Search Results (15,237)

Oat-based beverages are increasingly popular milk alternatives. However, the heat treatment required to ensure shelf stability is limited by rapid fouling formation on heated surfaces, reducing processing efficiency. Oat proteins, considered an important quality attribute of oat drinks, are suspected to play a key role in fouling initiation, but their specific contribution remains poorly understood. This study investigates the role of oat proteins in fouling formation during heat treatment on technical scale. Membrane filtration was applied and validated as sample preparation method for increasing the protein content. Fouling experiments were conducted using a previously validated fouling system with feed solutions containing different protein concentrations. Protein content was increased by filtration using 0.1, 0.8 and 1.4 µm ceramic membranes, yielding retentates with 10–21 g·100 g⁻¹ on a dry matter basis, and further enriched to >40 g·100 g⁻¹ through diafiltration. Fouling experiments (140 °C, 60 min) revealed a dependence of fouling formation on protein content in the feed solution. Fouling deposits were negligible at low protein concentrations (<2.5 g·100 g⁻¹), increased markedly between 8 and 14 g·100 g⁻¹, and reached a plateau at higher protein levels. Using oat supernatant or retentates, the protein content in the fouling correlated linearly with the protein content in the feed solution (R² = 0.98) but did not exceed ~25g·100 g⁻¹, resulting in predominantly carbohydrate-based deposits. In contrast, diafiltered protein-enriched feed solutions produced larger, protein-dominated deposits. A conceptual model describing feed-dependent fouling mechanisms is proposed. Full article

Background: Live music, understood as real-time musical performance delivered in the physical presence of patients or other participants, is increasingly incorporated into healthcare settings as an arts-based, non-pharmacological practice intended to support well-being and humanize care. While previous reviews have examined a broad range of music-based interventions in healthcare, limited attention has been given specifically to live music, its contextual characteristics, and the values attributed to its use within hospital environments. Objectives: This scoping review aims to map and synthesize the literature on live music in healthcare settings, focusing on clinical contexts, populations involved, and the therapeutic, psychosocial, and environmental values reported. Methods: A scoping review was conducted following the framework of Arksey and O’Malley. Searches were performed in Web of Science, Scopus and Pubmed using terms related to live music and healthcare settings. Studies published in English or Spanish over the past 20 years were considered. After screening titles, abstracts, and full texts, 81 studies met the inclusion criteria. Results: The studies covered diverse hospital units and patient groups, particularly oncology, neonatal and intensive care, palliative care, and haemodialysis. Reported outcomes were mainly psychological and emotional, including reductions in anxiety, stress, and distress, alongside improvements in mood, well-being, and quality of life. Cognitive, physiological, and environmental benefits were also identified, emphasizing the role of live music in creating supportive and humanized care environments. Most studies were conducted in Europe and North America. Conclusions: Live music is widely implemented in healthcare settings and is associated with benefits extending beyond symptom reduction to experiential and humanizing dimensions of care. This scoping review provides an overview of the existing evidence base and identifies directions for future research in arts and health. Full article

Artificial intelligence (AI) is increasingly influencing nanopharmaceutical development by supporting the transition from empirical formulation screening toward predictive, data-driven, and translationally oriented design. Nanocarrier-based therapeutics are governed by nonlinear relationships among material composition, physicochemical attributes, manufacturing parameters, biological identity, pharmacokinetics, toxicity, and therapeutic performance. In this review, we examine how AI can contribute to nanopharmaceutical development from predictive formulation design to clinical translation. We synthesize current applications of machine learning, deep learning, physics-informed modeling, hybrid mechanistic–AI approaches, and automated optimization workflows, with emphasis on critical quality attribute modeling, multi-objective optimization, design of experiments, quality-by-design, process analytical technology, digital twins, and continuous manufacturing. We also discuss applications involving nano–bio interactions, pharmacokinetics, toxicity, immunogenicity, and precision nanomedicine. AI-based approaches can support rational nanocarrier design, identify nonlinear formulation–property relationships, guide optimization, improve process understanding, and integrate heterogeneous experimental, biological, and manufacturing datasets across diverse nanopharmaceutical platforms. These methods are particularly relevant for modeling protein corona formation, cellular uptake, intracellular trafficking, biodistribution, pharmacokinetics, toxicity, immunogenicity, and patient-specific responses. However, translational implementation remains limited by fragmented datasets, inconsistent reporting standards, limited interpretability, insufficient external validation, uncertain predictions, poorly defined applicability domains, and evolving regulatory expectations for adaptive computational models. Overall, AI should be viewed not only as an optimization tool, but also as a translational framework connecting formulation science, biological prediction, manufacturing control, and clinical implementation. Future progress will depend on standardized data infrastructures, explainable and externally validated models, uncertainty quantification, applicability-domain definition, hybrid mechanistic–AI frameworks, regulatory-ready documentation, and clinically relevant case studies. Full article

Background/Objectives: Composite endpoints are commonly used in chronic kidney disease (CKD) trials to enhance statistical efficiency but may not reflect clinically meaningful outcomes. We assessed agreement between composite endpoints and key components using the bias attributable to composite outcome (BACO) index and explored determinants of variability. Methods: We performed a meta-epidemiological analysis of randomized controlled trials evaluating sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide-1 receptor agonists, and non-steroidal mineralocorticoid receptor antagonists in CKD. BACO was defined as the ratio of the log-hazard ratio for the composite endpoint to that of the reference outcome (kidney failure or cardiovascular death), with variance estimated using the delta method. Determinants were analyzed using inverse-variance weighted mixed-effects meta-regression. Results: Eight trials comprising 38 composite endpoints were included. Higher reference-event rates were associated with higher BACO values overall (β: 0.06, 95% CI: 0.02; 0.10) and in kidney failure-referenced analyses (β: 0.07, 95% CI: 0.02; 0.12). Stronger composite treatment effects correlated with higher BACO (β: −1.07, 95% CI: −1.84; −0.30). The number of components and follow-up duration showed no significant association. In cardiovascular death-referenced models, BACO was associated with trial size (β: 0.12 per 1000 participants), mean age (β: −0.04 per 10 years), and female proportion (β: 0.09 per 10% increase). Conclusions: Agreement between composite endpoints and clinically relevant outcomes is driven by the relative frequency and treatment responsiveness of component events rather than endpoint complexity. Composite endpoints in which clinically important outcomes are infrequent may not reliably reflect treatment effects, underscoring need for clinically aligned endpoint strategies. Full article

Background/Objectives: Remote islands in Japan constitute a unique medical environment in which physicians often manage a broad spectrum of clinical conditions. However, physicians practicing on remote islands have diverse medical backgrounds, and disease-level community-based care patterns in these settings have not been systematically described. This study aimed to characterize community-based care patterns across diseases in clinics on remote islands in Japan using an exploratory conceptual framework and to examine whether facility- and physician-related attributes were associated with these patterns. Methods: We conducted a questionnaire survey in February 2023 involving rural clinics on remote islands in Japan. For each disease, respondents reported community involvement at three clinical stages—initial consultation, follow-up, and completion of care—yielding eight possible care patterns (000–111). Primary community completeness was defined as the proportion of clinics reporting community-based involvement in initial consultation and completion of care (P111 + P101). Diseases were ranked according to this metric and stratified into three predefined conceptual zones (upper, middle, and lower). Subgroup analyses examined differences in primary community completeness according to facility- and physician-related attributes, including deployment duration, prior rural practice experience, career length, and specialty composition. Results: We analyzed data from 23 clinics covering 167 diseases. Diseases formed a continuous gradient ranging from community-completable to specialist-dependent conditions. Differences in community-based care patterns were most pronounced in the middle zone. Deployment duration was associated with directional differences in community-based care patterns, whereas specialty composition was associated with larger subgroup differences. In contrast, diseases in the lower zone demonstrated relatively stable specialist-dependent patterns regardless of facility- or physician-related attributes. Conclusions: This exploratory study proposed a conceptual framework for characterizing community-based care patterns across diseases in clinics on remote islands in Japan. The findings suggest that community-based care patterns on remote islands may reflect differences in disease-related care structures as well as contextual factors. The proposed framework may support future discussions regarding education, workforce planning, and healthcare systems in remote island settings in Japan. Full article

The acceleration and deceleration of high-speed gas flow within a pipeline, induced by the action of flow-restriction devices, frequently result in the emergence of unsteady flow phenomena. Consequently, the generated excitation forces provoke intense vibrations in the pipeline, thereby substantially elevating the operational risks of the pipeline system. To mitigate such risks, the pipeline is typically subjected to fixed constraints to reduce vibration. A pipeline designed to simulate unsteady airflow was developed for the purpose of validating the vibration attenuation effect. Within this context, the effects of binding and friction constraints were compared through fluid–structure interaction simulation, and their respective mechanisms of action were analyzed individually. The results demonstrate that the constraints, in conjunction with the original pipeline, will result in a higher first-order natural frequency, which constitutes one of the primary methods for mitigating resonance effects. Both friction constraints and binding constraints significantly elevate the first-order natural frequency of the pipeline system, with binding constraints demonstrating higher efficiency. This phenomenon is attributable to the arch-like bending deformation observed in such experimental pipelines during first-order resonance, as binding constraints effectively maximize the restriction on pipeline strain. Through a comparative analysis of the time-domain and frequency-domain results of outlet pipe 1 before and after constraint application, it was observed that the axial RMS value of the constrained pipe decreased by 21.8%, while the radial value diminished by 33%. This finding further substantiates that imposing binding constraints at the location of maximum strain can elevate the pipe’s natural frequency by reducing both strain and the effective length of the “beam”, thereby significantly alleviating pipe vibrations induced by unsteady flow. Full article

Background: Hemodynamic depression, characterized by bradycardia and hypotension, is a common complication of carotid artery stenting (CAS) and is primarily attributed to carotid sinus baroreceptor stimulation. While prophylactic atropine is often used in high-risk patients, predictors of unexpected hemodynamic depression among patients initially deemed low-risk remain incompletely defined. Objective: To identify clinical, anatomical, and procedural predictors of hemodynamic depression in patients undergoing CAS without prophylactic atropine. Methods: We performed a retrospective, single-center observational study of consecutive patients undergoing CAS between January 2015 and May 2024. Patients who received prophylactic atropine for low baseline heart rate (HR) were excluded. Hemodynamic depression was defined as a >20% reduction in HR from baseline. Absolute bradycardia (HR <50 bpm) and hypotension (>40% reduction in systolic blood pressure) were recorded descriptively. Results: A total of 158 patients underwent CAS, of whom 33 (20.9%) were excluded due to prophylactic atropine administration for low pre-procedural heart rates (<60 bpm). Among 125 included patients, 62 (49.6%) experienced significant HR reduction during CAS. In multivariable analysis, a shorter distance between the stenotic lesion and the carotid bifurcation was independently associated with hemodynamic depression (OR 0.90 per mm increase; 95% CI 0.82–0.99; p = 0.023). Greater intraprocedural reductions in systolic and mean arterial pressure were also associated with HR depression. Traditional clinical risk factors, including age, sex, comorbidities, degree of stenosis, calcification severity, anesthesia type, and procedure urgency, were not independently predictive. Conclusions: Hemodynamic depression remains frequent during CAS even among patients classified as low risk. Lesion proximity to the carotid bifurcation is a key anatomical predictor of autonomic instability, highlighting the limitations of standard risk stratification and supporting a lesion-specific approach to periprocedural hemodynamic management. Full article

Objectives. Hemolysis significantly interferes with insulin measurements in clinical settings, leading to inaccurate results. Although the activity of insulin-degrading enzyme (IDE) is assumed to be the primary mechanism, the potential involvement of additional mechanisms remains unclear. This study aims to determine if IDE is the sole cause of this interference by using a selective IDE inhibitor, 6bK, and to explore whether this inhibition can completely prevent hemolysis-related inaccuracies in insulin assays. Methods. The effects of 6bK on insulin degradation were evaluated in hemolyzed and non-hemolyzed serum samples at room temperature, following the CLSI guidelines EP07-A2 and C56-A. Insulin levels were measured using chemiluminescent immunoassays. Additional assessments included the impact of 6bK on serum C-peptide, proinsulin, and standard biochemical parameters. The effects of 6bK were also evaluated at 4 °C and after 21 days of storage at room temperature prior to use. Results. Hemolysis caused a significant decrease in insulin concentrations, dependent on hemolysate levels and incubation time. The addition of 10 µM 6bK completely reversed hemolysis-induced insulin degradation in serum across a broad range of insulin baseline concentrations and degrees of hemolysis. Furthermore, 6bK did not affect insulin levels in non-hemolyzed samples or alter the quantification of C-peptide, proinsulin, or standard biochemical parameters. Conclusions. The decrease in serum insulin concentration due to hemolysis is exclusively attributed to the action of IDE. Selective inhibition of IDE by 6bK effectively eliminates hemolysis-induced interference in insulin measurements, providing a novel and reliable solution for accurate insulin quantification in hemolyzed clinical samples. Full article

Background/Objectives: With the increasing incidence of drug counterfeiting and the emergence of personalized medicine, the need for unique marking of solid dosage forms, e.g., tablets, has attracted considerable interest in the current research and development landscape. Besides traditional printing methods, laser marking offers several advantages, as it eliminates the need for organic solvents and enables the generation of precise patterns. However, laser exposure may raise safety concerns regarding the stability of photosensitive drugs in the irradiated dosage forms. Therefore, the aim of the present study was to test the photoprotective effect of titanium dioxide (TiO₂) and its various alternatives, e.g., talc, calcium carbonate (CaCO₃), zinc oxide (ZnO), and black iron oxide (Fe₃O₄), alongside a ready-to-use reference formulation, Opadry^® Brown, which contains TiO₂ (titanium-containing, TC) on nifedipine, a light-sensitive model drug. Methods: Laser marking or short-term laser ablation at different wavelengths (193 nm, 248 nm, 532 nm, and 781 nm) was applied to different coating formulations. As a positive control, prolonged exposure to daylight was applied. The properties and photostability of these formulations were evaluated using several analytical methods (i.e., surface profilometry, Raman spectroscopy, and high-performance liquid chromatography (HPLC)). Results: The TiO₂, ZnO, Fe₃O₄, and Opadry^® TC Brown coatings maintained their color during the long-term study under all conditions. Furthermore, the prepared formulations exhibited different ablation depths and morphological changes depending on the coating and laser type. HPLC measurements confirmed significant differences in the protective ability of various pigments against sunlight and different types of lasers. Nevertheless, the obtained Raman spectra were not in complete agreement with HPLC results, which can be attributed to spectral overlap between key nifedipine degradation markers and excipient signals in the tablet core. Conclusions: Overall, laser treatment of tablets containing photosensitive drugs may induce API decomposition; however, this effect can be minimized or avoided by careful selection of the appropriate combination of laser type and photoprotective pigment. Under the applied experimental conditions, Ti:Sa laser treatment was associated with the lowest degree of nifedipine degradation among all formulations, while ZnO-containing coatings demonstrated the most consistent photoprotective performance against the majority of the tested laser types, while Fe₃O₄-containing coatings provided superior protection during prolonged sunlight exposure and Nd:YAG laser irradiation. Full article

Soil particle size fractions (PSFs)—sand, silt, and clay—are fundamental determinants of soil hydrological behavior, nutrient retention, and erodibility, yet their spatial prediction remains challenging due to the compositional nature of the data, unquantified prediction uncertainty, and limited interpretability of machine learning models. This study develops an integrated compositional mapping framework incorporating multi-source Sentinel-1/2 and topographic covariates, coupling the isometric log-ratio (ILR) transformation with Quantile Regression Forests (QRFs), a Monte Carlo simulation (MCS)-based latent-to-physical space uncertainty propagation strategy, and a Wrapper-SHAP attribution method to jointly address these challenges. The framework was evaluated across regional croplands in the central Shandong mountain-hilly region of China, using an elevation-stratified spatial cross-validation. Validations achieved R² values of 0.72, 0.61, and 0.59 for sand, silt, and clay, respectively, and a global Aitchison distance of 0.34. Critically, the MCS error propagation strategy effectively compensated for the probability distribution shift introduced by non-linear ILR back-transformation. This ensured that all predicted compositions strictly satisfied compositional closure and the [0, 100%] constraint, while aligning the prediction interval coverage probability (PICP) of each fraction closely with the 90% nominal level. Wrapper-SHAP overcame direct attribution limitations in compositional models, revealing the predictive associations of these multi-source covariates: high remote sensing-derived Bare Soil Index (BSI) and Moisture Stress Index (MSI) values primarily exhibited strong predictive associations with sand enrichment, whereas their lower values, combined with elevated Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and anthropogenic indicators, favored silt and clay accumulation. The proposed framework provides a transferable methodological reference for remote sensing-integrated compositional soil mapping with reliable uncertainty estimates and interpretable driver identification at regional scales. Full article

To address the limitations of vision-only defect detection, image–semantic misalignment, and spatial-logic conflicts in complex substation inspection scenarios, this paper proposes a camera-sensor-based multimodal defect sensing framework with cross-modal feature mapping for substation equipment monitoring. The proposed framework integrates field inspection images acquired by camera sensors, defect textual descriptions, and equipment topology knowledge and establishes a unified domain-adaptive pre-training–bidirectional cross-modal mapping–hierarchical reasoning workflow. First, a Contrastive Language–Image Pre-training (CLIP)-based domain-adaptive pre-training strategy is developed to enhance the representation of equipment categories, defect attributes, and inspection-scene semantics. Second, a bidirectional cross-modal feature mapping network is constructed to model fine-grained interactions between candidate visual regions and textual semantics, where uncertainty-aware fusion and prototype constraints are introduced to improve semantic alignment and defect discrimination. Third, a hierarchical neuro-symbolic reasoning module incorporates equipment topology and spatial rules for posterior verification, logical consistency checking, and false-positive suppression. Experiments on a substation inspection image dataset demonstrate that the proposed method achieves 90.8% mAP@0.5, 68.7% mAP@0.5:0.95, and 89.4% F1-score, outperforming mainstream and recent detection models. Full article

The mechanical properties and real-time damage evolution of sustainable concrete (SC) containing 100% recycled concrete aggregate (RCA) under the combined action of hybrid steel and polyolefin fibers were studied. Inspired by solving the massive effects on the environment from construction waste, as well as to improve the lower mechanical performance of lower-grade RCA, the effect of combining high-stiffness hooked-end steel fibers and flexible macro-polyolefin fibers within RCA was investigated. Six different mix designs were considered: plain, single-fiber (100% steel and 100% polyolefin) and three hybrid composites with varying fractions of the steel/polyolefin fibers (25/75, 50/50, and 75/25). Compressive, tensile and flexural strengths were determined by mechanical testing. During compressive testing, the damage evolution was monitored using low-cost acoustic emission (AE) as a non-destructive technique. Cumulative hits analysis, amplitude distributions, and the statistical b-value parameter were used for damage characterization. The results show that steel fiber significantly increased compressive strength (an increase of up to 13.8%), and the 50/50 hybrid mix showed a high synergistic effect, yielding the highest tensile (4.86 MPa) and flexural (25.54 MPa) strengths. AE analysis identified different damage fingerprints: Based on amplitude analysis, steel-fiber composites exhibited high-amplitude events (which may be attributable to fiber pull-out); polyolefin-fiber composites generated medium-amplitude events (may have resulted from distributed microcracking); and hybrid mixes displayed a mixed amplitude distribution. The b-value analysis provided insight into progressive damage and revealed that the hybrid fibers induce stable, diffuse damage that prevents the brittle failure of plain recycled aggregate concrete (RAC). The results show that hybrid fiber reinforcement can be a reliable approach to enhance the mechanical performance and crack resistance of RAC. Furthermore, low-cost acoustic emission (AE) serves as an effective non-destructive method for monitoring damage progression within the material. Full article

IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts, rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown and a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at an AUROC of 0.93), detecting 100% of injected faults, with a mean delay under 25 min. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW’s shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW’s shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together, these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems. Full article

Pre-Cenomanian Nubia sandstone is recognized one of the most prolific reservoirs in the Gulf of Suez, Egypt. Accurately determining its reservoir rock type (RRT) is crucial for reservoir characterization and modeling, especially when the reservoir is extremely heterogeneous. This study addresses the critical challenge of characterization in extremely heterogeneous reservoirs by introducing a novel integrated workflow that bridges the gap between traditional sedimentological geology, traditional x-y approaches, and advanced machine learning methods. To achieve this, this study utilizes sedimentological core description, routine core analysis, and conventional well log data from two wells (well A and well B) located in the southern Gulf of Suez, Egypt. The results demonstrate that the complete Nubia interval in the southern Gulf of Suez can be separated into seven distinct lithofacies (LF1–LF7). The first six lithofacies comprise various types of sandstone, while the seventh is composed of shale. The traditional techniques used to predict the RRTs show that the normalized reservoir quality index (NRQI) was the most effective method for predicting the Nubia rock types. The machine learning K–means clustering and self-organizing map (SOM) techniques utilizing raw log data and principal component analysis (PCA) can properly predict the Nubia reservoir rock types. The reservoir quality ranges from poor to very good; well A is dominated by moderate reservoir quality, while well B exhibits predominantly very good reservoir quality. This discernible difference in reservoir quality between the two wells is probably attributed to post-depositional diagenetic processes and variations in sandstone texture. Full article

Background: Infectious diseases remain a leading cause of mortality in South Africa, compounded by a high HIV prevalence. This study aimed to delineate the spectrum and clinicopathological characteristics of fatal infectious diseases through a postmortem audit to inform clinical practice and public health strategy. Methods: A retrospective, cross-sectional descriptive study was conducted on all autopsies with a final cause of death attributed to infectious disease at a National Health Laboratory Service, in Northern Pretoria, Gauteng, South Africa, from 2012 to 2021. Using the Systematised Nomenclature of Medicine Clinical Terms (SNOMED) code and word search engines codes, 55 cases were identified. Data on demographics, clinical presentation, HIV status, antiretroviral therapy (ART), comorbidities, and final autopsy diagnosis were extracted from the laboratory information system. Histological confirmation was performed using standard stains. Descriptive statistical analysis was conducted using STATA-18. Results: The cohort (n = 55) had a median age of 31 years (IQR 19–45) and was predominantly female (67%). HIV prevalence was 35%, with 68% of those on ART. The leading cause of death was multilobar pneumonia (36%), followed by bronchopneumonia (22%). AIDS-defining illnesses were present in 27% of cases, with disseminated tuberculosis being the most common (46%). Septic shock was identified in 18% of decedents. A significant proportion (60%) of the cohort was HIV-negative. Conclusions: This autopsy series reveals a high burden of fatal community-acquired pneumonias and HIV-associated opportunistic infections, with a notable proportion of deaths occurring in HIV-negative individuals. The findings underscore diagnostic gaps and highlight the critical role of autopsy in accurate mortality surveillance, advocating for enhanced antemortem diagnostic protocols and targeted public health interventions. Full article