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In vitro and in vivo delivery of mRNA to joint cells using polymeric nanoparticles
No full textOsteoarthritis (OA) is a progressive and degenerative disease of the joints, characterized by inflammation and loss of cartilage. Recently, mRNA therapies have emerged as promising disease-modifying treatments for cartilage repair and regeneration. Poly(amidoamine)-based polymeric nanoparticles (PAA-based NPs) were previously developed for intracellular mRNA delivery in chondrocytes, showing high biocompatibility and transfection efficiency. In this work, we aimed to evaluate this delivery system in models simulating the complex joint environment and in vivo in rat knee joints. For this purpose, cationic uncoated NPs and neutral PEG-coated NPs were formulated to test mRNA delivery in different models: (1) a 2D culture of chondrocytes supplemented with synthetic synovial fluid, (2) a cartilage-on-chip platform, (3) an ex vivo culture of mouse knee joints, and (4) an in vivo OA rat model. In the presence of synovial fluid, the PEG-coated NPs showed favorable physicochemical properties, higher cell uptake and equivalent GFP expression as uncoated NPs in the 2D cell culture. Similar observations were made using the cartilage-on-chip platform. In contrast, both NPs appeared to display cartilage penetration and uptake by tissue-resident chondrocytes in ex vivo joint culture. Upon intra-articular administration in vivo, the PAA-based NPs did not affect cartilage integrity in healthy nor OA rat knee joints, although enhanced synovial inflammation was observed. Uncoated NPs showed prolonged retention compared to PEG-coated NPs and higher luciferase expression in OA knee joints than in healthy joints of rats, whereas no difference was found for coated NPs. These results suggest that electrostatic interactions between cationic NPs and the anionic components of the extracellular matrix play a significant role in mRNA delivery to the articular cartilage, and that disease status may affect delivery of nucleic acids dependent on NP properties. In conclusion, PAA-based NPs are a promising platform for intra-articular mRNA delivery in the joints. Statement of significance: In this study, we investigate the application of poly(amidoamine)-based polymeric nanoparticles (PAA-based NPs) for mRNA delivery in the joints, aiming for use in osteoarthritis (OA) treatment. The formulations were tested in in vitro models mimicking the joint environment, and also following intra-articular injection ex vivo and in vivo (OA-induced rats). We demonstrate for positively charged uncoated NPs higher in vivo gene expression in OA knee joints than neutral PEG-coated NPs. However, PEG-coated NPs induced more consistent gene expression in both healthy and OA knee joints. These findings highlight the potential of PAA-based NPs for osteoarthritis research and how the interplay between the NP properties, joint biology and disease state can affect mRNA delivery.</p
Stretching Boundaries:Nurses’ Perceptions on Job Demands and Resources in Hospital Float Pools
No full textBackground: Float pools are increasingly used in healthcare to enhance staffing flexibility and efficiency. However, the impact of floating on nurses remains underexplored. Challenges may include adjusting to different ward routines and limited team integration.Aim: To explore the perceived demands and resources associated with hospital float pool work, comparing experiences of nurses in intraorganizational pools with expectations of those preparing for interorganizational floating.Methods: This qualitative study, guided by the job demands–resources (JD-R) model, involved semistructured interviews with 27 nurses across five Dutch hospitals. Participants included nurses currently working in intraorganizational float pools and those anticipating working in a float pool across organizations.Results: Nurses in intraorganizational float pools generally reported job satisfaction, experiencing minor demands such as limited team acceptance. Learning opportunities and variation in tasks were key resources. Effective coping was supported by openness, confidence, and communication skills. In contrast, nurses not yet deployed but are anticipating interorganizational floating expected greater demands, including adapting to varying protocols and working across multiple hospital cultures. They emphasized the need for extrinsic resources such as rewards and described personal challenges such as time management and a preference for routine.Conclusion: Interorganizational floating is perceived as more demanding than intraorganizational float pool work. However, experienced nurses often reframe demands as manageable. Findings highlight the importance of a person-centered float pool design that aligns with individual characteristics and support needs. Tailoring float pool policies may enhance job satisfaction, reduce burnout, and support retention among floating nurses
Blockchain Technology from The Supply Chain Perspective:A Systematic Literature Review
No full textBlockchain technology has emerged as a transformative force in supply chain management, offering significant advantages such as enhanced transparency, traceability, trust, and immutability. These features address critical challenges in modern supply chains, including inefficiencies, fraud, and lack of trust among stakeholders. Despite its potential, the integration of blockchain technology into supply chains remains underexplored in both academic and industrial contexts. This systematic literature review aims to bridge this knowledge gap by examining the benefits and challenges of blockchain technology in supply chain applications. The findings highlight key advantages such as improved traceability, transparency, and cost efficiency, while also identifying challenges like high implementation costs, data privacy concerns, and technological immaturity. The review concludes that blockchain technology holds significant promise for revolutionizing supply chain management, but further research and practical applications are needed to fully realize its potential. This study provides a comprehensive foundation for future research and offers valuable insights for practitioners considering blockchain adoption in their supply chain operations
Laboratory-based in situ and operando tricolor x-ray photoelectron spectroscopy
No full textInnovative approaches to study buried interfaces and heterogeneous interactions under reaction conditions are crucial for advancing energy and catalytic materials. Our near-ambient pressure x-ray photoelectron spectroscopy (NAP-XPS) setup is equipped with a tricolor x-ray source, with Al Kα, Ag Lα, and Cr Kα excitation energies, enabling information depth-selective operando and in situ analysis of solid-liquid, solid-gas, and solid-solid interfaces. We present three case studies to demonstrate the systems' capabilities. First, we compare experimental depth profiling of a LaMnO3/LaFeO3/Nb:SrTiO3 multilayer with SESSA (simulation of electron spectra for surface analysis) simulations. Second, we examine the oxidation and reduction of FexOy as a function of environment and temperature. Last, the Pt/liquid electrolyte interface is examined, revealing surface oxidation in the absence of bulk oxidation. As our results confirm, the unique combination of a NAP-XPS with the tricolor x-ray source empowers laboratory-based in situ and operando XPS characterization of advanced materials under reaction conditions in a wide range of applications.</p
Challenges and Opportunities for Statistics in the Era of Data Science
No full textStatistics as a scientific discipline is currently facing the great challenge of finding its place in data science once more. At the beginning of the last century, the development of the discipline of statistics was initiated by data-related research questions. Nowadays, it is often viewed to have not kept up with the current developments in data science, which are largely focused on algorithmic, exploratory, and computational aspects and often driven by other disciplines, such as computer science. However, statistics can—and should—contribute to the advances of data science. Of most interest are the strengths of statistics, such as the mathematical focus that leads to theoretical guarantees. This includes methods for formal modeling, hypothesis tests, uncertainty quantification, and statistical inference. Of particular interest are also established statistical frameworks to handle causality or data deficiencies such as dependence, missingness, biases, or confounding.This article summarizes the findings of a discussion workshop on the topic that was held in June 2023 in Hannover, Germany. The discussion centered around the following questions: How must statistics be set up so that it can contribute (more) to modern data science? In which direction should it develop further? Which strengths can already be used now? What conditions must be created so that this can succeed? What can be done to arrive at a common language? What is the added value of formal modeling, inference, and the mathematical perspective taken in statistics
Deep-ELA:Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems
No full textIn many recent works, the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize single-objective continuous optimization problems has been demonstrated. These numerical features provide the input for all kinds of machine learning tasks in the domain of continuous optimization problems, ranging, for example, from High-level Property Prediction to Automated Algorithm Selection and Automated Algorithm Configuration. Without ELA features, analyzing and understanding the characteristics of single-objective continuous optimization problems is-to the best of our knowledge-very limited. Yet, despite their usefulness, as demonstrated in several past works, ELA features suffer from several drawbacks. These include, in particular, (1) a strong correlation between multiple features, as well as (2) its very limited applicability to multiobjective continuous optimization problems. As a remedy, recent works proposed deep learning-based approaches as alternatives to ELA. In these works, among others, point-cloud transformers were used to characterize an optimization problem's fitness landscape. However, these approaches require a large amount of labeled training data. Within this work, we propose a hybrid approach, Deep-ELA, which combines (the benefits of) deep learning and ELA features. We pre-trained four transformers on millions of randomly generated optimization problems to learn deep representations of the landscapes of continuous single- and multiobjective optimization problems. Our proposed framework can either be used out of the box for analyzing single- and multiobjective continuous optimization problems, or subsequently fine-tuned to various tasks focusing on algorithm behavior and problem understanding.</p
High‐Throughput Single‐Cell Analysis of Local Nascent Protein Deposition in 3D Microenvironments via Extracellular Protein Identification Cytometry (EPIC) (Adv. Mater. 6/2025):Graphical Abstract
No full textKnowledge of the extracellular matrix drives our understanding of cell behavior. However, current analysis methods are limited to either bulk or low-throughput single-cell analysis, thus masking the heterogeneity in matrix deposition. Extracellular protein identification cytometry (EPIC) combines the high-throughput single-cell analysis of flow cytometry with engineered microniche 3D cell culture, jointly enabling in situ matrix analysis of large cell populations. More details can be found in article number 2415981 by Jeroen Leijten and co-workers
Assessment of Global and Detailed Chemical Kinetics in Supercritical Combustion for Hydrogen Gas Turbines
No full textSupercritical combustion is a promising technique for improving the efficiency and reducing the emissions of next-generation gas turbines. However, accurately modeling combustion under these conditions remains a challenge, particularly due to the complexity of chemical kinetics. This study aims to evaluate the applicability of a reduced global reaction mechanism compared to the detailed Foundational Fuel Chemistry Model 1.0 (FFCM-1) when performing hydrogen combustion with supercritical carbon dioxide and argon as diluents. Computational fluid dynamics simulations were conducted in two geometries: a simplified tube for isolating chemical effects and a combustor with cooling channels for practical evaluation. The analysis focuses on the evaluation of velocity, temperature, and the water vapor mass fraction distributions inside the combustion chamber. The results indicate good agreement between the global and detailed mechanisms, with average relative errors below 2% for supercritical argon and 4% for supercritical carbon dioxide. Both models captured key combustion behaviors, including buoyancy-driven flame asymmetry caused by the high density of supercritical fluids. The findings suggest that global chemistry models can serve as efficient tools for simulating supercritical combustion processes, making them valuable for the design and optimization of future supercritical gas turbine systems.</p
Impact of NH<sub>4</sub><sup>+</sup>-N on Organic Micropollutant Removal and Antibiotic Resistance Gene Occurrence during Simulated Riverbank Filtration
No full textOrganic micropollutants (OMPs) facilitate the spread of antibiotic resistance genes (ARGs). Ammonia-oxidizing microorganisms (AOMs) are crucial for OMP degradation during riverbank filtration (RBF) and significantly influenced by NH4+-N concentrations. However, the effect of NH4+-N on OMP removal and ARG occurrence in RBF remains unclear. This study aimed to examine the effects of low (∼0.1 mg/L) and high (∼2.2 mg/L) NH4+-N concentrations on OMP removal, ARG occurrence, and microbial communities. NH4+-N addition had no significant effect on the removal of 108 out of 128 OMPs, suggesting that other factors primarily govern the removal process. Notably, NH4+-N addition enhanced the removal of 20 OMPs by 3-70%, including three quinolones (e.g., flumequine), indicating its promotion of specific OMP removals. This effect may primarily result from NH4+-N enhancing OMP biotransformation through the stimulation of AOMs (particularly AOA and comammox) and heterotrophs (e.g., Bradyrhizobium). Furthermore, NH4+-N addition significantly reduced the abundance of eight ARGs, including quinolone ARGs, likely due to its inhibition of antibiotic-resistant bacteria. Additionally, we hypothesize that NH4+-N alleviates OMP selective pressure on microorganisms by promoting OMP conversion through AOMs. This study enhances the understanding of microbe-mediated OMP removal in the presence of NH4+-N and its impact on ARG occurrence during RBF.</p
Modeling Oil-Separation Properties of Lubricating Greases
No full textBase oil separation from the thickener structure of a lubricating grease, also known as “bleed,” is an essential process for grease lubrication of bearings. To get more insight in this process, a model is presented to describe the bleed behavior in terms of grease parameters such as base oil content, base oil-thickener affinity, thickener permeability and elasticity, and viscosity of the base oil. With this model the bleed rate can be calculated for a given external pressure gradient. Blotting paper is used to create this external pressure gradient as well as to measure the amount of bled oil. The model calculations are compared with experimental results of the bleed rate of grease samples with varying initial oil content and describe the overall bleeding process quite well.</p