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Background: Hemolysis in mechanical circulatory support systems is currently determined quantitatively. To also locally resolve hemolysis, we are developing a fluorescent hemolysis detection method. This requires a translucent two‐phase blood analog fluid combined with particle image velocimetry, an optical flow field measurement. The blood analog fluid is composed of red blood cell surrogates. However, producing surrogates in sufficient volume is a challenge. We therefore present a high‐volume and high‐concentration production for our surrogates: ghost cells, hemoglobin‐depleted erythrocytes. Methods: In the ghost cell production, the hemoglobin is removed by a repeated controlled osmolar lysis. We have varied the solution mixture, centrifugation time, and centrifugation force in order to increase production efficiency. The production is characterized by measurements of output volume, hematocrit, transparency, and rheology of the blood analog fluid. Results: The volume of produced ghost cells was significantly increased, and reproducibility was improved. An average production of 389 mL of ghost cells were achieved per day. Those ghost cells diluted in plasma have a rheology similar to blood while being permeable to light. Conclusion: The volume of ghost cells produced is sufficient for optical measurements as particle image velocimetry in mechanical circulatory support systems. This makes further work on experimental measurements for a locally resolved hemolysis detection possible. [ABSTRACT FROM AUTHOR]

The applicability of magnetic nanoparticles (MNP) highly depends on their physical properties, especially their size. Synthesizing MNP with a specific size is challenging due to the large number of interdepend parameters during the synthesis that control their properties. In general, synthesis control cannot be described by white box approaches (empirical, simulation or physics based). To handle synthesis control, this study presents machine learning based approaches for predicting the size of MNP during their synthesis. A dataset comprising 17 synthesis parameters and the corresponding MNP sizes were analyzed. Eight regression algorithms (ridge, lasso, elastic net, decision trees, random forest, gradient boosting, support vectors and multilayer perceptron) were evaluated. The model performance was assessed via root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and standard deviation of residuals. Support vector regression (SVR) exhibited the lowest RMSE values of 3.44 and a standard deviation for the residuals of 5.13. SVR demonstrated a favorable balance between accuracy and consistency among these methods. Qualitative factors like adaptability to online learning and robustness against outliers were additionally considered. Altogether, SVR emerged as the most suitable approach to predict MNP sizes due to its ability to continuously learn from new data and resilience to noise, making it well-suited for real-time applications with varying data quality. In this way, a feasible optimization framework for automated and self-regulated MNP synthesis was implemented. Key challenges included the limited dataset size, potential violations of modeling assumptions, and sensitivity to hyperparameters. Strategies like data regularization, correlation analysis, and grid search for model hyperparameters were employed to mitigate these issues. [ABSTRACT FROM AUTHOR]

Launching biomedical innovations based on clinical demands instead of translating basic research findings to practice reduces the risk that the results will not fit the clinical routine. To realize this type of innovation, a meta-analysis of the body of research is necessary to reveal demand-matching concepts. However, both the data deluge and the narrow time constraints for innovation make it impossible to perform such reviews manually. Thus, this paper proposes a specifically adapted "Potential Technologies Review" approach focusing on automated text mining and information retrieval techniques. The novel framework combines features from both systematic and scoping reviews. It aims at high coverage and reproducibility while mapping technologies--even with a fuzzy initial scope. To achieve these goals for search and triage, a set of closely interrelated methods has been developed: (a) automated query optimization, (b) screening prioritization, and (c) recall estimation. To determine appropriate parameters, a variety of published literature corpora were used and compared with an evaluation on a real-world dataset. Our results show that it is feasible to automate the identification of relevant works using this newly introduced framework. It achieved a workload reduction of up to 91% "Work-saved-over Sampling (WSS)" with a 76% overall recall compared with manually screening search results. Reducing the workload is a prerequisite for a rapid Potential Technologies Review when conducting demand-pull innovations. Moreover, it facilitates the updating and closer monitoring of latest findings. Studying the robustness of the framework and expanding it to patent documents are future tasks.

More than 13 million children are born preterm annually. Prematurity-related mortality accounts for 0.9 million deaths worldwide. The majority of those affected are Extremely Preterm Infants (gestational age less than 28 weeks). Immaturity causes organ failure and specific morbidities like germinal matrix hemorrhage, bronchopulmonary dysplasia, and necrotizing enterocolitis. Artificial womb and placenta technologies address these issues. As a bridge-to-life technology, they provide a liquid environment to allow organ maturation under more physiological conditions. The proposed artificial womb can adapt to fetal growth. Volume adjustment is achieved by removing fluid from the interspace between an inner and outer chamber. Results of the in vitro tests showed a temperature constancy of 36.8℃ ± 0.3℃ without pressure loss over 7 days. The volume of the inner sac was variable between 3.6 and 7.0 L. We designed a filtration and disinfection system for this particular purpose. This system has proven strong disinfection capabilities, effective filtering of metabolic waste, and the ability to avoid phospholipid washout. The presented artificial womb has sufficient volume variability to adapt to the physiologic growth of an extremely preterm neonate over a 4-week period. We regard this as an important step in the development of this bridge-to-life technology. [ABSTRACT FROM AUTHOR]

Magnetic fluid hyperthermia (MFH) is a promising approach for organ-confined tumor treatment. In MFH, magnetic nanoparticles (MNP) are magnetically targeted at the tumor site and heated in an alternating magnetic field. The heat produced by the MNP is used to cause tumor cell death. At the tumor site, MNP bind to the cell membrane and form agglomerates before they are internalized into the intracellular compartments. Intracellular immobilization and the formation of agglomerates influence heating properties of MNP making it difficult to control the local heating inside the tumor. In this study, we investigated MNP agglomerated samples for their heating efficiency. We found an increase in heating of 22 % upon agglomeration. If MNP are additionally immobilized, however, the heating decreases by 30 %. Consequently, due to the binding of bigger MNP agglomerates at cellular level, heating efficiency inside tumors is assumed to decrease.

The pathway from the flash of a technological invention until its use as a medical device in every day care is tedious and burdensome. But the often postulated acceleration has to balance the speed of innovation and the indispensable product safety by an improved understanding of the innovation cycle. While several studies investigated the time course of pharmaceutical innovation, a comparable empirical analysis of medical devices is lacking. Thus we evaluated the time between the patent priority date and the corresponding receipt of the CE mark as a function of a medical device risk class in 61 cases. The statistical analysis yielded a time increment (trend) from medical devices in risk category I (median = 5.8 years) compared to risk category III (median = 10.4 years), which is close to literature reported values for drug development (9–12 years). The difference between products in risk classes I and II did not reach significance. To investigate the underlying facts, a text-mining approach especially to resolve the ambiguity of, e.g. patents, CE Marks etc. is suggested for increasing the sample size.