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Organisationseinheit der BAM
This survey will discuss current and emerging isothermal and rapid polymerase chain reaction (PCR) based nucleic acid amplification methods for patient near diagnostics. To assess the clinical need of rapid diagnostics for infectious diseases based on nucleic acid tests (NATs) we performed and analysed a questionnaire among laboratories participating in corresponding INSTAND ring trials for external quality assurance. The questions concerning new amplification technologies like isothermal nucleic acid amplification, potentially suited to significantly decrease turnaround times, were complemented by questions to evaluate the present status of NATs. Besides end-users, companies were also addressed by sending out a manufacturer specific questionnaire. Analysis of the answers from 48 laboratories in 14 European countries revealed that a much shorter turnaround time is requested for selected pathogens compared to about 2 h or longer when applying temperature cycling amplification, i.e. PCR. In this context, most frequently mentioned were MRSA, norovirus, influenza A and B viruses, cytomegalovirus (CMV) as well as hepatitis B virus (HBV) and hepatitis C virus (HCV). At present, 8% of the laboratories having participated in this survey apply isothermal amplification of nucleic acid to identify infectious pathogens.
Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis.