Search Results (28,693)

Background/Objectives: Difficult biliary cannulation during endoscopic retrograde cholangiopancreatography (ERCP) poses significant challenges and increases the risk of adverse events. Pancreatic guidewire (PGW)-assisted techniques offer potential solutions, but real-world comparative data are limited. Methods: This cohort study of prospectively collected data analyzed 234 ERCP cases from the Hungarian ERCP Registry, focusing on three PGW-assisted methods: transpancreatic sphincterotomy (TPS), double-guidewire technique (DGW), and prophylactic pancreatic stent-assisted cannulation (PPS-C). Results: TPS demonstrated the highest primary cannulation success rate (83.1%), significantly outperforming DGW (67.7%) and PPS-C (67.6%) (p < 0.001). With salvage methods, cannulation success was high across all groups. Post-ERCP pancreatitis rates were low (5.0% TPS, 5.6% DGW, 3.9% PPS-C), but prophylactic measures (pancreatic stents, indomethacin) were underutilized. Conclusions: Our findings suggest that TPS is a safe and effective alternative for difficult biliary cannulation in ERCP. Routine considerations of post-ERCP pancreatitis prophylaxis (prophylactic pancreatic stents and non-steroidal suppositories) are recommended in all PGW-assisted cannulations to minimize complications. Full article

This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) Various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied to cosmological data analysis, improving parameter estimation and handling large datasets. However, models achieving significant computational speedups often exhibit worse confidence regions compared to traditional methods, emphasizing the need for future research to enhance both efficiency and measurement precision. (2) Traditional cosmological methods, such as those using Type Ia Supernovae, baryon acoustic oscillations, and cosmic microwave background data, remain fundamental, but most studies focus narrowly on specific datasets. We recommend broader dataset usage to fully validate alternative cosmological models. (3) The reviewed studies mainly address the $H_0$ tension, leaving other cosmological challenges—such as the cosmological constant problem, warm dark matter, phantom dark energy, and others—unexplored. (4) Hybrid methodologies combining machine learning with Markov chain Monte Carlo offer promising results, particularly when machine learning techniques are used to solve differential equations, such as Einstein Boltzmann solvers, prior to Markov chain Monte Carlo models, accelerating computations while maintaining precision. (5) There is a significant need for standardized evaluation criteria and methodologies, as variability in training processes and experimental setups complicates result comparability and reproducibility. (6) Our findings confirm that deep learning models outperform traditional machine learning methods for complex, high-dimensional datasets, underscoring the importance of clear guidelines to determine when the added complexity of learning models is warranted. Full article

Lipids obtained from microalgae have recently received significant attention from the energy and food industries. Microalgae are promising alternatives and are more sustainable sources of lipids for the food industry, which faces a growing demand for food and increased environmental awareness among consumers. This study provides a bibliometric review of research articles published between 2019 and 2024 with the aim of understanding the future trends and tendencies of the applications of microalgal lipids in the food industry. A thorough assessment of 255 articles retrieved from the Scopus database showed an apparent decrease in the number of publications per year within the analyzed timeframe. The predominant focus has been basic research conducted on a lab-scale using chlorophytes (green algae) to optimize lipid production by modulating physicochemical cultivation parameters (i.e., nutrient availability, temperature, light, and pH). Lipids were mainly extracted using the Bligh and Dyer or Folch methods, quantified gravimetrically, and characterized using gas chromatography coupled to mass spectrometry. Publications referring to polyunsaturated fatty acids, such as omega-3 and omega-6, were the most abundant. The results emphasized the significance of microalgae as a promising biotechnological platform for the production of lipids within the food industry. Full article

Background: Extracellular vesicles, particularly exosomes, play a crucial role in cell–cell communication and as carriers of biomarkers. However, their use in clinical settings is limited due to a lack of standardized isolation and characterization. Ultracentrifugation (UC) is considered a gold standard for exosome isolation but presents several limitations. Size-exclusion chromatography (SEC) has recently gained attention as a superior method, which offers better yield, purity, and protection of exosome physical properties. This study focused on optimizing the SEC method for isolation of exosomes from seminal plasma and comparing yield, quality, and proteome profiles with those obtained by UC. Methods: In this SEC method, seminal plasma (0.5 mL) was loaded onto a SEC column and collected in 13 fractions of 0.4 mL each. The physical and molecular characterization of exosomes was carried out using a ZetaView analyzer and Western blot, respectively. Further, SEC-isolated exosomes were used for proteomic profiling and functional bioinformatic analysis. Results: The second and third fractions had the highest concentration of exosomes with uniform size and strong expression of exosome markers. Also, comparative proteomic analysis identified 3315 proteins in SEC-isolated exosomes and 931 in UC-isolated exosomes, with 709 proteins in common. SEC-isolated exosomes showed greater overlap with Vesiclepedia’s and ExoCarta’s top 100 lists than UC-isolated exosomes (Vesiclepedia: 91 vs. 77 proteins, ExoCarta: 94 vs. 79). Proteins from SEC- and UC-isolated exosomes showed similar enrichment profiles across all three gene ontology categories. Conclusions: Overall, this optimized SEC protocol is a reliable alternative method to isolate seminal exosomes with high purity, supporting its potential applications in clinical and basic research. Full article

This paper introduces a hypergraph-based formalism for modeling kinematic and dynamic structures in robotics, addressing limitations of the existing formats such as Unified Robot Description Format (URDF), MuJoCo-XML, and Simulation Description Format (SDF). Our method represents mechanical constraints and connections as hyperedges, enabling the native description of multi-joint closures, tendon-driven actuation, and multi-physics coupling. We present a tensor-based representation derived via star-expansion, implemented in the Hypergraph Model Cognition Framework (HyMeKo) language. Comparative experiments show a substantial reduction in model verbosity compared to URDF while retaining expressiveness for large-language model integration. The approach is demonstrated on simple robotic arms and a quarter vehicle model, with derived state-space equations. This work suggests that hypergraph-based models can provide a modular, compact, and semantically rich alternative for the next-generation simulation and design workflows. The introduced formalism reaches 50% reduction compared to URDF descriptions and 20% reduction compared to MuJoCo-XML descriptions. Full article

Objective: Non-contrast computed tomography (CT) remains the gold standard for diagnosing ureteral stones, with excellent sensitivity and specificity. However, reliance on CT alone raises concerns regarding cumulative radiation exposure, particularly in recurrent stone formers. Clinical scoring systems such as CHOKAI, STONE, and modified STONE have been developed to provide practical bedside tools for diagnostic decision-making. This study prospectively compared these three clinical scores for their ability to predict urinary-stone disease in the emergency department. Study Design: Prospective study. Methods and Duration of the Study: Between 6 August 2024 and 15 February 2025, 130 consecutively enrolled adults with flank pain underwent bedside scoring and reference-standard non-contrast CT. Associations were analysed with Chi-Square Tests and multivariable logistic regression. Model calibration was assessed with the Hosmer–Lemeshow test; overall accuracy was calculated. Results: When the variables used in different stone scoring formulas were compared according to the computer tomography results, there was a statistically significant difference (p < 0.01) between patients with and without a history of stone and hydronephrosis. Patients with nausea, history of stone, and hydronephrosis were 11, 4.2, and 5 times more highly to have a stone on computer tomography than those without, respectively. Conclusions: In this Turkish cohort, CHOKAI and modified STONE demonstrated superior predictive performance compared to the original STONE score. These findings suggest that clinical scoring systems, when incorporating predictors such as nausea, prior stone history, and hydronephrosis, may serve as practical alternatives to CT-first diagnostic approaches. Multicenter validation studies are required before routine clinical adoption. Full article

Background: Recombinant monoclonal antibodies (mAbs) represent the fastest-growing sector of the biopharmaceutical industry, with their efficient expression being a key technological factor for scalability. Objectives: In this study we compared the performance of two bicistronic vectors, which alternate the positions of the light and heavy chain coding genes, employing a wild-type Encephalomyocarditis virus (EMCV) IRES functional element to drive expression of the second gene. Methods: Using two neutralizing anti-SARS-CoV-2 IgG1 antibodies as model molecules, we conducted transient transfections in the commercially available ExpiCHO^TM platform. Following protein A affinity purification and quantification, vectors positioning the light chain as the first cistron consistently yielded higher expression levels than those with the heavy chain upstream. To confirm the quality attributes of the mAbs, we applied a comprehensive analytical workflow, including SDS-PAGE and Western blot for molecular mass and purity, circular dichroism for secondary structure, intrinsic tryptophan fluorescence for tertiary structure, and SEC-HPLC for quaternary structure and aggregate detection. Additionally, we assessed binding affinity to the target using spot blot and surface plasmon resonance, analyzed N-glycosylation profiles by HILIC-HPLC and mass spectrometry, and examined molecular structure by transmission electron microscopy. Results and Conclusions: Together, these results provide insight into the impact of gene positioning within bicistronic vectors on mAb expression efficiency and quality, supporting optimization strategies for scalable recombinant antibody production. Full article

Background: Histone post-translational modifications (PTMs) play a pivotal role in the regulation of chromatin structure and gene expression, making them key targets in structural and epigenetic research. Synthetic histone peptides bearing specific PTMs are essential tools for elucidating the molecular mechanisms of histone function and protein–histone interactions. Methods: We synthesized histone H4 tail peptides containing site-specific lysine modifications using solid-phase peptide synthesis (SPPS). The correct synthesis of the peptides was confirmed by their molecular weights using a mass spectrometer. Results: An improved high-performance liquid chromatography (HPLC) method was developed to efficiently separate peptides with one modification difference. In alignment with green chemistry principles, we evaluated ethanol and isopropanol as an alternative organic solvent to acetonitrile in the mobile phase. The optimized HPLC method using acetonitrile enabled effective resolution of closely related peptide species, providing peptides suitable for downstream applications requiring high purities such as structural biology. Conclusions: This study presents a strategy for the purification of histone PTM peptides, emphasizing both analytical performance and sustainability. Further investigation must be undergone to develop high-precision purification using green chemicals. Full article

Effluents from the textile industry, particularly those containing synthetic azo dyes, poses a significant environmental threat, necessitating the development of more effective and sustainable pollutant removal methods. Traditional dye removal techniques often fall short in efficiency and environmental impact, prompting the exploration of enzymatic degradation as a promising alternative. This study focuses on chloroperoxidase, a natural biocatalyst recognized for its ability to oxidize synthetic dyes into less harmful products. By exploring the mechanistic distinction between chlorination and oxidative processes, we investigate the enzyme’s specific degradation pathways for azo dyes and the resulting by-products. Utilizing analytical techniques, including liquid chromatography/mass spectrometry (LC/MS), and density functional theory (DFT), we gain insights into the decolorization mechanism, revealing that the enzyme preferentially generates oxidative products through C–N bond cleavage as its initial degradation step. These findings underscore not only the unique mechanistic properties of chloroperoxidase but also its potential as a biocatalyst for industrial applications. This study advocates further research into the optimization of enzyme-based systems, highlighting their relevance in advancing greener chemical practices in the textile industry, thus contributing to more sustainable manufacturing processes. Full article

This study investigates the behaviour of dense silty sands with kaolinite clay under static drained/undrained conditions at low confining stress. Conventional laboratory tests assessed the mixtures’ physical properties, but standard void ratio methods proved inadequate for silty sands with kaolinite. Despite targeting 80% relative density, specimens exhibited loose sand behaviour in both drained and undrained tests. With increasing kaolinite content, conventionally reconstituted mixtures exhibit reduced peak stress ratios up to 10% fines, with little change beyond, while critical ratios generally rise at 25 kPa but remain unchanged or decrease slightly at 50 kPa. Analytical redefinition of minimum/maximum void ratios (based on sand–clay volumetric fractions) improved specimen reconstitution, yielding dense behaviour matching that of the host sand. The alternatively reconstituted mixtures display increasing drained peaks and minor changes in undrained peaks with increasing kaolinite content, with critical ratios increasing markedly at 25 kPa and only slightly at 50 kPa. However, this analytical void ratio determination method is limited to non-expansive, low-plasticity clays. Void ratios in silty sands with clay mineras are influenced by confining stress, drainage, saturation, clay content, and the sand skeleton structure. Unlike pure sands, these mixtures exhibit variable void ratios due to changes in the clay phase under different saturation levels. A new evaluation method is needed that accounts for clay composition, saturation-dependent consistency, and initial sand skeleton configuration to characterise these soils accurately. The findings highlight the limitations of conventional approaches and stress the need for advanced frameworks to model complex soil behaviour in geotechnical applications. Full article

Background/Objectives: Children with myelomeningocele (MMC) often experience lower extremity muscular contractures, which can impact their functional mobility. While standing programs have demonstrated benefits for children with other neuromuscular conditions, there is limited evidence on their use in ambulatory children with MMC who have joint deformities. This single-subject design study examined the impact of a home-based standing program on two ambulatory children with MMC, focusing on lower extremity muscle flexibility, functional movement quality, gait velocity, and participation in daily activities. Methods: Two children participated in a multi-phase single-subject (ABABA) withdrawal design beginning with the baseline phase and then alternating between the intervention and withdrawal phases. The intervention consisted of 60-minute standing sessions, five days a week, using a sit-to-stand stander (STSS) with support and supervision from a physical therapist (PT) and the parent. Primary outcomes included goniometric passive range of motion (PROM) and 10-Meter Walk Test (10 MWT). Secondary outcomes included the Pediatric Neuromuscular Recovery Scale (Peds NRS) and the Pediatric Evaluation of Disability Inventory Computer Adaptive Test (PEDI-CAT). Results: Improvements in hip and knee muscle flexibility were observed during the intervention phases, with some loss during the withdrawal phase. Functional movement quality improved in both children. Gait velocity and participation in daily activity scores remained stable during intervention phases. Parental feedback reflected increased independence and high engagement with the home program. One child discontinued due to a heel injury, highlighting the need for individualized support. Conclusions: Personalized standing programs may improve muscle flexibility and functional movement quality in ambulatory children with MMC. Further research is warranted to determine the optimal dosing regimen, ensure safety, and assess long-term functional outcomes. Full article

Accurate prediction of the flow field is crucial to evaluating the aerodynamic performance of an aircraft. While traditional computational fluid dynamics (CFD) methods solve the governing equations to capture both global flow structures and localized gradients, they are computationally intensive. Deep learning-based surrogate models offer a promising alternative, yet often struggle to simultaneously model long-range dependencies and near-wall flow gradients with sufficient fidelity. To address this challenge, this paper introduces the Message-passing And Global-attention block (MAG-BLOCK), a graph neural network module that combines local message passing with global self-attention mechanisms to jointly learn fine-scale features and large-scale flow patterns. Building on MAG-BLOCK, we propose FLOW-GLIDE, a cross-architecture deep learning framework that learns a mapping from initial conditions to steady-state flow fields in a latent space. Evaluated on the AirfRANS dataset, FLOW-GLIDE outperforms existing models on key performance metrics. Specifically, it reduces the error in the volumetric flow field by 62% and surface pressure prediction by 82% compared to the state-of-the-art. Full article

This study proposes an approach to design and develop a prosthetic foot using bamboo laminate, a sustainable and cost-effective material. The proposed design method is an alternative to the current prosthetic foot design methods developed mainly for carbon-fiber reinforced polymer. This design solution entails significant economic and environmental costs. The research is structured into three main phases: an experimental campaign to characterize the reference prosthesis and select materials for the prototype; a dimensional design and prototyping phase using the new materials; and finally, a phase dedicated to testing and comparative evaluation of the results. Static tests, conducted in accordance with ISO 22675 on both the bamboo prototype and a commercial reference model, revealed comparable static performance between the two solutions. The laminated bamboo prosthetic foot exhibits a strength-to-weight ratio comparable to that of carbon fiber laminates. Its stiffness deviates by less than 15%, with an 8% reduction in weight and a 7% increase in volume compared to the carbon fiber counterpart. Therefore, these results confirm the feasibility of laminated bamboo as a viable material for prosthetic foot design. Full article

Planetary gearbox serves as a key transmission component in planetary ball screw actuator systems. Under the action of alternating loads, the stress concentration locations of the planet carrier in actuators with planetary gear trains are prone to fatigue cracks, which can lead to catastrophic system breakdowns. However, due to the complex vibration transmission path and the interference of uninterested vibration components, the characteristic modulation signal is ambiguous, so it is challenging to diagnose this fault. Therefore, this paper proposes a new fault diagnosis method. Firstly, a vibration signal model is established to accurately characterize the amplitude and phase modulation effects caused by cracked carriers, providing theoretical guidance for fault feature identification. Subsequently, three novel sideband evaluators of the modulation signal bispectrum (MSB) and their parameter selection ranges are proposed to efficiently locate the optimal fault-related bifrequency signatures and reduce computational cost, leveraging the effects identified by the model. Finally, a novel health indicator, the mean absolute root value (MARV), is used to monitor the state of the planet carrier. The effectiveness of this method is verified by experiments on the planetary gearbox test rig. The results show that the robustness of the amplitude and phase modulation effect of the cracked carrier in the low-frequency band is significantly higher than that in the high-frequency band, and the initial carrier crack can be accurately identified using this phenomenon under different operating conditions. This study provides a reliable solution for the condition monitoring and health management of the actuation system, which is helpful to improve the safety and reliability of operation. Full article

As network automation and self-organizing networks (SONs) rapidly evolve, edge devices increasingly demand lightweight, real-time, and high-precision classification algorithms to support critical tasks such as traffic identification, intrusion detection, and fault diagnosis. In recent years, cross-entropy (CE) loss has been widely adopted in deep learning classification tasks due to its computational efficiency and ease of optimization. However, traditional CE methods primarily focus on class separability without explicitly constraining intra-class compactness and inter-class boundaries in the feature space, thereby limiting their generalization performance on complex classification tasks. To address this issue, we propose a novel classification loss framework—Contrastive Geometric Cross-Entropy (CGCE). Without incurring additional computational or memory overhead, CGCE explicitly introduces learnable class representation vectors and constructs the loss function based on the dot-product similarity between features and these class representations, thus explicitly reinforcing geometric constraints in the feature space. This mechanism effectively enhances intra-class compactness and inter-class separability. Theoretical analysis further demonstrates that minimizing the CGCE loss naturally induces clear and measurable geometric class boundaries in the feature space, a desirable property absent from traditional CE methods. Furthermore, CGCE can seamlessly incorporate the prior knowledge of pretrained models, converging rapidly within only a few training epochs (for example, on the CIFAR-10 dataset using the ViT model, a single training epoch is sufficient to reach 99% of the final training accuracy.) Experimental results on both text and image classification tasks show that CGCE achieves accuracy improvements of up to 2% over traditional CE methods, exhibiting stronger generalization capabilities under challenging scenarios such as class imbalance, few-shot learning, and noisy labels. These findings indicate that CGCE has significant potential as a superior alternative to traditional CE methods. Full article