements have further extended the application prospects of TLS technology. Despite these advancements, there remains a crucial need to systematically identify global research trends to identify the effectiveness, limitations, and knowledge gaps of TLS in sediment management. The methodological advantages and challenges of TLS applications provide insights into its gradual development role in enhancing sediment monitoring and environmental resilience. The objective of this study is to synthesize the current state of sediment management by conducting a systematic review of 108 peer-reviewed research papers retrieved from academic databases, including Google Scholar, ResearchGate, ScienceDirect, Scopus, and Web of Science, from 28 countries, published between 2000 and 2025. The study will evaluate the effectiveness of TLS methodologies in comparison to conventional techniques and management procedures, following the PRISMA 2020 guidelines. It will examine their capacity to enhance measurement accuracy, reduce error margins, and improve structural guidelines, particularly by advancing TLS technology through the integration of AI and machine learning (ML) algorithms. The findings of the study indicate that TLS and Iterative Closest Point (ICP) techniques can enhance the analysis of 3D models of dam deformation, ensuring improved structural monitoring and safety. The findings offer insights into the evolving role of TLS in sediment monitoring, emphasizing its potential for enhancing environmental management and climate resilience strategies. Furthermore, this review identifies future research directions to optimize TLS applications in sediment management through interdisciplinary approaches. Full article

This study investigates the characterisation of particulate matter (PM) across different size fractions (TSP, PM₁₀, PM₄, PM_2.5, and PM₁) in Porto, Portugal, over a 2-year period. Sampling was conducted at two heights (ground level and rooftop), integrating real-time measurements and filter-based analyses to evaluate seasonal and spatial variations. Elemental composition was determined using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS), enabling detailed assessments of 30 chemical elements. Meteorological parameters, including temperature, precipitation, wind speed, and direction, were analysed to understand their influence on PM concentrations. Results indicate that significant seasonal trends, with higher PM concentrations observed during autumn and winter, were associated with low boundary layer height, promoting greater mixing of particles, enhanced deposition, and higher anthropogenic emissions, with average seasonal TSP values ranging from 0.001 to 0.059 µg m⁻³. Elemental analysis revealed distinct profiles at ground and rooftop levels, with Ba, Cu, Pb, Mg, and Na among the most frequently detected elements; ground-level samples showed stronger contributions from local sources, such as traffic, while rooftop samples reflected regional and long-range transport. Meteorological factors, such as precipitation and wind speed, exhibited negative correlations with PM concentrations, underscoring their role in atmospheric washing. These findings highlight the complex interplay of local and regional factors in shaping PM dynamics and emphasise the importance of multi-level monitoring for effective air-quality management. Full article

Mining exploitation and copper smelting in Bor (Serbia) have led to long-term environmental pollution with toxic metals, primarily copper (Cu) and arsenic (As). The aim of this research was to assess the contamination of arable land and the bioaccumulation of metals in wheat (Triticum aestivum L.), to determine significant differences in copper and arsenic concentrations between the soil and specific wheat tissues across six locations, and to evaluate environmental and health risks in agricultural areas around the Zijin Copper Mine, Serbia. Sampling was carried out at six locations (Brezonik, Veliki Krivelj, Oštrelj, Slatina, Zlot, and Gornjane; L1–L6, respectively). Analyses of soil and wheat to determine toxic elements were performed using the ICP-MS method, while contamination was assessed using descriptive statistics and a combination of several indices (CV, Igeo, EF, CF, Er, RI, PLI, BAF, TF, and HRA). In addition to Cu and As, accompanying elements (Fe and Al) were also included in the analysis, due to their importance as indicators of geogenic and anthropogenic origin. The analysis of the distribution within the root, stem, leaf, and grain of wheat enabled the assessment of bioaccumulation (BAF and TR) and implications for food safety (HRA). The results showed that concentrations of Cu and As at several locations significantly exceed the regulatory limit values, with Slatina-L4 and Oštrelj-L3 identified as the most polluted areas, while Gornjane-L6 can be considered a reference location with minimal risk. Background values were taken from location L6, considered a reference site due to the absence of direct mining and industrial influence (B_Cu—20 mg/kg, B_As—10 mg/kg, and B_ref·Al—33,300 mg/kg). The MANOVA analysis revealed statistically significant differences in copper and arsenic concentrations between the soil and various wheat tissues, with the effect being more pronounced for arsenic. The integrated analysis of indices (RI and PLI) confirmed the pronounced anthropogenic impact and location-specific risks, emphasizing the need for continuous monitoring, locally adapted remediation strategies, and sustainable land management. Full article

Nine technology-related metal(loid)s (Ag, Co, Fe, Mo, Pt, Rh, Sb, Se and Y) were monitored in 137 topsoil samples from urban parks, industrial areas and gardens in Alcalá de Henares (Spain) using ICP–MS. Selenium was not detected, while Mo, Sb and Rh showed a high proportion of values below the detection limit, indicating generally low contamination. In contrast, Fe, Co and Y were detected in all samples, with industrial soils showing about two-fold higher median Co and Fe than urban soils. Garden soils displayed marked silver enrichment (median 0.439 vs. 0.068 mg kg⁻¹ in urban soils), with Ag pollution indices up to 71 and enrichment factors up to 69; around 17% of garden samples exceeded EF > 40, and more than one-quarter had EF > 10. Principal component analysis suggested a predominantly geogenic association for Co, Fe and Y and an anthropogenic component for Ag, Mo, Rh and Sb, while Pt was mainly linked to vehicular emissions. Under standard US EPA exposure scenarios applied to the 2001 topsoil concentrations, oral and inhalation hazard quotients for elements with available benchmarks remained <0.2 and inhalation cancer risks for Co were ≤2.5 × 10⁻⁷, indicating low estimated risk within the model assumptions. However, quantitative risk characterisation remains constrained by benchmark gaps for Pt and Rh and by limited consensus toxicity values for Y, which introduces uncertainty for these technology-related elements. These results should therefore be interpreted primarily as a baseline (2001) in surface soils for Alcalá de Henares rather than as a direct representation of current exposure conditions. Full article

Ultrasound-assisted microextraction (UAME) has emerged as a powerful and sustainable technique for food chemical contaminant analysis, offering a rapid, efficient, and environmentally friendly alternative to conventional extraction methods. This review provides a comprehensive overview of recent advancements in the application of UAME for the determination of various food chemical contaminants, including pesticide residues, potentially toxic elements, mycotoxins, veterinary drugs, and other chemical contaminants. The fundamental principles of ultrasound-assisted extraction are discussed, with an emphasis on the mechanisms of acoustic cavitation and mass transfer enhancement that enable improved analyte recovery from complex food matrices. Key factors influencing extraction efficiency (solvent selection, ultrasonic frequency and power, extraction time, and sample characteristics) were critically analyzed. Additionally, the integration of UAME with modern analytical platforms, such as LC-MS, GC-MS, and ICP-MS, was explored, highlighting its compatibility with high-throughput and multiresidue detection. Compared with traditional techniques, UAME offers significant benefits, including reduced solvent consumption, shorter extraction times, and improved analytical performance. This review also addresses current limitations and future perspectives, particularly regarding standardization, automation, and application in routine food safety monitoring. Overall, UAME represents a promising direction for more sustainable and efficient food chemical contaminant analysis, aligning with the growing demand for green analytical chemistry approaches. Full article

This paper reviews results published by the International Co-operative Programme on Effects on Materials including Historic and Cultural Monuments (ICP Materials) with emphasis on those obtained after the turn of the century. Data from ICP Materials come from two main sources. The first is through exposures of materials and collection of environmental data in a network of atmospheric exposure test sites mainly distributed across Europe. Corrosion of carbon steel has continued to decrease during the period 2000–2020 but corrosion of zinc only up until 2014, and the trend in zinc corrosion is only visible when examining four-year data. Surface recession of limestone as well as soiling of modern glass show no decreasing trend during 2000–2020. The second is through case studies performed at heritage sites across Europe. Risk analysis of corrosion and soiling for twenty-six sites indicate that currently soiling is a more significant maintenance trigger than corrosion. Costs for maintaining heritage sites are substantial and costs attributable to air pollution is estimated from 40% to as much as 80% of the total cost. Future directions of the program are work on effects of particulate matter, improving the scientific basis for the work, and making the monitoring data publicly available. Full article

This study integrates hyperspectral remote sensing with chemical and pedological data to estimate Zn, Pb, and Cd concentrations in the upper soil layers. Conducted in agricultural fields east and northeast of Celje, Slovenia, an area impacted by past industrial activities such as zinc ore smelting, the research integrates remote sensing and soil sampling to rapidly identify and map soil pollution over large surfaces. A multi-sensor approach was employed, combining two hyperspectral cameras (VNIR and SWIR, aerial), laboratory spectrometry, soil parameters, and content of chemical covariates measured with portable XRF and ICP-OES with a direct comparison of both techniques for this specific purpose. Accurate atmospheric and signal transformations were performed to improve modeling. The importance of covariates was thoroughly evaluated using conditional permutations to assess their contribution to the prediction of metal concentrations. The proposed framework utilizes spectral data and privileged information during training, improving prediction accuracy through a multi-stage model architecture. Here, a base model trained on spectral data is corrected using privileged information. During inference, the model functions without relying on privileged data providing a scalable and cost-effective solution for large-scale environmental monitoring. Our model achieved a reduction of predicted RMSE for Zn and Cd maps in comparison to the baseline models, translating to more precise identification of possibly polluted zones. However, for Pb, no improvements were observed, potentially due to variability in the data, including spectral issues or imbalances in the training and test datasets. Full article

Mineral elements can either be pollutants or essential dietary components. Monitoring their levels in the environment and living organisms is crucial because excessive amounts can become toxic. Dogs, due to their proximity to humans, shared habitats, and similar organ structures, can be effective indicators of environmental pollution by toxic elements. This study aimed to assess the levels of 11 mineral elements in 80 dog carcasses (49 males and 31 females), aged between 2 and 16 years, from the Ionian-Etnean volcanic region of the province of Catania, where the dogs had died under unknown circumstances. A direct mercury analyzer (DMA-80) was used to measure Hg, and an inductively coupled plasma mass spectrometer (ICP-MS) was used for the other elements. A one-way ANOVA, followed by Bonferroni’s multiple comparison for post hoc analysis, was conducted to evaluate significant differences between the organ samples and different minerals and between the weight and metal levels. The statistical significance was set at p < 0.05. The study indicates that high concentrations of metals like cadmium, mercury, lead, and chromium are present in the liver, kidneys, and other organs. These elevated concentrations suggest that the local volcanic emissions contribute to soil, water, and atmospheric contamination. The data showed differences in the metal concentrations between the sexes, which could be attributed to biological and environmental factors. Full article

Heavy metal ion (HMI) contamination poses significant threats to public health and environmental safety, necessitating advanced detection technologies that are rapid, sensitive, and field-deployable. While conventional methods like atomic absorption spectroscopy (AAS) and inductively coupled plasma mass spectrometry (ICP-MS) remain prevalent, their limitations—including high costs, complex workflows, and lack of portability—underscore the urgent need for innovative alternatives. This review consolidates advancements in the last five years in microfluidic technologies for HMI detection, emphasizing their transformative potential through miniaturization, integration, and automation. We critically evaluate the synergy of microfluidics with cutting-edge materials (e.g., graphene and quantum dots) and detection mechanisms (electrochemical, optical, and colorimetric), enabling ultra-trace detection at parts-per-billion (ppb) levels. We highlight novel device architectures, such as polydimethylsiloxane (PDMS)-based labs-on-chip (LOCs), paper-based microfluidics, 3D-printed systems, and digital microfluidics (DMF), which offer unparalleled portability, cost-effectiveness, and multiplexing capabilities. Additionally, we address persistent challenges (e.g., selectivity and scalability) and propose future directions, including AI integration and sustainable fabrication. By bridging gaps between laboratory research and practical deployment, this review provides a roadmap for next-generation microfluidic solutions, positioning them as indispensable tools for global HMI monitoring. Full article

Soil contamination by potentially toxic elements (PTEs) poses a major environmental concern. The distribution and concentration of these elements can vary significantly in polluted areas, making detailed assessments crucial. A comprehensive analysis is essential to accurately characterise contamination patterns, as a foundation for effective site evaluation and remediation efforts. This study evaluates the effectiveness and reliability of X-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry (ICP-MS) for determining PTEs in soil samples. Statistical analyses reveal significant differences between the two techniques for Sr, Ni, Cr, V, As, and Zn, likely due to variations in detection sensitivity, calibration methods, or matrix effects. Pb exhibits a weaker difference, suggesting a potential, yet statistically insignificant, difference between methods. Correlation analyses indicate a strong linear relationship for Ni and Cr, while Zn and Sr display high variability, limiting direct comparability. Bland–Altman plots highlight systematic biases, particularly for V, where XRF consistently underestimates concentrations compared to ICP-MS. These findings underscore the importance of selecting the appropriate analytical technique based on detection limits, sample characteristics, and measurement reliability. While both methods provide valuable insights for environmental monitoring, carefully considering their limitations is crucial for accurate contamination assessment. Full article