Search Results (169)

Nanobubbles have attracted increasing interest in food systems because they can modify gas dispersion, interfacial transport, washing performance, preservation processes, and the structures of dispersed matrices. However, their behavior cannot be interpreted based on bubble size alone. Proteins, polysaccharides, lipids, salts, colloidal particles, gas composition, and processing conditions can alter interfacial adsorption, gas transfer, bubble persistence, and matrix organization in food systems. This review examines the physicochemical mechanisms proposed to explain nanobubble persistence and functionality, with an emphasis on surface charge, interfacial adsorption, gas supersaturation, confinement, and interactions with food biopolymers. A central distinction is made between passive nanobubble-containing systems and externally activated systems involving hydrodynamic cavitation, ultrasound, plasma, pressure fluctuations, and reactive gases. Under passive conditions, nanobubbles mainly act as gas–liquid interfaces that influence local transport and adsorption. In activated systems, microbial inactivation, reactive oxygen species formation, and apparent mass-transfer enhancement often arise from external energy input, gas chemistry, turbulence, and transient supersaturation rather than from nanobubbles alone. Interfacial stability is used here as an organizing concept to connect nanobubble persistence, food-matrix interactions, generation methods, characterization limitations, and interpretation of reported technological effects. Current methods, such as dynamic light scattering and nanoparticle tracking analysis, provide useful size and concentration estimates but cannot unambiguously distinguish nanobubbles from protein aggregates, fat droplets, micelles, polysaccharide assemblies, and other colloidal structures in complex matrices. Therefore, reliable interpretation requires complementary methods, appropriate controls, and standardized reporting of gas composition, generation method, energy input, matrix properties, and processing conditions. Thus, nanobubble-containing technologies show promise for food processing; however, their value depends on the separation of nanoscale interfacial effects from concurrent hydrodynamic, chemical, and matrix-dependent phenomena. Full article

Populus nigra buds contain resinous exudates rich in flavonoids, phenolic acids, terpenoids and other bioactive constituents. These exudates are the main botanical source of European Poplar-type propolis. Since hive-collected propolis shows strong botanical, geographical and hive contaminant variability, P. nigra bud resin exudate represents an attractive, standardizable and reproducible alternative for obtaining natural-complex ingredients. This study investigates the compositional relationship between Propolgemma^® standardized P. nigra buds (PBHE) and European propolis (PHE) hydroalcoholic extracts through integrated analytical approaches and evaluates the functional bioactivity of PBHE and a related oral spray formulation (Propolgemma^® spray forte, PBHE-SF). Untargeted metabolomic fingerprinting revealed clear clustering of P. nigra bud exudate with European propolis, demonstrating high compositional similarity. Targeted analyses confirmed that PBHE belongs to the poplar-type propolis family, while retaining additional bud-derived constituents such as salicylates, lignins and tannins, typical of bud tissue and largely absent from hive-collected propolis. Functionally, PBHE showed concentration-dependent antioxidant activity and significant inhibition of Streptococcus pyogenes biofilm at sub-MIC levels. PBHE, incorporated into a patented oral spray formulation (PBHE-SF), demonstrated strong mucoadhesion, high resistance to salivary wash-off, retention of antioxidant flavonoids on epithelial substrates and a mechanical barrier effect, reducing LPS-induced IL-6 release by 39%. It also showed dispersion of pre-formed S. pyogenes biofilms. PBHE emerges as a reproducible, plant-derived, bee-independent alternative to European propolis. Its chemical consistency, functional reliability, independence from bee foraging and from hive-derived contaminants improve the therapeutic potential on mucosal protection in medical device formulations and the suitability for scalable, controlled and industrially sustainable production. Full article

Water, sanitation, and hygiene (WASH) services are essential for learner health and equitable education. Persistent gaps in WASH infrastructure and hygiene provision remain a major challenge in low- and middle-income countries. We assessed WASH interventions, learner knowledge and perceptions, and implementation challenges and opportunities in five selected primary schools in eThekwini District, South Africa. We conducted a cross-sectional study among Grade 7 learners using a structured questionnaire adapted from the World Health Organization (WHO) Surveillance of WASH in Schools Tool, complemented by observational checklists. Stratified random sampling yielded 129 participants (76 girls and 53 boys), with a 72% response rate. Quantitative data were analysed using Chi-square, Fisher’s exact, and Kruskal–Wallis tests, as appropriate. Although drinking water access was generally reliable, significant gaps were observed in sanitation privacy, soap and toilet paper availability, cleanliness, and menstrual hygiene facilities. Female learners consistently reported poorer conditions than males (p < 0.05). The Hygiene Access Index differed significantly across gender and age groups (p < 0.05), reflecting inequitable provision of hygiene materials. Despite educational initiatives, substantial shortcomings persist in school WASH systems, particularly regarding sanitation privacy, hygiene supplies, and menstrual hygiene management, with potential implications for learner health, educational participation, gender equity, and school attendance. Sustaining gender-responsive WASH systems is essential for improving learner health and promoting equitable educational environments. Sustainable WASH systems refer to the consistent availability, functionality, maintenance, and equitable use of WASH infrastructure and services over time, supported by institutional capacity and behavioural adherence. These findings highlight the need for implementation-focused and systems-oriented approaches to strengthen sustainable WASH service delivery in resource-constrained school settings. Full article

Urban stormwater is often viewed as a drainage problem rather than a local water resource, even in areas where runoff capture could simultaneously reduce flooding and promote the reuse of non-potable water. This study develops, installs, and field-tests a decentralized, school-scale stormwater harvesting system that relocates permeable concrete, transforming it from a passive infiltration surface into a purpose-built capture and pretreatment interface. The system integrates a 3 m × 3 m permeable concrete slab with load-bearing sections, an impermeable underlayer to ensure controlled flow, a double-compartment sump for staged sedimentation and hydraulic damping, sequential filtration with sand/gravel and activated carbon, and a 5000 L storage tank. The prototype was implemented at CETis 105 in Querétaro, Mexico, and evaluated during its commissioning and operation in the 2023 rainy season. Field operations demonstrated reduced ponding in the catchment area and a reliable flow of runoff to the pretreatment units. In the sump compartments, apparent color decreased from 221 to 59 Pt-Co, turbidity from 46.8 to 12.9 NTU, and COD from approximately 30–35 to 15–18 mg·L⁻¹, corresponding to approximate pretreatment reductions of 73.3%, 72.4%, and 40–57%, respectively, before post-filtration. Conversely, the elevated pH, electrical conductivity, and total dissolved solids indicated interaction with fresh cementitious materials and dissolved ionic residues during initial operation, highlighting the need for curing, initial washing, and post-filtration verification before declaring compliance with reuse requirements. Therefore, the results support the feasibility of the proposed configuration as a decentralized, low-infrastructure architecture for localized runoff control and pretreatment, while confirming that full reuse validation still requires microbiological and post-filtration evaluation. The study provides a field-proven system design adaptable to school campuses and similar institutional environments for distributed stormwater management and non-potable water storage. Full article

During the machining processes, surfaces are often contaminated by cutting fluids, metallic debris, and residual films, which may compromise subsequent operations (e.g., coating, bonding, or precision assembly). In the present study, the effectiveness of several cleaning methods applied to machined metallic surfaces was experimentally evaluated. A set of commonly used industrial metals, including stainless steels, alloy steels, aluminum alloys, and brass, was machined under controlled conditions and subjected to various cleaning treatments, including solvent-based cleaning, ultrasonic washing, and aqueous detergent processes. Surface conditions were first assessed through optical microscopy, focusing on machining grooves as preferential sites for contaminant accumulation. Then, scanning electron microscopy (SEM) combined with energy dispersive X-ray spectroscopy (EDS) was employed to better identify residual contaminants. Optical observations highlighted the progressive removal of debris and lubricant residues, while SEM–EDS analyses revealed the presence of thin organic films and localized carbon-rich contaminants, even on apparently clean surfaces. Results show a consistent trend across all materials, with increasing cleaning effectiveness from solvent-based treatments to ultrasonic cleaning and specific aqueous detergent processes. Ultrasonic cleaning proved particularly effective in removing thin films and contaminants in complex geometries, whereas aqueous detergent treatment demonstrated superior performance in eliminating larger debris and achieving overall surface cleanliness. The findings, combining a broad experimental campaign across multiple materials, cleaning treatments, and characterization techniques, underline the importance of multi-scale characterization for a reliable assessment of cleaning efficiency and suggest that combined cleaning approaches may further enhance surface quality in demanding industrial applications. Full article

Background: Proton pump inhibitors (PPIs) have been associated with lung dysbiosis and increased respiratory risk. Micro-aspiration is a proposed mechanism, but reliable biomarkers remain elusive. This study evaluates the potential of fluorescein as a biomarker of micro-aspiration and PPI-associated pulmonary risk. Methods: We conducted a retrospective analysis of 137 bronchial washing fluid samples from patients with pulmonary conditions to assess microbial colonization in relation to PPI use. Bacterial burden was determined by culture and PCR and categorized as 0, 1 or ≥2 pathogens. Micro-aspiration was evaluated by quantifying fluorescein-laden macrophages in bronchoalveolar lavage following oral fluorescein administration. Associations between PPI use, fluorescein levels and pathogen burden were analyzed using adjusted ordinal regression models. Results: PPI use was associated with higher odds of increased pathogen burden, though not statistically significant (OR = 1.40, 95% CI: 0.71–2.75, p = 0.33). Fluorescein-laden macrophages were higher in PPI users (41.5 versus 31.2 ng/mL), but showed no meaningful correlation with pathogen load (p = 0.09). Corticosteroid therapy was significantly associated with Gram stain results (OR = 2.37, 95% CI: 1.12–5.15, p = 0.03). Conclusions: These findings suggest a potential link between PPI use and airway colonization. Fluorescein shows promise as a biomarker for micro-aspiration, but its clinical utility requires further validation. Full article

Precise object detection is critical for preventing damage to vehicle attachments during automatic car washing. However, the existing methods often suffer from low accuracy and false detections due to the diverse shapes and visual ambiguity of these attachments. To address these challenges, we propose a novel framework integrating a YOLOv11-based detector with a graph neural network. Specifically, we introduce a spatial graph module to refine object localization by capturing invariant spatial constraints within the car wash environment. Furthermore, we incorporate a class graph module to model inter-class semantic correlations, thereby improving the classification of visually ambiguous objects such as emblems. Experimental results on a real-world dataset demonstrate that our method achieves an mAP50 of 97.9%, outperforming state-of-the-art models including D-FINE 96.5% and RT-DETR 96.1%. These findings confirm the robustness of our approach under varying viewpoints and background conditions, offering a significant improvement in the safety and reliability of automatic car wash systems. Full article

The increasing scarcity of urban water resources, particularly in regions with intermittent supply and household water storage tanks, demands monitoring approaches capable of identifying end-use consumption patterns beyond aggregated volume measurements. Framed primarily as a feasibility study, this research presents an IoT-based framework for the automated classification of residential water consumption activities using water-level dynamics and supervised machine learning. A non-intrusive sensing architecture based on hydrostatic pressure measurements was deployed in a domestic water tank and integrated with a cloud-based data acquisition and processing platform. Five representative household states and activities were considered: tank refilling, stable state, toilet flushing, washing clothes, and taking a bath. A labeled dataset comprising 4396 consumption events was used to train and evaluate Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors, and Recurrent Neural Network (LSTM) models using features derived from water-level variations. All models achieved high performance, with accuracies above 0.92 and weighted F1-scores up to 0.93. The evaluated models showed highly comparable results, with the SVM (RBF) achieving a slightly higher accuracy (0.9307) in this evaluation setting, while ROC analysis showed AUC values between 0.97 and 1.00 across all classes, indicating strong discriminative capability. Additionally, specific activities such as washing clothes and tank refilling achieved precision and recall values above 0.95. These findings confirm that hydrostatic pressure-based sensing, combined with machine learning, enables reliable identification of domestic water-use events under intermittent supply conditions. The proposed approach provides actionable insights for demand management, leak detection, and user awareness, supporting more efficient and sustainable residential water consumption strategies. Full article

To meet the demand for refined laundry care in intelligent washing machines and address the low accuracy, poor robustness, and lack of physical interpretability of existing material recognition technologies, a recognition method integrating physical prior knowledge is proposed. Based on a physical experimental platform for drum washing machines, mechanical vibration signals from a three-axis acceleration sensor and motor electromagnetic signals are collected synchronously, a dataset consisting of soft and hard loads is constructed, and time-domain alignment of heterogeneous signals is realized using adaptive pooling technology. Combined with the mechatronic coupling mechanism in the loosening, deviation detection, and weighing stages of washing machines, a Physics-Aware Dual-Stream Multi-Scale Temporal Convolutional Network (PSA-DSMS-TCN) is designed. The network extracts mechanical and electromagnetic features in parallel through a dual-stream structure, expands the receptive field using multi-scale dilated convolution, and introduces an operating condition-gated attention mechanism to achieve dynamic feature fusion. The results of 5-fold cross-validation show that the model achieves an average recognition accuracy of 94.05%, with consistent performance enhancement and substantial practical robustness. The results demonstrate that the PSA-DSMS-TCN effectively improves the precision of material prediction while maintaining lightweight characteristics, providing reliable technical support for the intelligent matching of laundry care parameters. Full article

Background/Objectives: To investigate and develop a non-invasive parametric radiomics model derived from dynamic contrast-enhanced MRI (DCE-MRI) time-intensity curve (TIC) kinetics for predicting breast cancer molecular subtypes (HR+/HER2−, HER2+ and triple-negative breast cancer). Methods: This multicenter retrospective study enrolled 935 female patients with histologically confirmed breast cancer who underwent pretreatment breast DCE-MRI from August 2017 to July 2022. Based on the wash-in rate (WIR) and the area under the TIC, the original multiphase DCE-MRI images were converted into two types of parametric images. Radiomics features were extracted from TIC-WIR and TIC-Area images and analyzed using low variance filtering, the elimination of highly correlated features, and the least absolute shrinkage and selection operator regression. The categorical boosting algorithm was employed to develop multiclass prediction models for breast cancer molecular subtyping. A TIC-Combined model was further established by integrating the calibrated probability outputs of the TIC-WIR and TIC-Area models using a decision-level fusion strategy. The discrimination, calibration, and interpretability of the models were evaluated in the study datasets. Results: The TIC-Combined model achieved superior predictive performance in both the internal validation set (micro-average AUC: 0.79, macro-average AUC: 0.77) and the external validation set (micro-average AUC: 0.77, macro-average AUC: 0.75). For subtype-specific classification by the TIC-Combined model, the highest one-vs-rest AUCs were 0.81 for triple-negative breast cancer in the internal validation set and 0.76 for HER2+ breast cancer in the external validation set. The TIC-Combined model also showed good calibration and high interpretability which ensured reliable predictions and provided clear insights into feature importance. Conclusions: Interpretable parametric radiomics from TIC-derived parametric maps links kinetic features to molecular phenotypes, enabling accurate and non-invasive classification of breast cancer molecular subtypes. Full article

The development of photocurable hydrogel biomaterial inks with suitable rheology, low cytotoxicity, and tuneable mechanical properties is essential for reliable biofabrication. This study aimed to formulate PEGDA–gelatine–collagen inks using lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP) as photoinitiator. Rheological characterisation and flow-model fitting were performed, mechanical stiffness modulation under different light intensities was evaluated, complex structures were printed using direct extrusion and FRESH methodologies, and PEGDA/LAP extractables were quantified by NMR after controlled washing procedures. In vitro assays assessed cell viability and proliferation on the resulting scaffolds. The Herschel–Bulkley model best described the flow behaviour across formulations; while viscoelastic measurements showed that increasing light intensity progressively enhanced hydrogel stiffness, enabling fine control over final mechanical properties. NMR analysis showed that washing removed a substantial fraction of residual LAP, in agreement with the biological findings: fibroblasts failed to survive on unwashed scaffolds but exhibited robust proliferation and recovered their characteristic elongated morphology on washed constructs. Among all inks, PeGeCol_10_2 provided the best combination of shear-thinning behaviour, structural integrity, low residual photoinitiator, and tuneable mechanics. Using this formulation, we successfully printed large anatomical models with high fidelity and excellent handling properties, underscoring its potential for soft-tissue prosthetics and broader tissue-engineering applications. Full article

Urban air pollution is a major public health concern, especially in densely populated cities. This problem also includes food safety issues in outdoor retail environments, where fresh products may be exposed to airborne pollutants. This study examines the presence of polycyclic aromatic hydrocarbons (PAHs) on fruits sold at indoor and outdoor locations across Budapest and several Hungarian cities. Results showed higher PAH concentrations on fruit sold outdoors, with benzo[a]pyrene (BAP) exceeding 2 µg/kg in 62% of outdoor samples and in 22% of indoor ones. Washing with water reduced contamination by 40–50% on average, with some samples showing over 65% reduction for BAP. Differences across fruit types were limited overall, though statistically significant for BAP in certain cases, highlighting compound-specific variability. Correlation analysis revealed weak but interpretable associations between PAH levels and ambient air quality indicators, with a moderate correlation for fine particulate matter ≤ 2.5 µm (PM_2.5) (r = 0.4355) and a weaker one for the calculated Air Quality Index (AQI) (r = 0.2148). These findings suggest that while urban microenvironments influence contamination, the general air quality indices may not predict surface PAH burden reliably. The study highlights the role of public wells in enabling citizen-level mitigation through rinsing and calls for integrated urban health strategies considering food exposure alongside infrastructural access. Full article

Appliance recognition from aggregate household measurements is challenging under real deployment conditions, where multiple devices operate concurrently and sensor data are affected by imperfections such as noise, missing samples, and nonlinear meter response. In contrast to many studies that rely on curated or idealized datasets, this work investigates appliance recognition using real multimodal utility data (electricity, water, gas) collected at the building entry point, in the presence of substantial uninstrumented background activity. We present a case study evaluating transparent, rule-based detectors designed to exploit characteristic temporal dependencies between modalities while remaining interpretable and robust to sensing imperfections. Four household appliances—washing machine, dishwasher, tumble dryer, and kettle—are analyzed over six weeks of data. The proposed approach achieves reliable detection for structured, water-related appliances (22/30 washing cycles, 19/21 dishwashing cycles, and 23/27 drying cycles), while highlighting the limitations encountered for short, high-power events such as kettle usage. The results illustrate both the potential and the limitations of conservative rule-based detection under realistic conditions and provide a well-documented baseline for future hybrid systems combining interpretable rules with data-driven adaptation. Full article

Mimotope-based immunoassays offer an eco-friendly alternative to chemically synthesized antigens for the quantitative analysis of small molecules, but their use for practical on-site and high-throughput residue monitoring remains limited. Herein, we report the selection, production, and application of a phage display–derived mimotope targeting an anti-uniconazole monoclonal antibody (UCZ-mAb), with the aim of developing two complementary immunoassays that enable sensitive, eco-friendly detection of UCZ residues in agricultural samples. A 12-mer phage-displayed peptide library was screened to identify UCZ-specific mimotopes, and a selected sequence was genetically fused to SpyTag and expressed in Escherichia coli to generate a SpyTagged mimotope. Leveraging the SpyCatcher/SpyTag self-assembly system, the SpyTagged mimotope was directionally conjugated onto SpyCatcher-functionalized time-resolved fluorescence beads (TRFBs) and subsequently used as a signal-labeled competitive antigen in a lateral flow immunoassay (LFIA) designed for rapid on-site screening. In parallel, a wash-free magnetic separation immunoassay (MSIA) suitable for green, high-throughput screening in routine laboratories was established using self-assembled mimotope-TRFB probes. The LFIA and MSIA exhibited half-maximal inhibitory concentrations (IC₅₀) of 3.70–6.72 μg/kg and 16.4–18.3 μg/kg, respectively, in real samples. Spiked-sample recoveries ranged from 91.1 to 107.8% for LFIA and 92.6–115.7% for MSIA, demonstrating acceptable accuracy and precision. These results indicate that the SpyTagged mimotope–based LFIA and MSIA provide complementary, reliable, and sensitive platforms for on-site screening and high-throughput monitoring of UCZ residues in agricultural samples, while avoiding the drawbacks associated with traditional chemical antigen synthesis. Full article

Background: Rabies is a fatal yet preventable zoonosis. In Saudi Arabia, uneven surveillance and limited public awareness may delay post-exposure prophylaxis (PEP). In Makkah, where residents regularly encounter free-roaming dogs, knowledge gaps could elevate exposure risks. Objectives: This study aims to assess public knowledge, attitudes, and post-bite practices regarding rabies, including wound washing and access to PEP among adult residents of the Makkah Region, and to examine associations with pet dog ownership. Methods: A cross-sectional survey was conducted in the Makkah Region (March–June 2025). An online validated bilingual questionnaire targeted residents ≥ 18 years via social media. Descriptive statistics, chi-square tests, 95% confidence intervals, and binomial logistic regression were applied in IBM SPSS v26; p < 0.05 was significant. Results: Of 523 respondents, 91.8% lived in Makkah city, 52.8% were female, and the age distribution was 18–24 years (44.2%), 25–34 years (35.6%), 35–44 years (12.0%), and ≥45 years (8.2%). Pet dog ownership was rare (1.9%), yet 39.4% reported stray dogs in their communities. Overall, 60.6% knew what rabies is and 63.7% knew it is vaccine-preventable, but 52.2% wrongly believed that transmission occurs only via dog bites. Hospitals (79.7%) and health centers (79.2%) were the most cited vaccination sites; social media was the dominant information source (74.6%). No significant association was found between pet ownership and rabies awareness (all p > 0.05). In multivariable regression (n = 509), adequate rabies knowledge increased the odds of an appropriate intended response (AOR 1.85, 95% CI: 1.27–2.68). Participants aged 30–40 years and those >50 years had significantly lower odds (AOR 0.45, 95% CI: 0.24–0.85 and AOR 0.23, 95% CI: 0.09–0.56, respectively). Conclusions: Despite moderate awareness, critical misconceptions and inconsistent first aid intentions persist. Priority actions include clear, locally adapted education on immediate wound washing and prompt PEP, standardized bite management pathways across facilities, reliable access to vaccines and immunoglobulin, and targeted social media micro-campaigns. By identifying public misconceptions, knowledge gaps, and preferred communication channels, this study provides baseline evidence to guide community awareness programs, intersectoral collaboration, and One Health-based surveillance essential for Saudi Arabia’s progress toward the global “Zero rabies by 2030” goal. Full article