Search Results (954)

Ready-to-eat (RTE) foods can carry antimicrobial-resistant pathogens; however, few studies link real-world surveillance to practical interventions. This study addressed this gap by estimating the prevalence of Staphylococcus aureus (S. aureus) and methicillin-resistant S. aureus (MRSA) in ready-to-eat foods from Al-Qassim and evaluating a rapid, orthogonal confirmation workflow (culture → MALDI-TOF MS → Vitek 2 → mecA/mecC PCR). The in vitro activity of citrate-stabilized silver nanoparticles (AgNPs) against food-derived MRSA was quantified, and synergy with oxacillin (primary) and ciprofloxacin (secondary) was examined. Silver-susceptibility stability was assessed over 20 days of sub-MIC serial passage, with attention to whether β-lactam co-exposure constrained drift. We surveyed 149 RTE products and paired the confirmation workflow with mechanistic tests of AgNPs as antibiotic adjuvants. S. aureus was recovered from 24.2% of products and MRSA from 6.7%, with higher recovery from animal-source matrices and street-vendor outlets. MALDI-TOF MS provided rapid species confirmation and revealed two reproducible low-mass peaks (m/z 3990 and 4125) associated with MRSA, supporting spectral triage pending molecular confirmation. Antimicrobial susceptibility testing showed the expected β-lactam split (MRSA oxacillin/cefoxitin non-susceptible; MSSA oxacillin-susceptible but largely penicillin-resistant), with last-line agents retained. Citrate-stabilized AgNPs displayed consistent potency against food-derived MRSA (MIC 8–32 µg/mL; MIC₅₀ 16; MIC₉₀ 32) and were predominantly bactericidal (MBC/MIC ≤ 4 in 90%). Checkerboards demonstrated frequent AgNP–oxacillin synergy (median fractional inhibitory concentration index [FICI] 0.37; 4–16-fold oxacillin MIC reductions) and additive-to-synergistic effects with ciprofloxacin (median FICI 0.63), translating time–kill assays into rapid, sustained bactericidal activity without antagonism. During sub-MIC evolution, silver MICs rose modestly (median two-fold) and often regressed off drug; oxacillin co-exposure limited drift. RTE foods therefore represent credible MRSA exposure routes. Integrating MALDI-assisted triage with automated AST enables scalable surveillance, and standardized AgNP formulations emerge as promising β-lactam adjuvants—pending in situ efficacy, safety, and residue evaluation. Full article

Urbanization reshapes bird communities by filtering species according to their ecological traits, often reducing richness, altering relative abundances, and favoring a subset of functionally tolerant species that dominate urban assemblages. Some native taxa are able to inhabit cities, even using them as stepping stones for range expansion. We examined urban habitat use, microhabitat selection, and potential niche partitioning between two range-expanding thrushes (Austral Thrush [Turdus falcklandii] and Rufous-bellied Thrush [Turdus rufiventris]) in two urban settlements in the Pampas region, Argentina. Using 131 transects across green areas and urbanized zones, we related abundance patterns to habitat features at the transect scale and evaluated microhabitat selection at the individual level. Austral Thrush abundance increased with herbaceous cover, tree cover, and even concrete surfaces, suggesting a relatively high tolerance to fragmented green spaces within dense urban matrices. In contrast, Rufous-bellied Thrush showed a positive association with tree cover, avoided tall buildings, and reached higher abundance in the smaller city, consistent with its recent arrival in the region and preference for less intensively urbanized environments. Microhabitat data revealed marked vertical stratification: Austral Thrush foraged almost exclusively at ground level on grassy or bare substrates, while Rufous-bellied Thrush used trees, shrubs, and vines more frequently. These differences reflect fine-scale resource partitioning that may contribute to reducing niche overlap and favor the coexistence of both species in recently colonized urban areas, while recognizing that such dynamics occur within broader bird assemblages where multiple species interact and compete for space and resources. Our findings highlight that even closely related species can respond divergently to urban structure, and that maintaining structural and substrate heterogeneity within cities may help support native bird diversity. Full article

The comprehensive (“universal”) PFAS ban proposed by the EU raises questions of a scientific, philosophical, regulatory, and policy nature. This overview scrutinizes the proposal and finds it wanting in each of these respects. The grouping of all PFAS is based on a methodology that is only loosely science based. The scientific data on PFAS do not support the open-ended definition and the drastic restrictions that would be imposed by the proposal. To illustrate the weak scientific basis, we look more closely at the immunotoxicity claims as found in a few landmark epidemiological papers. We find these claims not well-founded and methodologically lacking. Also, we scrutinize a few animal studies and comment on their results. Detecting PFAS in multiple matrices is briefly looked at. The analytical context of detectability, quantifiability, measurement precision, and reproducibility of results of PFAS present in especially complex matrices (e.g., foods, soil, waxes, and fats) poses quite the challenge. Experimental uncertainties are reported to be extremely high. Disregarding the key distinctions between hazard and risk and between potential hazard and hazard, the proposal treats all PFASs, broadly defined, as presenting hazards based on an assumed common property: persistence. On this and other grounds, including the requirement of “unacceptable risk”, the proposed ban fails to meet the requirements imposed by the REACH Regulation. From a public policy perspective, the costs of the proposal would appear to eclipse any advantages for human health and the environment. Thus, we conclude that the proposed PFAS ban is problematic from these key perspectives. Full article

Background/Objectives: Clozapine remains the gold standard for treatment-resistant schizophrenia. However, its narrow therapeutic window and risk of severe side effects require close monitoring of both clozapine and its primary metabolite, norclozapine. Existing therapeutic drug monitoring (TDM) methods are limited by delays, high costs, and operational complexity. This study introduces three rapid point-of-care (POC) assays utilizing a miniature mass spectrometer (Mini-MS) to quantify clozapine and norclozapine in plasma, whole blood, and dried blood spots (DBSs), facilitating applications across diverse clinical settings. Methods: The analytical performance of the assay was evaluated for sensitivity, specificity, reproducibility, and correlation with reference methods. Clinical samples from two hospitals were analysed and validated against conventional liquid chromatography tandem mass spectrometry (LC-MS/MS) reference standards at New South Wales Health Pathology (NSWHP) and Tsinghua University laboratories. Results: The Mini-MS assay accurately quantified both analytes within therapeutic ranges across all matrices. Inter-assay coefficients of variation ranged from 7.9 to 14.1% for clozapine and from 1.6 to 14.6% for norclozapine. Accuracy fell between 85 and 117% in plasma and blood extracts. Strong linearity was demonstrated (R² = 0.98–0.99) over the concentration range of 10–1000 ng/mL. Results from the Mini-MS analysis showed excellent correlations with LC-MS/MS results (r = 0.998). Conclusions: In this proof-of-concept study, the Mini-MS-based POC assays enable rapid, reliable quantification of clozapine and norclozapine, with performance comparable to conventional laboratory methods. This platform supports real-time TDM, facilitating timely dose adjustments, adherence monitoring, and ultimately improving patient outcomes. Full article

Bioavailable fractions of particulate-bound trace elements are key determinants of inhalation toxicity, yet air-quality assessments typically rely on total metal concentrations, which may underestimate health risks. This study integrates the exchangeable (F1) and reducible (F2) fractions of trace elements in fine (PM_2.5) and coarse (PM₁₀) particulate matter with multi-criteria decision-making (TOPSIS) and similarity-based classification (Czekanowski’s method). Archival weekly-integrated samples from the summer season were collected at eight industrially influenced sites in southern Poland. Sequential extraction (F1–F2) and ICP-MS were applied to quantify concentrations of cadmium, cobalt, chromium, nickel, and lead in PM_2.5 and PM₁₀. Aggregated hazard values were then derived with TOPSIS, and site similarity was explored using Czekanowski’s reordered distance matrices. Regulatory targets for cadmium and nickel, and the limit for lead in PM₁₀ were not exceeded, but F1/F2 profiles revealed pronounced site-to-site differences in potential mobility that were not evident from total concentrations. Rankings were consistent across size fractions, with site P₁ exhibiting the lowest hazard indices and P₈ the highest, while mid-rank sites formed reproducible similarity clusters. The proposed chemical-fractionation and multivariate framework provides a reproducible screening tool for multi-element exposure, complementing compliance checks and supporting prioritisation of sites for targeted investigation and environmental management. In the sustainability context, bioavailability-based indicators strengthen air-quality assessment by linking monitoring data with health-relevant and cost-effective management strategies, supporting efficient resource allocation and reducing exposure in vulnerable populations. Full article

This study develops a novel analytical solution for three-dimensional axisymmetric consolidation of unsaturated soils incorporating radial–vertical composite seepage mechanisms and anisotropic permeability characteristics. A groundbreaking dual orthogonal expansion framework is established, utilizing innovative Bessel–trigonometric function coupling to solve the inherently complex spatiotemporal coupled partial differential equations in cylindrical coordinate systems. The mathematical approach synergistically combines modal expansion theory with Laplace transform methodology, achieving simultaneous spatial expansion of gas–liquid two-phase pressure fields through orthogonal function series, thereby transforming the three-dimensional problem into solvable ordinary differential equations. Rigorous validation demonstrates exceptional accuracy with coefficient of determination R² exceeding 0.999 and relative errors below 2% compared to numerical simulations, confirming theoretical correctness and practical applicability. The analytical solutions reveal four critical findings with quantitative engineering implications: (1) dual-directional drainage achieves 28% higher pressure dissipation efficiency than unidirectional drainage, providing design optimization criteria for vertical drainage systems; (2) normalized matric suction variation exhibits characteristic three-stage evolution featuring rapid decline, plateau stabilization, and slow recovery phases, while water phase follows bidirectional inverted S-curve patterns, enabling accurate consolidation behavior prediction under varying saturation conditions; (3) gas-water permeability ratio k_a/k_w spanning 0.1 to 1000 produces two orders of magnitude time compression effect from 10⁻² s to 10⁻⁴ s, offering parametric design methods for construction sequence control; (4) initial pressure gradient parameters λ_a and λ_w demonstrate opposite regulatory mechanisms, where increasing λ_a retards consolidation while λ_w promotes the process, providing differentiated treatment strategies for various geological conditions. The unified framework accommodates both uniform and gradient initial pore pressure distributions, delivering theoretical support for refined embankment engineering design and construction control. Full article

Global supply chains face increasing disruptions from cyber threats, geopolitical instability, extreme weather events, and a range of economic, social, and environmental sustainability challenges. As these disruptions intensify, enhancing Supply Chain Resilience (SCR) has become a strategic priority. This study investigates how Distributed Ledger Technology (DLT) can contribute to SCR by mitigating vulnerabilities and strengthening key capabilities within global supply chains. A qualitative research approach is employed, utilizing expert evaluations to examine DLT’s impact on supply chain vulnerabilities and capabilities. Five workshops were conducted with 25 industry professionals from logistics, IT, procurement, and risk management. Experts examined how DLT could address disruptions stemming from supplier instability, poor traceability, and regulatory and environmental pressures, while highlighting its potential to drive ethical sourcing and environmentally responsible practices. The structured discussions were guided by theoretical frameworks and expert evaluations were synthesized into two analytical matrices illustrating DLT’s influence on SCR. The findings reveal that the contribution of DLT to SCR and sustainability is highly context-dependent, with its effectiveness hinging on how it is embedded within governance structures and aligned with the interplay of complementary technologies. Building on these insights, the study presents the DLT-LFL (Distributed Ledger Technology–Learning Feedback Loop) framework, which integrates sensing, decision-making, adaptation, and predictive learning from distributed operational data, allowing supply chains to better anticipate disruptions, adjust processes dynamically, and continuously strengthen resilience and sustainable practices. The study also develops a practical checklist to assess how effective DLT applications and their integration with predictive and AI-driven analytics reduce vulnerabilities, strengthen capabilities, mitigate risks, and support adaptive decision-making. Full article

Unsymmetrical dimethylhydrazine (1,1-Dimethylhydrazine, UDMH) is widely used as a high-performance liquid rocket propellant for the space industry globally. The release and leakage of UDMH into the environment, especially the soil environment, pose serious threats to ecosystems and human beings. In order to reveal the hazards of UDMH to soil and facilitate subsequent remediation, the adsorption behavior of UDMH in typical soil (yellow-brown soil, red soil, and black soil) matrices was explored, the environmental fate and toxicity of UDMH were presented by simulation calculation, and the phytotoxicity was evaluated by germination assay in the present study. The results showed that the adsorption performance of red soil, yellow-brown soil, and black soil for UDMH increased sequentially by integrating the findings from kinetic and thermodynamic studies. A highly significant correlation between the physicochemical and adsorption parameters for various soil matrices indicated a considerable impact of soil physicochemical properties on the adsorption behavior of UDMH in soils. The environmental fate simulation calculation indicated that UDMH and its transformation products were prone to being dissolved in soil water and migrating; however, once these compounds were present in the surface layer of dry soil, severe ecological and environmental pollution would occur. Based on a thorough evaluation of the toxicity parameters, formaldehyde dimethylhydrazone has been identified as demonstrating the most pronounced environmental toxicity profile, thus warranting prioritized attention. The results of a germination assay demonstrated that more than 100 mg·kg⁻¹ of UDMH in the soil would lead to strong phytotoxicity to plants, and more than 200 mg·kg⁻¹ of UDMH would significantly affect the early germination of seeds. Hence, this research provided helpful insights and theoretical support for the environmental fate and remediation of UDMH. Full article

Epilepsy has diverse seizure types that challenge diagnosis and treatment, requiring automated and accurate classification to improve patient outcomes. Traditional electroencephalogram (EEG)-based diagnosis relies on manual interpretation, which is subjective and inefficient, particularly for multi-class differentiation in imbalanced datasets. This study aims to develop a hybrid framework for automated multi-class seizure type classification using segment-wise EEG processing and multi-band feature engineering to enhance precision and address data challenges. EEG signals from the TUSZ dataset were segmented into 1-s windows with 0.5-s overlaps, followed by the extraction of multi-band features, including statistical measures, sample entropy, wavelet energies, Hurst exponent, and Hjorth parameters. The mutual information (MI) approach was employed to select the optimal features, and seven machine learning models (SVM, KNN, DT, RF, XGBoost, CatBoost, LightGBM) were evaluated via 10-fold stratified cross-validation with a class balancing strategy. The results showed the following: (1) XGBoost achieved the highest performance (accuracy: 0.8710, F1 score: 0.8721, AUC: 0.9797), with $\gamma$-band features dominating importance. (2) Confusion matrices indicated robust discrimination but noted overlaps in focal subtypes. This framework advances seizure type classification by integrating multi-band features and the MI method, which offers a scalable and interpretable tool for supporting clinical epilepsy diagnostics. Full article

In the context of sustainable forest resource development, balancing ecological conservation with rational utilization is essential to achieving forest multifunctionality. Longyuwan National Forest Park, located in Luanchuan County, Henan Province, serves as a transitional zone between rural mountainous ecosystems and nearby urban settlements. Increasingly, this area faces urbanization pressures such as tourism expansion, infrastructure development, and intensified land use, which may threaten ecological stability. This study aims to evaluate the ecological sensitivity of the park and optimize its spatial functional zoning. Using the Analytic Hierarchy Process (AHP), we followed four key steps: constructing the hierarchical model, generating the pairwise judgment matrices, computing the weights and conducting the consistency check, and determining the final weights. A hierarchical evaluation framework was constructed using the AHP, incorporating twelve ecological indicators across geomorphological, hydrological, atmospheric, biological, and anthropogenic dimensions. Spatial analysis tools in ArcGIS 10.2, including reclassification and weighted overlay, were employed for single-factor and integrated sensitivity assessments. The results indicated that land-use type, elevation, and water-body distribution were the most influential indicators. Ecological sensitivity across the park was categorized into five levels: extremely high (0.02%), high (11.99%), moderate (73.53%), low (14.19%), and extremely low (0.28%). Based on these findings, four functional zones were delineated: ecological conservation (50.99%), core landscape (22.86%), general recreation (23.94%), and management and service (2.21%). This research provides spatially explicit insights into forest management under anthropogenic stress, offering theoretical support for the sustainable governance of forest–urban interface landscapes. Full article

Background: Multimodal Deep learning has emerged as a crucial method for automated patient-specific quality assurance (PSQA) in radiotherapy research. Integrating image-based dose matrices with tabular plan complexity metrics enables more accurate prediction of quality indicators, including the Gamma Passing Rate (GPR) and dose difference (DD). However, modality imbalance remains a significant challenge, as tabular encoders often dominate training, suppressing image encoders and reducing model robustness. This issue becomes more pronounced under task heterogeneity, with GPR prediction relying more on tabular data, whereas dose difference prediction (DDP) depends heavily on image features. Methods: We propose BMMQA (Balanced Multi-modal Quality Assurance), a novel framework that achieves modality balance by adjusting modality-specific loss factors to control convergence dynamics. The framework introduces four key innovations: (1) task-specific fusion strategies (softmax-weighted attention for GPR regression and spatial cascading for DD prediction); (2) a balancing mechanism supported by Shapley values to quantify modality contributions; (3) a fast network forward mechanism for efficient computation of different modality combinations; and (4) a modality-contribution-based task weighting scheme for multi-task multimodal learning. A large-scale multimodal dataset comprising 1370 IMRT plans was curated in collaboration with Peking Union Medical College Hospital (PUMCH). Results: Experimental results demonstrate that, under the standard 2%/3 mm GPR criterion, BMMQA outperforms existing fusion baselines. Under the stricter 2%/2 mm criterion, it achieves a 15.7% reduction in mean absolute error (MAE). The framework also enhances robustness in critical failure cases (GPR < 90%) and achieves a peak SSIM of 0.964 in dose distribution prediction. Conclusions: Explicit modality balancing improves predictive accuracy and strengthens clinical trustworthiness by mitigating overreliance on a single modality. This work highlights the importance of addressing modality imbalance for building trustworthy and robust AI systems in PSQA and establishes a pioneering framework for multi-task multimodal learning. Full article

Background/Objectives: Hydrogels are highly hydrated, biocompatible polymer networks increasingly investigated as drug-delivery systems (DDS) for analgesics. Their ability to modulate local release, prolong drug residence time, and reduce systemic toxicity positions them as promising platforms in perioperative, chronic, and localized pain settings. This scoping review aimed to systematically map clinical applications, efficacy, and safety of hydrogel-based DDS for analgesics, while also documenting non-DDS uses where the matrix itself contributes to pain modulation through physical mechanisms. Methods: Following PRISMA-ScR guidance, PubMed, Embase, and Cochrane databases were searched without publication date restrictions. Only peer-reviewed clinical studies were included; preclinical studies and non-journal literature were excluded. Screening and selection were performed in duplicate. Data extracted included drug class, hydrogel technology, clinical setting, outcomes, and safety. Protocol was registered with Open Science Framework. Results: A total of 26 clinical studies evaluating hydrogel formulations as DDS for analgesics were included. Most were randomized controlled trials, spanning 1996–2024. Local anesthetics were the most frequent drug class, followed by opioids, corticosteroids, Non-Steroidal Anti-Inflammatory Drugs (NSAIDs), and neuromodulators. Application sites were predominantly topical/transdermal and perioperative/incisional. Across the DDS cohort, most of the studies reported improved analgesic outcomes, including reduced pain scores and lower rescue medication use; neutral or unclear results were rare. Safety reporting was limited, but tolerability was generally favorable. Additionally, 38 non-DDS studies demonstrated pain reduction through hydrogel-mediated cooling, lubrication, or barrier effects, particularly in burns, ocular surface disorders, and discogenic pain. Conclusions: Hydrogel-based DDS for analgesics show consistent clinical signals of benefit across diverse contexts, aligning with their mechanistic rationale. While current evidence supports their role as effective, well-tolerated platforms, translational gaps remain, particularly for hybrid nanotechnology systems and standardized safety reporting. Non-DDS applications confirm the intrinsic analgesic potential of hydrogel matrices, underscoring their relevance in multimodal pain management strategies. Full article

In recent years, the use of silver nanoparticles (AgNPs) in various fields has been investigated due to their highly potent properties. One of these areas is the adaptation of AgNPs to food packaging/preservation technologies. The primary reasons for the use of AgNPs in food preservation studies are their high levels of antibacterial, antioxidant, and antifungal activities. In particular, the slow and controlled release of silver provides a sustained protective effect throughout the contact period of AgNP-integrated packaging with food and reduces microbial load by preventing biofilm formation. Furthermore, high thermal stability of AgNPs provides high protection to foods under various conditions. Their high surface-area-to-volume ratio, making them effective even at low concentrations, further supports AgNPs as a promising alternative in food preservation technologies. Moreover, their ease of surface modification facilitates the integration of these nanoparticles (NPs) into polymer matrices, biodegradable films, and coatings. Additionally, some AgNP-based films are also used in smart packaging applications, providing a color change indicator of degradation. Their broad pH tolerance enhances their applicability to a variety of food types, from dairy to meat products. For all these reasons, AgNPs are considered as one of the essential components of innovative food packaging to slow down food spoilage, prevent microbial contamination, and provide safer, longer-lasting products to the consumer, and studies on them are ongoing. Full article

Background: The treatment of wounds remains a significant clinical challenge, particularly in chronic and infected wounds, where delayed healing often results in complications. Recent advances in biomaterials have highlighted the potential of polymer-based scaffolds as promising platforms for wound management due to their ability to mimic the extracellular matrix, support tissue regeneration, and provide a moist environment conducive to healing. Objectives: This review aims to provide a comprehensive overview of the recent progress in the design and application of polymer-based scaffolds loaded with essential oil (EO) components, emphasizing their role in promoting effective wound healing. Methods: Relevant literature on polymeric scaffolds and EO-based bioactive agents was systematically reviewed, focusing on studies that investigated the biological activities, fabrication techniques, and therapeutic performance of EO-loaded scaffolds in wound management. Results: Findings from recent studies indicate that EO components, particularly monoterpenoids such as thymol, carvacrol, and eugenol, exhibit remarkable antimicrobial, anti-inflammatory, antioxidant, and analgesic properties that accelerate wound healing. When incorporated into polymer matrices, these components enhance scaffold biocompatibility, antimicrobial efficacy, and tissue regeneration capacity through synergistic interactions. Conclusions: The integration of essential oil components into polymeric scaffolds represents a promising strategy for developing multifunctional wound dressings. Such systems combine the structural advantages of polymers with the therapeutic benefits of EOs, offering an effective platform for accelerating healing and preventing wound infections. Full article

The persistent conflict between strength and electrical conductivity in copper-based materials presents a fundamental limitation for next-generation high-performance applications. Graphene, with its unique two-dimensional architecture and exceptional intrinsic characteristics, has become a promising reinforcement phase for copper matrices. This comprehensive review synthesizes recent advancements in graphene–copper composites (CGCs), focusing particularly on structural design innovations and scalable manufacturing approaches such as powder metallurgy, molecular-level mixing, electrochemical deposition, and chemical vapor deposition. The analysis examines pathways for optimizing key properties—including mechanical strength, thermal conduction, and electrical performance—while investigating the fundamental reinforcement mechanisms and charge/heat transport phenomena. Special consideration is given to how graphene morphology, concentration, structural quality, interfacial chemistry, and processing conditions collectively determine composite behavior. Significant emphasis is placed on interface engineering strategies, graphene alignment, consolidation control, and defect management to minimize electron and phonon scattering while improving stress transfer efficiency. The review concludes by proposing research directions to resolve the strength–conductivity paradox and broaden practical implementation domains, thereby offering both methodological frameworks and theoretical foundations to support the industrial adoption of high-performance CGCs. Full article