Reutlingen University

Purpose Surgical interventions and the intraoperative environment can vary greatly. A system that reliably recognizes the situation in the operating room should therefore be flexibly applicable to different surgical settings. To achieve this, transferability should be focused during system design and development. In this paper, we demonstrated the feasibility of a transferable, scenario-independent situation recognition system (SRS) by the definition and evaluation based on non-functional requirements. Methods Based on a high-level concept for a transferable SRS, a proof of concept implementation was demonstrated using scenarios. The architecture was evaluated with a focus on non-functional requirements of compatibility, maintainability, and portability. Moreover, transferability aspects beyond the requirements, such as the effort to cover new scenarios, were discussed in a subsequent argumentative evaluation. Results The evaluation demonstrated the development of an SRS that can be applied to various scenarios. Furthermore, the investigation of the transferability to other settings highlighted the system’s characteristics regarding configurability, interchangeability, and expandability. The components can be optimized step by step to realize a versatile and efficient situation recognition that can be easily adapted to different scenarios. Conclusion The prototype provides a framework for scenario-independent situation recognition, suggesting greater applicability and transferability to different surgical settings. For the transfer into clinical routine, the system’s modules need to be evolved, further transferability challenges be addressed, and comprehensive scenarios be integrated.

The German National Medication Plan (GNMP) can be a valuable and interoperable data source for clinical studies, due to its digital specification and mandatory provisioning for chronically ill patients. Digital transfer of a patients current GNMP from the Patient Data Management System (PDMS) into electronic case report forms would avoid error prone manual data capturing. It is also essential for studies in practice-based research networks (PBRN), where data capturing must have as little impact as possible on everyday practice. The following issues are currently preventing seamless digital integration: There is no standardized interoperable export of the GNMP from PDMS. In the current form, pharmaceutical catalogs are needed to decode the contained pharmaceutical registration numbers. As accessibility to the pharmaceutical catalogs is restricted, there is no generic access to the actual information needed for study data evaluation. In order to conduct studies, feasible workarounds for these issues had to be implemented in the standard operating procedures, tools and participating GP practices. To overcome the GNMP’s current lack of digital interoperability, the proposed solution combines semi-automated data export from PDMS at the GP practice and manual database search at the study center with a semi-automated processing pipeline to balance workload between GP practices, study management and evaluation.

Condition monitoring (CM) has garnered extensive attention in the era of Industry 4.0 and digital manufacturing. It is crucial to monitor the health status of machinery to ensure reliability, safety, production quality, and effectiveness. Advanced predictive maintenance strategies increase equipment availability and effectiveness by accurately predicting failures, thus facilitating maintenance engineering decisions and preventing unplanned machinery breakdowns. In this research, a novel predictive maintenance strategy is proposed by integrating anomaly detection and fault prognostics, two significant challenges in smart maintenance, into one CM system. For anomaly detection, we developed an intelligent methodology based on the skip convolution generative adversarial network (SCGAN). This network combines a convolutional autoencoder (CAE), generative adversarial network (GAN), and skip connections, forming a robust system to construct health indicators (HIs), effectively and efficiently tracking the degradation status of rolling element bearings with fault identified using the $3\sigma$ criterion. Validation on real experimental datasets demonstrates that the developed HIs show a stable trend during the healthy stage and a marked increase when deterioration is detected. Subsequently, we employ an advanced graph isomorphic network (GIN) for remaining useful life (RUL) prediction. GIN utilizes graph data and graph convolutions (GCs) to map complex relationships between degradation evolution and RUL. This approach outperforms existing deep learning models, such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and CNN-LSTM, providing more accurate RUL prediction.

Many high-quality educational innovations are freely available, and some are known to motivate evidence-based climate and sustainability action. Typically, efforts to propagate educational innovations rely on outreach and word-of-mouth diffusion, but these approaches tend to achieve little. We develop and analyse a dynamic computational model to understand why and to test other propagation strategies. Our analysis reveals that outreach has limited impact and does little to accelerate word-of-mouth adoption under conditions typical in higher education. Instead, we find that community-based propagation can rapidly accelerate adoption, as is also shown by a small number of successful real-world scaling efforts. This approach supports a community of ‘ambassadors’, facilitating and rewarding their sharing the innovation with potential adopters. Community-based propagation can generate exponential growth in adopters, rapidly outpacing outreach and word-of-mouth propagation. Without it, we are unlikely to rapidly scale the educational innovations needed to build urgently needed capacity in sustainability.

The use of surface-enhanced Raman spectroscopy (SERS) in liquid solutions has always been challenging due to signal fluctuations, inconsistent data, and difficulties in obtaining reliable results, especially at very low analyte concentrations. In our study, we introduce a new method using a three-dimensional (3D) SERS substrate made of silica microparticles (SMPs) with attached plasmonic nanoparticles (NPs). These SMPs were placed in low-concentration analyte solutions for SERS analysis. In the first approach to perform SERS in a 3D environment, glycerin was used to immobilize the particles, which enabled high-resolution SERS imaging. Additionally, we conducted time-dependent SERS measurements in an aqueous solution, where freely suspended SMPs passed through the laser focus. In both scenarios, EFs larger than 200 were achieved, which enabled the detection of low-abundance analytes. Our study demonstrates a reliable and reproducible method for performing SERS in liquid environments, offering significant advantages for the real-time analysis of dynamic processes, sensitive detection of low-concentration molecules, and potential applications in biomolecular interaction studies, environmental monitoring, and biomedical diagnostics.

White adipose tissue (WAT) plays a crucial role in energy homeostasis and secretes numerous adipokines with far‐reaching effects. WAT is linked to diseases such as diabetes, cardiovascular disease, and cancer. There is a high demand for suitable in vitro models to study diseases and tissue metabolism. Most of these models are covered by 2D‐monolayer cultures. This study aims to evaluate the performance of different WAT models to better derive potential applications. The stability of adipocyte characteristics in spheroids and two 3D gellan gum hydrogels with ex situ lobules and 2D‐monolayer culture is analyzed. First, the differentiation to achieve adipocyte‐like characteristics is determined. Second, to evaluate the maintenance of differentiated ASC‐based models, an adipocyte‐based model, and explants over 3 weeks, viability, intracellular lipid content, perilipin A expression, adipokine, and gene expression are analyzed. Several advantages are supported using each of the models. Including, but not limited to, the strong differentiation in 2D‐monolayers, the self‐assembling within spheroids, the long‐term stability of the stem cell‐containing hydrogels, and the mature phenotype within adipocyte‐containing hydrogels and the lobules. This study highlights the advantages of 3D models due to their more in vivo‐like behavior and provides an overview of the different adipose cell models.

Fibrosis—excessive scarring after injury—causes >40% of disease-related deaths worldwide. In this misguided repair process, activated fibroblasts drive the destruction of organ architecture by accumulating and contracting extracellular matrix. The resulting stiff scar tissue, in turn, enhances fibroblast contraction—bearing the question of how this positive feedback loop begins. We show that direct contact with profibrotic but not proinflammatory macrophages triggers acute fibroblast contractions. The contractile response depends on αvβ3 integrin expression on macrophages and Piezo1 expression on fibroblasts. The touch of macrophages elevates fibroblast cytosolic calcium within seconds, followed by translocation of the transcription cofactors nuclear factor of activated T cells 1 and Yes-associated protein, which drive fibroblast activation within hours. Intriguingly, macrophages induce mechanical stress in fibroblasts on soft matrix that alone suppresses their spontaneous activation. We propose that acute contact with suitable macrophages mechanically kick-starts fibroblast activation in an otherwise nonpermissive soft environment. The molecular components mediating macrophage-fibroblast mechanotransduction are potential targets for antifibrosis strategies.

Cultured or cultivated meat, animal muscle, and fat tissue grown in vitro, could transform the global meat market, reducing animal suffering while using fewer resources than traditional meat production and no antimicrobials at all. To ensure the appeal of cultured meat to future customers, cultured fat is essential for achieving desired mouthfeel, taste, and texture, especially in beef. In this work we show the establishment of primary bovine adipose-derived stem cell spheroids in static and dynamic suspension culture. Spheroids are successfully differentiated using a single-step protocol. Differentiated spheroids from dynamic cultures maintain stability and viability during 3D bioprinting in edible gellan gum. Also, the fatty acid composition of differentiated spheroids is significantly different from control spheroids. The cells are cultured antibiotic-free to minimize the use of harmful substances. This work presents a stable and bioprintable building block for cultured fat with a high cell density in a 3D dynamic cell culture system.