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Dental stem cells have been isolated from the medical waste of various dental tissues. They have been characterized by numerous markers, which are evaluated herein and differentiated into multiple cell types. They can also be used to generate cell lines and iPSCs for long-term in vitro research. Methods for utilizing these stem cells including cellular systems such as organoids or cell sheets, cell-free systems such as exosomes, and scaffold-based approaches with and without drug release concepts are reported in this review and presented with new pictures for clarification. These in vitro applications can be deployed in disease modeling and subsequent pharmaceutical research and also pave the way for tissue regeneration. The main focus herein is on the potential of dental stem cells for hard tissue regeneration, especially bone, by evaluating their potential for osteogenesis and angiogenesis, and the regulation of these two processes by growth factors and environmental stimulators. Current in vitro and in vivo publications show numerous benefits of using dental stem cells for research purposes and hard tissue regeneration. However, only a few clinical trials currently exist. The goal of this review is to pinpoint this imbalance and encourage scientists to pick up this research and proceed one step further to translation.
New sustainable, environmentally friendly materials for thermal insulation of buildings are necessary to reduce their carbon footprints. In this study, Miscanthus fiber-reinforced geopolymer composites, foamed with sodium dodecyl sulfate (SDS), were developed using fly ash as a geopolymer precursor. The effects of fiber content, fiber size, curing temperature, foaming agent content, fumed silica specific surface area and fumed silica content on thermal conductivity and compressive strength were evaluated using a Plackett-Burman design of experiment. Furthermore, the microstructure of geopolymer composites was investigated using X-ray diffraction (XRD), X-ray micro-computed tomography (μCT) and scanning electron microscopy (SEM). The measured characteristic values were in the following ranges: Thermal conductivity 0.057 W (m K)−1 to 0.127 W (m K)−1, compressive strength 0.007 MPa–0.719 MPa and porosity 49 vol% to 76 vol%. The results reveal an enhancement of thermal conductivity by elevated fiber size and foaming agent content. In contrast, the compressive strength is enhanced by high fiber content. Additionally, SEM images indicate a good interaction between the fibers and the geopolymer matrix, because nearly the whole fiber surface is covered by the geopolymer.
The analysis of used engine oils from industrial engines enables the study of engine wear and oil degradation in order to evaluate the necessity of oil changes. As the matrix composition of an engine oil strongly depends on its intended application, meaningful diagnostic oil analyses bear considerable challenges. Owing to the broad spectrum of available oil matrices, we have evaluated the applicability of using an internal standard and/or preceding sample digestion for elemental analysis of used engine oils via inductively coupled plasma optical emission spectroscopy (ICP OES). Elements originating from both wear particles and additives as well as particle size influence could be clearly recognized by their distinct digestion behaviour. While a precise determination of most wear elements can be achieved in oily matrix, the measurement of additives is performed preferably after sample digestion. Considering a dataset of physicochemical parameters and elemental composition for several hundred used engine oils, we have further investigated the feasibility of predicting the identity and overall condition of an unknown combustion engine using the machine learning system XGBoost. A maximum accuracy of 89.6% in predicting the engine type was achieved, a mean error of less than 10% of the observed timeframe in predicting the oil running time and even less than 4% for the total engine running time, based purely on common oil check data. Furthermore, obstacles and possibilities to improve the performance of the machine learning models were analysed and the factors that enabled the prediction were explored with SHapley Additive exPlanation (SHAP). Our results demonstrate that both the identification of an unknown engine as well as a lifetime assessment can be performed for a first estimation of the actual sample without requiring meticulous documentation.
Das Projekt adressiert ein Problem aus dem Bereich Medizintechnologie (ein NRW-Förderschwerpunkt): die Entwicklung eines für Patienten maßgeschneiderten Gewebeersatzmaterials, ein Knochensurrogat. Kritische (“critical size“) Knochendefekte stellen ein signifikantes Gesundheitsproblem dar, das durch die zurzeit gängigen Knochenersatzmaterialien nicht bzw. nicht effizient therapiert werden kann. Kritische Knochendefekte werden mit artifiziellen Biomaterialien behandelt, die bislang eine unzureichende Regenerationskapazität aufweisen.