@article{OliveiraMolinnusBegingetal.2021, author = {Oliveira, Danilo A. and Molinnus, Denise and Beging, Stefan and Siqueira Jr, Jos{\´e} R. and Sch{\"o}ning, Michael Josef}, title = {Biosensor Based on Self-Assembled Films of Graphene Oxide and Polyaniline Using a Field-Effect Device Platform}, series = {physica status solidi (a) applications and materials science}, volume = {218}, journal = {physica status solidi (a) applications and materials science}, number = {13}, publisher = {Wiley-VCH}, address = {Weinheim}, issn = {1862-6319}, doi = {10.1002/pssa.202000747}, pages = {1 -- 9}, year = {2021}, abstract = {A new functionalization method to modify capacitive electrolyte-insulator-semiconductor (EIS) structures with nanofilms is presented. Layers of polyallylamine hydrochloride (PAH) and graphene oxide (GO) with the compound polyaniline:poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PANI:PAAMPSA) are deposited onto a p-Si/SiO2 chip using the layer-by-layer technique (LbL). Two different enzymes (urease and penicillinase) are separately immobilized on top of a five-bilayer stack of the PAH:GO/PANI:PAAMPSA-modified EIS chip, forming a biosensor for detection of urea and penicillin, respectively. Electrochemical characterization is performed by constant capacitance (ConCap) measurements, and the film morphology is characterized by atomic force microscopy (AFM) and scanning electron microscopy (SEM). An increase in the average sensitivity of the modified biosensors (EIS-nanofilm-enzyme) of around 15\% is found in relation to sensors, only carrying the enzyme but without the nanofilm (EIS-enzyme). In this sense, the nanofilm acts as a stable bioreceptor onto the EIS chip improving the output signal in terms of sensitivity and stability.}, language = {en} } @article{GriegerSchwabedalWendeletal.2021, author = {Grieger, Niklas and Schwabedal, Justus T. C. and Wendel, Stefanie and Ritze, Yvonne and Bialonski, Stephan}, title = {Automated scoring of pre-REM sleep in mice with deep learning}, series = {Scientific Reports}, volume = {11}, journal = {Scientific Reports}, number = {Art. 12245}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-021-91286-0}, year = {2021}, abstract = {Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.}, language = {en} } @article{HunkerGossmannRamanetal.2021, author = {Hunker, Jan L. and Gossmann, Matthias and Raman, Aravind Hariharan and Linder, Peter}, title = {Artificial neural networks in cardiac safety assessment: Classification of chemotherapeutic compound effects on hiPSC-derived cardiomyocyte contractility}, series = {Journal of Pharmacological and Toxicological Methods}, volume = {111}, journal = {Journal of Pharmacological and Toxicological Methods}, number = {Article number 107044}, publisher = {Elsevier}, address = {New York}, issn = {1056-8719}, doi = {10.1016/j.vascn.2021.107044}, year = {2021}, language = {en} }