Designing a Patient-Centred Educational Extracorporeal Membrane Oxygenation Simulator (EduECMO) – A training needs analysis with international experts

Introduction: Extracorporeal membrane oxygenation (ECMO) is a complex, invasive technique to provide prolonged cardiac and respiratory support. A variety of simulators are available to train advanced technical skills and interdisciplinary team communication. However, high-fidelity simulators are lacking, yet are needed. In the EduECMO project, such a simulator is designed following the design method of Van Meurs. This design process starts with a training needs analysis (TNA) to investigate mission, task, trainee, and training. This study aims to perform an ECMO simulator TNA, and provide guidelines for ECMO simulator design. Methods: Training needs assessment was based on the INACSL Standards of Best Practice. Experts were invited by email to participate in an online survey (July-October 2023). All participants signed informed consent. The study was approved by the University of Twente Ethics Committee (NES:230117). Descriptive statistics were used to describe the data. Results: 41 participants from 11 countries responded. Participants were medical specialists (n=23) and allied healthcare professionals, e.g. nurses, perfusionists (n=18). The number of ECMO cases per year varied between centres from less than 6 cases (18%) to over 30 cases (40%) per year. Participants indicated the main tasks for an ECMO simulator to be provision of optimal diagnostics, cannulation, circuit priming, and monitoring. Respondents stated that trainees should train their skills on an ECMO simulator regardless of background. Desired simulator parts should allow for patient-specific differences in sex, skin tone, and body surface area. ECMO challenges and risks should be addressed in varying scenarios. Discussion &amp; Conclusion: The TNA resulted in implementable guidelines for the EduECMO simulator. Strengths of this study were that an international multidisciplinary expert group from low-, mid-, and high-volume ECMO centres was included. Follow-up observations of experts performing ECMO are proposed to validate findings from the survey and identify attitudes of the learners.</p

Electromyography-driven musculoskeletal models with time-varying fatigue dynamics improve lumbosacral joint moments during lifting

Muscle fatigue is prevalent across different aspects of daily life. Tracking muscle fatigue is useful to understand muscle overuse and possible risk of injury leading to musculoskeletal disorders. Current fatigue models are not suitable for real-world settings as they are either validated using simulations or non-functional tasks. Moreover, models that capture the changes to muscle activity due to fatigue either assume a linear relationship between muscle activity and muscle force or utilize a simple muscle model. Personalised electromygraphy (EMG)-driven musculoskeletal models (pEMS) offer person-specific approaches to model muscle and joint kinetics during a wide repertoire of daily life tasks. These models utilize EMG, thus capturing central fatigue-dependent changes in multi-muscle bio-electrical activity. However, the peripheral muscle force decay is missing in these models. Thus, we studied the influence of fatigue on a large scale pEMS of the trunk. Eleven healthy participants performed functional asymmetric lifting task. Average peak body-weight normalized lumbosacral moments (BW-LM) were estimated to be 2.55 ± 0.26 Nm/kg by reference inverse dynamics. After complete exhaustion of the lower back, the pEMS overestimated the peak BW-LM by 0.64 ± 0.37 Nm/kg. Then, we developed a time-varying muscle force decay model resulting in a time-varying pEMS (t-pEMS). This reduced the difference between BW-LM estimated by the t-pEMS and reference to 0.49 ± 0.14 Nm/kg. We also showed that five fatiguing contractions are sufficient to calibrate the t-pEMS. Thus, this study presents a person and muscle specific model to track fatigue during functional tasks.</p

TREE:Tree Regularization for Efficient Execution

The rise of machine learning methods on heavily resource constrained devices requires not only the choice of a suitable model architecture for the target platform, but also the optimization of the chosen model with regard to execution time consumption for inference in order to optimally utilize the available resources. Random forests and decision trees are shown to be a suitable model for such a scenario, since they are not only heavily tunable towards the total model size, but also offer a high potential for optimizing their executions according to the underlying memory architecture. In addition to the straightforward strategy of enforcing shorter paths through decision trees and hence reducing the execution time for inference, hardware-aware implementations can optimize the execution time in an orthogonal manner. One particular hardware-aware optimization is to layout the memory of decision trees in such a way, that higher probably paths are less likely to be evicted from system caches. This works particularly well when splits within tree nodes are uneven and have a high probability to visit one of the child nodes. In this paper, we present a method to reduce path lengths by rewarding uneven probability distributions during the training of decision trees at the cost of a minimal accuracy degradation. Specifically, we regularize the impurity computation of the CART algorithm in order to favor not only low impurity, but also highly asymmetric distributions for the evaluation of split criteria and hence offer a high optimization potential for a memory architecture-aware implementation. We show that especially for binary classification data sets and data sets with many samples, this form of regularization can lead to an reduction of up to approximately four times in the execution time with a minimal accuracy degradation

¹⁸F-Sodium fluoride PET-CT visualizes disease activity in chronic nonbacterial osteitis in adults

Chronic nonbacterial osteitis (CNO) is a rare disease spectrum, which lacks biomarkers for disease activity. Sodium fluoride-18 positron emission tomography/computed tomography ([18F]NaF-PET/CT) is a sensitive imaging tool for bone diseases and yields quantitative data on bone turnover. We evaluated the capacities of [18F]NaF-PET/CT to provide structural and functional assessment in adult CNO. A coss-sectional study was performed including 43 adult patients with CNO and 16 controls (patients referred for suspected, but not diagnosed with CNO) who underwent [18F]NaF-PET/CT at our expert clinic. Structural features were compared between patients and controls, and maximal standardized uptake values (SUVmax [g/mL]) were calculated for bone lesions, soft tissue/joint lesions, and reference bone. SUVmax was correlated with clinical disease activity in patients. Structural assessment revealed manubrial and costal sclerosis/hyperostosis and calcification of the costoclavicular ligament as typical features associated with CNO. SUVmax of CNO lesions was higher compared with in-patient reference bone (mean paired difference: 11.4; 95% CI: 9.4–13.5; p &lt; .001) and controls (mean difference: 12.4; 95%CI: 9.1–15.8; p &lt; .001). The highest SUVmax values were found in soft tissue and joint areas such as the costoclavicular ligament and manubriosternal joint, and these correlated with erythrocyte sedimentation rate in patients (correlation coefficient: 0.546; p &lt; .002). Our data suggest that [18F]NaF-PET/CT is a promising imaging tool for adult CNO, allowing for detailed structural evaluation of its typical bone, soft-tissue, and joint features. At the same time, [18F]NaF-PET/CT yields quantitative bone remodeling data that represent the pathologically increased bone turnover and the process of new bone formation. Further studies should investigate the application of quantified [18F]NaF uptake as a novel biomarker for disease activity in CNO, and its utility to steer clinical decision making.</p

Leveraging the potential of the German operating room benchmarking initiative for planning:A ready-to-use surgical process data set

We present a freely available data set of surgical case mixes and surgery process duration distributions based on processed data from the German Operating Room Benchmarking initiative. This initiative collects surgical process data from over 320 German, Austrian, and Swiss hospitals. The data exhibits high levels of quantity, quality, standardization, and multi-dimensionality, making it especially valuable for operating room planning in Operations Research. We consider detailed steps of the perioperative process and group the data with respect to the hospital’s level of care, the surgery specialty, and the type of surgery patient. We compare case mixes for different subgroups and conclude that they differ significantly, demonstrating that it is necessary to test operating room planning methods in different settings, e.g., using data sets like ours. Further, we discuss limitations and future research directions. Finally, we encourage the extension and foundation of new operating room benchmarking initiatives and their usage for operating room planning

Ammonia combustion and emissions in practical applications:A review

Ammonia is emerging as a viable alternative to fossil fuels in combustion systems, aiding in the reduction of carbon emissions. However, its use faces challenges, including NOx emissions and low flame speed. Innovative approaches and technologies have significantly advanced the development and implementation of ammonia as a zero-carbon fuel. This review explores current advancements in using ammonia as a fuel substitute, highlighting the complexities that various systems need to overcome before reaching full commercial maturity in support of practical decarbonising global strategies. Different from other reviews, this article incorporates insights of various industrial partners currently working towards green ammonia technologies. The work further addresses fundamental complexities of ammonia combustion, crucial for its practical and industrial implementation in various types of equipment

Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment

Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R 2 of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R 2 = 0.83) and with low-cost measurements (R 2 = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p

Electrochemical Sensing with Spatially Patterned Pt Octahedra Electrodes

Locally controlling the position of electrodes in 3D can open new avenues to collect electrochemical signals in complex sensing environments. Implementing such electrodes via an electrical network requires advanced fabrication approaches. This work uses corner lithography and Pt ALD to produce electrochemical 3D electrodes. The approach allows the fabrication of (sub)micrometer size Pt octahedra electrodes spatially supported over 3D fractal-like structures. As a proof of concept, electrochemical sensing of ferrocyanide in biofouling environments, e.g., bovine serum albumin (BSA) and Pseudomonas aeruginosa (P. aeruginosa), is assessed. Differences between before and after BSA addition show a reduction in the active electrode surface area (ΔAeff) ≈49% ± 7% for the flat electrode. In comparison, a ΔAeff reduction of 25% ± 2% for the 3D electrode has been found. The results are accompanied by a 24% ± 16% decrease in peak current for the flat Pt substrate and a 14% ± 5% decrease in peak current for the 3D electrode 24 h after adding BSA. In the case of P. aeruginosa, the 3D electrode retains electrochemical signals, while the flat electrode does not. The results demonstrate that the 3D Pt electrodes are more stable than their flat counterparts under biofouling conditions.</p

Consumer behavioral intention toward sustainable biscuits:An extension of the theory of planned behavior with product familiarity and perceived value

Sustainable food consumption may help mitigate the impact that the food industry exerts on the natural environment. To foster sustainable food consumption, it is essential to understand consumers' perceptions related to sustainable food as well as the determinants of the intention to purchase sustainable food. Through an extension of the theory of planned behavior (TPB) with product familiarity (direct and indirect experience) and perceived value (perceived quality and green perceived utility), this study examines the drivers of purchase intention of sustainable biscuits. A survey of 2396 Italian consumers was conducted and structural equation modeling was used to test the developed model. Results show that perceived quality and environmental concern have positive and significant effects on purchase intention, regardless of the specific characteristics of sustainable biscuits, whereas mixed results are obtained about the effect of direct experience and perceived consumer effectiveness. Indirect experience, green perceived utility, perceived behavioral control, and subjective norms do not display any significant effect.</p