TY  - JOUR
A1  - Oliveira, Danilo A.
A1  - Molinnus, Denise
A1  - Beging, Stefan
A1  - Siqueira Jr, José R.
A1  - Schöning, Michael Josef
T1  - Biosensor Based on Self-Assembled Films of Graphene Oxide and Polyaniline Using a Field-Effect Device Platform
JF  - physica status solidi (a) applications and materials science
N2  - 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.
KW  - capacitive electrolyte–insulator–semiconductor sensors
KW  - graphene oxide
KW  - layer-by-layer technique
KW  - nanomaterials
KW  - polyaniline
Y1  - 2021
U6  - https://doi.org/10.1002/pssa.202000747
SN  - 1862-6319
N1  - Corresponding author: José R. Siqueira Jr & Michael J. Schöning
VL  - 218
IS  - 13
SP  - 1
EP  - 9
PB  - Wiley-VCH
CY  - Weinheim
ER  - 
TY  - JOUR
A1  - Grieger, Niklas
A1  - Schwabedal, Justus T. C.
A1  - Wendel, Stefanie
A1  - Ritze, Yvonne
A1  - Bialonski, Stephan
T1  - Automated scoring of pre-REM sleep in mice with deep learning
JF  - Scientific Reports
N2  - 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.
Y1  - 2021
U6  - https://doi.org/10.1038/s41598-021-91286-0
SN  - 2045-2322
N1  - Corresponding author: Stephan Bialonski
VL  - 11
IS  - Art. 12245
PB  - Springer Nature
CY  - London
ER  - 
TY  - JOUR
A1  - Hunker, Jan L.
A1  - Gossmann, Matthias
A1  - Raman, Aravind Hariharan
A1  - Linder, Peter
T1  - Artificial neural networks in cardiac safety assessment: Classification of chemotherapeutic compound effects on hiPSC-derived cardiomyocyte contractility
JF  - Journal of Pharmacological and Toxicological Methods
Y1  - 2021
U6  - https://doi.org/10.1016/j.vascn.2021.107044
SN  - 1056-8719
VL  - 111
IS  - Article number 107044
PB  - Elsevier
CY  - New York
ER  -