TY - JOUR A1 - Flachs, Dennis A1 - Köhler, Tim A1 - Thielemann, Christiane T1 - Transparent poly(3,4-ethylenedioxythiophene)-based microelectrodes for extracellular recording JF - Biointerphases N2 - It is well known that at the interface between neuronal tissue and recording electrode low electrical impedance is required. However, if simultaneous optical detection or stimulation is an issue, good optical transmittance of the electrode material is desirable as well. State-of-the-art titanium nitride electrodes provide superior low impedance compared to gold or iridium, but are nontransparent. Transparent electrode materials like the transparent conducting oxide, indium tin oxide (ITO), or graphene offer high light transmittance (>80%) but reveal relatively high impedance. In this paper, the authors propose the conducting polymer poly(3,4-ethylenedioxythiophene) with the counter ion NO−3 as the electrode material for low impedance and good optical transmittance properties. The polymer is electrochemically deposited onto ITO improving the relatively high impedance of ITO. This multilayer electrode allows not only for electrophysiological recordings of cardiomyocytes but also for monitoring of cell contraction under the microscope. Electrochemical impedance spectroscopy and action potential recordings reveal that the new transparent electrodes are a good compromise in terms of low impedance and transparency if deposition parameters are optimized. KW - Impedanzspektroskopie KW - Mikroelektrode Y1 - 2018 U6 - https://doi.org/10.1116/1.5041957 VL - 2018 IS - 13:041008 SP - 1 EP - 1 ER - TY - JOUR A1 - Hölter, Jan A1 - Rickmeyer, Tim A1 - Thielemann, Christiane T1 - CellRex: Software platform for managing biological cell data JF - SoftwareX N2 - This work introduces the software platform CellRex, a research data management system for laboratories capable of storing, searching, and enriching data with biological metadata. CellRex addresses data management challenges by storing data in an ontology-based directory structure within the filesystem, with metadata saved as JSON files and in a document-oriented SQLite database. The framework, deployed as container services in a software-as-a-service model, features a web-based GUI and API for user interaction and machine-readable access, providing functionalities such as duplicate detection, experiment grouping, and templating. CellRex improves research efficiency and facilitates data reuse, providing a targeted solution for laboratories focused on cell analysis research. KW - Forschungsdaten KW - Zytologie Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1016/j.softx.2025.102284 SN - 2352-7110 VL - 31 PB - Elsevier BV ER - TY - JOUR A1 - Altmann, Alexander Anton A1 - Suppelt, Sven A1 - Wüst, Philipp A1 - Dörsam, Jan Helge A1 - Latsch, Bastian A1 - Flachs, Dennis A1 - Blaeser, Andreas A1 - Zhang, Xiaoqing A1 - Thielemann, Christiane A1 - Seggern, Heinz von A1 - Kupnik, Mario T1 - Self-supporting mid-air 3D printing of single-layer polypropylene structures: Flow rate-dependent analytical modeling and surface characterization JF - Additive Manufacturing N2 - High-precision deposition in material extrusion-based additive manufacturing (MEX-AM), especially in mid-air extrusion, is essential for creating microfluidic channels, integrated sensors, and tissue scaffolds with fine vascular features. However, despite advances in support-free slicing and multi-axis printing, no standardized method exists for fabricating enclosed air cavities with high shape fidelity, particularly in single-layer structures. Reliable deposition at sub-millimeter scales remains challenging, especially when printing low-stiffness polymers such as polypropylene (PP), due to delayed solidification and viscoelastic effects. This study introduces a flow-rate-dependent analytical model to predict extrusion behavior, interfacial bonding, and surface morphology in self-supporting single-layer PP structures. To validate the model, surface roughness, material distribution, and intralayer bonding were quantified using spectral analysis, cross-sectional thickness measurements, and Abbott–Firestone curve evaluation. Results show that reducing the flow rate (relative to 100% nominal flow through a 0.4 mm nozzle) to 40% improves surface roughness (Ra) to below 20 m on average across all surfaces, with the lowest Ra of 4.52 ± 0.49 m observed at a 20% flow rate on the top surfaces enclosing the cavity. The intralayer bonding between adjacent extruded lines increases up to 70%, improving deposition uniformity. Nozzle-induced smoothing effects enhance surface quality at flow rates of 60% or lower, while higher flow rates result in increased surface waviness and geometric irregularities. These findings enable the fabrication of enclosed microfluidic channels and functional cavities with well-defined surfaces and reduced mechanical stiffness, suitable for applications involving sensing, controlled deformation, or flexible system integration. KW - Fertigungstechnik KW - 3D-Druck Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1016/j.addma.2025.105010 SN - 2214-8604 VL - 113 PB - Elsevier BV ER - TY - JOUR A1 - Alves, Caroline L. A1 - Toutain, Thaise A1 - Porto, Joel A1 - Aguiar, Patricia A1 - de Sena, Eduardo Pondé A1 - Rodrigues, Francisco A. A1 - Pineda, Aruane M. A1 - Thielemann, Christiane T1 - Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia JF - Journal of Neural Engineering N2 - Objective. Schizophrenia (SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals. Approach. For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature. Main results. When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ. Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain. KW - Maschinelles Lernen KW - Schizophrenie KW - Deep Learning Y1 - 2023 U6 - https://doi.org/10.1088/1741-2552/acf734 VL - 2023 IS - 20/5 SP - 0 EP - 0 ER -