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3d printing is capable of providing dose individualization for pediatric medicines and translating the precision medicine approach into practical application. In pediatrics, dose individualization and preparation of small dosage forms is a requirement for successful therapy, which is frequently not possible due to the lack of suitable dosage forms. For precision medicine, individual characteristics of patients are considered for the selection of the best possible API in the most suitable dose with the most effective release profile to improve therapeutic outcome. 3d printing is inherently suitable for manufacturing of individualized medicines with varying dosages, sizes, release profiles and drug combinations in small batch sizes, which cannot be manufactured with traditional technologies. However, understanding of critical quality attributes and process parameters still needs to be significantly improved for this new technology. To ensure health and safety of patients, cleaning and process validation needs to be established. Additionally, adequate analytical methods for the in-process control of intermediates, regarding their printability as well as control of the final 3d printed tablets considering any risk of this new technology will be required. The PolyPrint consortium is actively working on developing novel polymers for fused deposition modeling (FDM) 3d printing, filament formulation and manufacturing development as well as optimization of the printing process, and the design of a GMP-capable FDM 3d printer. In this manuscript, the consortium shares its views on quality aspects and measures for 3d printing from drug-loaded filaments, including formulation development, the printing process, and the printed dosage forms. Additionally, engineering approaches for quality assurance during the printing process and for the final dosage form will be presented together with considerations for a GMP-capable printer design.
Remaining-useful-life (RUL) prediction of Li-ion batteries is used to provide an early indication of the expected lifetime of the battery, thereby reducing the risk of failure and increasing safety. In this paper, a detailed method is presented to make long-term predictions for the RUL based on a combination of gated recurrent unit neural network (GRU NN) and soft-sensing method. Firstly, an indirect health indicator (HI) was extracted from the charging processes using a soft-sensing method that can accurately describe power degradation instead of capacity. Then, a GRU NN with a sliding window was applied to learn the long-term performance development. The method also uses a dropout and early stopping method to prevent overfitting. To build the models and validate the effectiveness of the proposed method, a real-world NASA battery data set with various battery measurements was used. The results show that the method can produce a long-term and accurate RUL prediction at each position of the degradation progression based on several historical battery data sets.
Pressure injuries remain a serious health complication for patients and nursing staff. Evidence from the past decade has not been analysed through narrative synthesis yet. PubMed, Embase, CINAHL Complete, Web of Science, Cochrane Library, and other reviews/sources were screened. Risk of bias was evaluated using a slightly modified QUIPS tool. Risk factor domains were used to assign (non)statistically independent risk factors. Hence, 67 studies with 679,660 patients were included. In low to moderate risk of bias studies, non-blanchable erythema reliably predicted pressure injury stage 2. Factors influencing mechanical boundary conditions, e.g., higher interface pressure or BMI < 18.5, as well as factors affecting interindividual susceptibility (male sex, older age, anemia, hypoalbuminemia, diabetes, hypotension, low physical activity, existing pressure injuries) and treatment-related aspects, such as length of stay in intensive care units, were identified as possible risk factors for pressure injury development. Health care professionals’ evidence-based knowledge of above-mentioned risk factors is vital to ensure optimal prevention and/or treatment. Openly accessible risk factors, e.g., sex, age, BMI, pre-existing diabetes, and non-blanchable erythema, can serve as yellow flags for pressure injury development. Close communication concerning further risk factors, e.g., anemia, hypoalbuminemia, or low physical activity, may optimize prevention and/or treatment. Further high-quality evidence is warranted.