University of Applied Sciences Upper Austria

The classical complement pathway (CCP) is an essential part of the immune system, activated when complement protein C1 binds to IgG antibody oligomers on the surface of pathogens, infected or malignant cells, culminating in the formation of the membrane attack complex and subsequent cell lysis. IgG oligomers also engage immune effector cells through Fcγ receptors or complement receptors, facilitating antibody‐dependent cellular cytotoxicity and phagocytosis. Understanding the factors that drive IgG oligomerization is thus crucial for improving IgG‐based therapies. Herein, a kinetic model to predict oligomer formation based on IgG concentration, antigen density, IgG subclass, Fc mutants, and oligomerization inhibitors like staphylococcal protein A is developed. The underlying molecular interactions in single molecule force spectroscopy and grating coupled interferometry experiments are characterized. By fitting experimental data from high‐speed atomic force microscopy experiments, key rate constants and thermodynamic parameters, including free energy changes associated with oligomerization and apply the model to predict complement‐mediated lysis in liposomal vesicle‐based assays, are further quantified. The presented mechanistic framework may serve as a basis for optimizing antibody engineering and pharmacokinetic/pharmacodynamic modeling in the context of immunotherapies exploiting the CCP.

Maternal metabolic factors are increasingly recognized as critical pre-conceptional determinants of fertility outcomes. To investigate how metabolic health influences female fertility, we investigated the molecular composition of follicular fluid (FF), with a focus on microRNA (miRNA) expression. Blood and FF samples from 15 women undergoing controlled ovarian stimulation were examined in a pilot study. Clinical traits (glucometabolic markers, lipid profiles, liver function markers, inflammatory cytokines, and hormonal parameters) and oocyte outcomes were measured and recorded. Elevated plasma bilirubin levels were associated with a distinct miRNA profile in FF, characterized by an enrichment of anti-inflammatory miRNAs, including miR-146a-5p, miR-146b-5p, miR-487b-3p, and miR-21-5p. Bioinformatic analysis revealed that these miRNAs directly target key inflammatory mediators, including IL6, COX2, TLR4, IRAK1, and NFKB1, suggesting a regulatory role in intra-follicular inflammation. Furthermore, patients with a fertilization rate of ≤50% exhibited higher transcript levels of miRNAs associated with elevated plasma bilirubin. Our findings provide a novel perspective on the growing body of evidence supporting bilirubin's regulatory properties, including anti-oxidative and anti-inflammatory effects and highlight the relationship between plasma bilirubin and FF miRNA expression. The observed associations between bilirubin levels, follicular fluid miRNA composition, and oocyte quality underscore the critical influence of metabolic factors on reproductive outcomes. This exploratory work provides a foundation for further studies to investigate the functional role of plasma bilirubin in follicular physiology and its potential as a biomarker to optimize fertility treatments.

Rationale: Pulmonary hypertension (PH) poses a significant health threat. Current biomarkers for PH lack specificity and have poor prognostic capabilities. Objectives: To develop better biomarkers for PH that are useful for patient identification and management. Methods: Explorative analysis of a broad spectrum of metabolites in PH patients, healthy controls and disease controls in a training and a validation cohort and in vitro studies on human pulmonary arteries. Measurements: High resolution mass spectrometry in 233 subjects coupled with machine learning analysis. Histologic and gene expression analysis with focus on lipid metabolism in human pulmonary arteries (PA) of idiopathic pulmonary arterial hypertension lungs (IPAH) and assessment of the acute effects of extrinsic fatty acids (FAs). Results: We enrolled a training cohort of 74 PH patients, 30 disease controls without PH, and 65 healthy controls, and an independent validation cohort of 64 subjects. Among other metabolites, the FAs were significantly increased. Machine learning showed a high diagnostic potential for PH. Additionally, we developed fully explainable lipid ratios with exceptional diagnostic accuracy for PH (AUC 0.89 training cohort, 0.90 external validation cohort), outperforming machine learning results. These ratios were also prognostic and complemented established clinical markers and scores, significantly increasing their hazard ratios for mortality risk. IPAH lungs showed lipid accumulation and altered expression of lipid homeostasis-related genes. In human PA smooth muscle and endothelial cells, FAs caused excessive proliferation and barrier dysfunction, respectively. Conclusion: Our metabolomics approach suggests that lipid alterations in PH provide diagnostic and prognostic information, complementing established markers. These alterations may reflect pathologic changes in the pulmonary arteries of PH patients.

The solution of two-point boundary value problems (TPBVPs) in multibody system dynamics is a tough challenge. In this article, a double shooting method (DSM) tailored for TPBVPs is presented. The method builds upon the classical single shooting method while incorporating optimization strategies to overcome numerical instability and sensitivity to initial guesses. Conventional shooting methods are associated with some numerical problems: The numerical gradient computation for updating initial values causes problems in many cases, since the disturbance parameter for determining the numerical derivative must be selected appropriately in order to achieve sufficient accuracy. Moreover, integrating the initial value problem can be numerically challenging, especially when the interval is large and the differential equations have unstable modes. The DSM is designed to address these challenges. Therefore, the method incorporates a discrete costate variable approach to cope with the numerical gradient computation, which is often the Achilles' heel of classical shooting methods. Finally, the method is formulated using a discrete implicit integration scheme to determine, as a first example, a double pendulum upswing maneuver and, as a second example, the energy-optimal control of a robot multibody model.

Virtual reality (VR) allows to embody avatars. Coined the Proteus effect, an avatar's visual appearance can influence users' behavior and perception. Recent work suggests that athletic avatars decrease perceptual and physiological responses during VR exercise. However , such effects can fail to occur when users do not experience avatar ownership and identification. While customized avatars increase body ownership and identification, it is unclear whether they improve the Proteus effect. We conducted a study with 24 participants to determine the effects of athletic and non-athletic avatars that were either customized or randomly assigned. We developed a customization editor to allow creating customized avatars. We found that customized avatars reduced perceived exertion. We also found that athletic avatars decreased heart rate while holding weights, however, only when being customized. Results indicate that customized avatars can positively influence users during physical exertion. We discuss the utilization of avatar customization in VR exercise systems.

Virtual reality (VR) enables users to experience avatars as their own body. Avatars can induce the Proteus effect-a shift in behavior and perception conforming to the avatar's appearance. Previous work found that sweaty avatars can reduce the perceived exertion while cycling in VR. However, it is unknown if the effects of sweaty avatars depend on the cycling intensity and if they can also influence physiological responses. Hence, we conducted a study to investigate the effects of sweaty avatars on breathing responses and perception of effort while cycling at low and high intensities. We found that participants' oxygen consumption and perception of effort was only influenced by the sweaty appearance of the avatar during low-intensity cycling. Results suggest that an avatar's sweaty appearance can influence breathing and perceptual responses, however, only during low-intensity workloads. Our work contributes to the Proteus effect on physiological and perceptual responses in VR.

Navigating is essential in many video games. However, previous work suggests that many games still suffer from navigational problems that decrease enjoyment. In this paper, we focus on "Desire Paths", informal trails collectively created by pedestrians representing the most convenient route. While they are known to be useful wayfinding aids, it is unclear how they affect navigation and experience in games. We therefore investigated diegetically visualized player trajectory data in a 2D game through virtual footprints that were persistently visible for all subsequent players. Through a mixed-methods study involving 50 participants, we found that virtual footprints improved navigation by guiding players to points of interest and reducing disorientation for early players. However, visual clutter from excessive footprints reduced their effectiveness in later stages. They also fostered a sense of community, especially for late-stage players and prompted exploration of yet undiscovered areas. We further discuss design implications and future research directions.

Background: Adherence to clinical guidelines supports high quality patient care. Conformance checking, a feature of process mining, can potentially automate the assessment of adherence to clinical guidelines in practice. Objectives: This paper investigates appropriate conformance checking in practice. Methods: Conformance checking in practice was simulated with generated test data, a FHIR server and process mining tools. A corresponding literature review was conducted in parallel. Results: Activities of clinical guidelines or in healthcare processes should be coded using clinical nomenclature to support conformance checking. Conclusion: SNOMED CT should be used as a nomenclature and activities should be coded with SNOMED concepts of the type "procedure".

This paper introduces a Circular Economy Framework integrated with a Digital Product Passport (CEF-DPP) to advance sustainable mechatronics design. The framework combines a Design Decision Support System (DDSS) and a Design Workflow Process (DWP) to embed sustainability principles throughout the product lifecycle, leveraging digital tools for transparency, traceability, and stakeholder collaboration. Validated through a case study of the EBS SGI-G V2 electric motor, the CEF-DPP demonstrates a 45.2% reduction in annual CO2 emissions and significant material savings, such as 100% reuse of aluminum and electrical sheets, achieved through remanufacturing and circular design. Despite challenges like high upfront costs and technical complexities, the framework offers a scalable approach to sustainable product design, aligning with circular economy goals and regulatory requirements.

Background and Aims Appendicitis is the most common surgical emergency in pediatric patients, requiring timely diagnosis to prevent complications. This study introduces an innovative approach by integrating clinical, laboratory, and imaging features with advanced machine‐learning techniques to enhance diagnostic accuracy in pediatric appendicitis. Methods A retrospective analysis was conducted on 782 pediatric patients from the Regensburg Pediatric Appendicitis Data set. Clinical scores, laboratory markers, and imaging findings were analyzed. Statistical comparisons were performed using independent t‐tests and χ² tests, with significance set at p < 0.05. Predictive models, including logistic regression and machine learning classifiers, were developed and evaluated using accuracy, precision, recall, and F1‐score. Results Significant differences were observed in clinical scores (e.g., Alvarado Score and Pediatric Appendicitis Score) and laboratory markers (e.g., WBC count and neutrophil percentage) between appendicitis (AA) and non‐appendicitis (Non‐AA) groups (p < 0.001). Imaging features, including appendix diameter, also demonstrated diagnostic value. Among predictive models, the Random Forest classifier achieved the highest accuracy (94.5%), with strong precision (93.8%) and recall (95.2%) for appendicitis diagnosis. Conclusion This study represents a novel application of machine learning models, particularly Random Forest, to enhance diagnostic accuracy for pediatric appendicitis. The integration of clinical, laboratory, and imaging features offers a comprehensive and precise diagnostic framework. Further validation in diverse populations is recommended.

Background Accurate positioning of the glenoid component in reverse total shoulder arthroplasty is important since it reduces prosthesis-related complications. Conventional instrumentation is still the gold standard. However, patient-specific instrumentation using 3-D printed guides, as well as computer-assisted navigation, is gaining more and more importance. Augmented reality has been established to enable the surgeon to have more precision in the positioning and inclination of the glenoid component. A new technique called “NextAR” (©Medacta) supports the surgeon during operation by guidance on bone preparation, instrument navigation, and implant placement, using virtual reality goggles or displays. The aim of this study was to determine a learning curve for reversed total shoulder arthroplasty by a high-volume single shoulder surgeon using the NextAR system. Methods We performed retrospective data analysis of the first 20 cases of one high-volume single shoulder surgeon performing reversed total shoulder arthroplasty using the NextAR system. Parameters such as anesthesia time, surgical time, length of hospital stay, classification of the American Society of Anesthesiologists, body mass index, intraoperative blood loss, and complications were analyzed. Results We found significant decreases in measures like surgery time (p = 0,001), amount of blood loss during surgery (p = 0,005), and anesthesia time (p = 0,08). A remarkable turning point of blood loss occurs within the fifteenth surgery. A significant reduction of the operation time is shown at the fourteenth operation. Looking at blood loss and operation time together using a standardized cumulative curve, a significant reduction of the cumulative score is found within the fifteenth surgery. Conclusion Significance was found in surgery time as well as in the amount of blood loss. However, these results may not be generalizable since they depend on further conditions. Future studies could provide information about other outcome parameters and learning curves of several surgeons performing reversed total shoulder arthroplasty with augmented reality applications.

Developing and optimizing manufacturing processes for composite components is commonly supported by finite element (FE) simulations. Initial concepts are modeled and parametric studies are conducted to determine optimum process parameters. Built-in application programming interfaces (APIs) typically allow for a script-based automation of systematic model modifications for many FE solvers. However, the evaluation of simulation results typically depends on a manual inspection by experts, despite its repetitive nature. This study aims to use APIs to develop a fully-automated method for identifying and evaluating critical features and thus, the quality of draping simulations. The focus thereby lies on providing a validated framework for the automated evaluation of draping simulation results, rather than claiming perfect virtual representation of experimental draping. Three different metrics (deviations of fiber angles, the boundary contour and topological defects) are used to determine the overall draping quality of simulation results. With the developed routine, all quality metrics can be estimated quite well for simulation results. The routine is designed to be extended for the use with experimental data for a reliable real-life quality assessment.

The exogenous shock of COVID-19 strongly challenged Greek retail, especially in small and micro-sized enterprises (SMiEs). In particular, the decline of customer visits in physical retail stores caused by lockdowns and other restrictive measures led to a business threatening reduction of sales. As a result, adjusting business model elements became one of the first priorities for Greek business owners. This paper focuses on the effects of the COVID-19 pandemic on business model elements of Greek SMiE retailers. To investigate this impact, eight expert interviews with Greek SMiE retailers were conducted. Results show that the pandemic situation directly and indirectly affected most of the business model elements of Greek SMiE retailers. Naturally, each studied company displayed unique characteristics as well as different needs and priorities, which facilitated the specific changes in their business model in order to become profitable. However, especially opportunities offered by digital technologies, such as web shops, online advertising, and online customer engagement, were most frequently mentioned to influence business model elements. Thus, this research contributes to the existing body of knowledge in the field of business models and information systems, by providing a business owner perspective on how business model elements were adjusted to the circumstances of the COVID-19 pandemic.

Background Maintaining intestinal health is crucial for the overall well-being and productivity of livestock, as it impacts nutrient absorption, immune function, and disease resistance. Oxidative stress and inflammation are key threats to intestinal integrity. This study explored the antioxidant, anti-inflammatory, and barrier-strengthening properties of a fermented plant macerate (FPM) derived from 45 local herbs, using a specifically developed fermentation process utilizing the plants’ inherent microbiota to enhance bioactivity and sustainability. Results In vitro experiments with IPEC-J2 cells showed that FPM significantly reduced intracellular reactive oxygen species (ROS) levels, improved barrier integrity, and enhanced cell migration under stress. Similar antioxidant effects were observed in THP-1 macrophages, where FPM reduced ROS production and modulated inflammatory responses by decreasing pro-inflammatory cytokines [tumor necrosis factor alpha (TNF-α), monokine induced by gamma interferon (MIG), interferon-inducible T cell alpha chemoattractant (I-TAC), macrophage inflammatory proteins (MIP)-1α and -1β] and increasing anti-inflammatory interleukin (IL)-10 levels. Mechanistic studies with HEK-Blue reporter cell lines revealed that FPM inhibited nuclear factor kappa B (NF-κB) activation via a toll-like receptor (TLR)4-independent pathway. In vivo, FPM significantly reduced ROS levels in Drosophila melanogaster and improved activity and LT 50 values in Caenorhabditis elegans under oxidative stress, although it did not affect intestinal barrier integrity in these models. Conclusion The findings indicate that FPM shows promising application as a functional feed supplement for improving intestinal health in livestock by mitigating oxidative stress and inflammation. Further studies, including livestock feeding trials, are recommended to validate these results.

Information security is a critical issue for small and medium-sized enterprises (SMEs) around the world. These organisations face an increasing number of security incidents and the sophistication of attacks. In order to remain competitive and protect their and their customers’ critical information, it is essential that SMEs can manage their cybersecurity risks appropriately. Accordingly, it is important that these SMEs can rely on tailored information security assessments and frameworks. However, there is a scarcity of knowledge regarding their specific needs and the practical implementation of cybersecurity within these organisations. To address this knowledge gap, an exploratory study was conducted on the SME cybersecurity situation, with a particular focus on the implementation level of cybersecurity controls within SMEs in Austria and Germany. We surveyed 30 SMEs regarding their cybersecurity implementation situation in 2023. Our findings show, among other things, a very heterogeneous picture regarding the implementation level of cybersecurity controls and outline areas for action.

Purpose: To evaluate the marginal gap of two-piece polyetheretherketone (PEEK) abutments fabricated with different methods, before and after thermal cycling, while also focusing on their pull-off bond strength. Materials and Methods: A two-piece abutment was virtually designed after digitizing a titanium-base (Ti-base) abutment. This design was used to fabricate printed (P-PEEK), milled (M-PEEK), and heat-pressed (HP-PEEK) PEEK abutments (n = 8). The marginal gaps of all abutments were evaluated under a stereomicroscope (15 points on each side, ×40 magnification), before and after thermal cycling (10,000 cycles, 5 • C-55 • C). Then, all abutments were subjected to a pull-off bond strength test. The marginal gap data were analyzed with a generalized linear model, while the pull-off bond strength data were analyzed with one-way analysis of variance and Tukey tests (α = 0.05). Results: The marginal gaps were affected by the interaction between the fabrication method and aging condition, as well as by the fabrication method and aging condition (p ≤ 0.003). HP-PEEK abutments before thermal cycling had the lowest gap, whereas M-PEEK abutments after thermal cycling mostly had the highest (p ≤ 0.042). Thermal cycling increased the marginal gap of HP-PEEK (p < 0.001). M-PEEK had the lowest and HP-PEEK had the highest pull-off bond strength (p < 0.001). Most of the failures of P-PEEK and M-PEEK abutments were mixed. Conclusions: The tested abutments had marginal gaps below the clinically accept- able threshold of 120 µm, both before and after thermal cycling. HP-PEEK abutments may be more resistant to dislodgment from the Ti-base abutments than P-PEEK and M-PEEK abutments.