TY - RPRT A1 - Mayer, Margot A1 - Ritter, Sylvia A1 - Thielemann, Christiane T1 - Electrophysiological Effects of Ionising Radiation on Cortical Rat Neurons in vitro KW - Nervenzelle KW - Ionisierende Strahlung Y1 - 2013 ER - TY - CHAP A1 - Nick, Christoph A1 - Lippert, Paul A1 - Quednau, Sebastian A1 - Schlaak, Helmut A1 - Thielemann, Christiane T1 - Gold Nanopillar Microelectrodes on Low Temperature Curing Polyimide for the Interface with Electrogenic Cells T2 - Middle East Conference on Biomedical Engineering KW - Mikroelektrode Y1 - 2014 SN - 978-1-4799-4799-7 VL - 2014 SP - 55 EP - 58 CY - Doha, Qatar ER - TY - CHAP A1 - Nick, Christoph A1 - Quednau, Sebastian A1 - Sarwar, Reza A1 - Thielemann, Christiane A1 - Schlaak, Helmut T1 - Gold-Nanodrähte auf Mikroelektroden zur extrazellulären Ableitung elektrogener Zellen T2 - Mikrosystemtechnik Kongress KW - Mikroelektrode Y1 - 2013 PB - VDE Verlag CY - Aachen, Deutschland ER - TY - CHAP A1 - Emmerich, Florian A1 - Thielemann, Christiane T1 - Direct Visualisation of Stored Charges in PECVD ONO-Layers by Kelvin-Probe-Force-Microscopy T2 - 15th International Symposium on Electrets ISE KW - Kelvin-Sonde KW - Rasterkraftmikroskop Y1 - 2014 VL - 2014 CY - Baltimore, USA ER - TY - RPRT A1 - Frieß, Johannes A1 - Heselich, Anja A1 - Ritter, Sylvia A1 - Daus, Andreas A1 - Layer, Paul A1 - Thielemann, Christiane T1 - Electrophysiological Effects of Heavy Ion Irradiation on Cardiomyocytes KW - Herzmuskelzelle KW - Elektrophysiologie Y1 - 2012 ER - TY - CHAP A1 - Stern, Maike A1 - Nick, Christoph A1 - Thielemann, Christiane T1 - A Novel LabVIEW based Multi-Channel Closed-Loop Stimulator T2 - 9th International Meeting on Substrate-Integrated Micro-electrode Arrays KW - LabVIEW KW - Simulation Y1 - 2014 VL - 2014 CY - Reutlingen, Germany ER - TY - CHAP A1 - Nick, Christoph A1 - Schlaak, Helmut A1 - Thielemann, Christiane T1 - PEDOT:PSS coated Gold Nanopillar Micro-electrodes for Neural Interfaces T2 - 4th International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale KW - Mikroelektrode Y1 - 2014 VL - 2014 CY - Taipeh, Taiwan ER - TY - CHAP A1 - Frieß, Johannes A1 - Heselich, Anja A1 - Ritter, Sylvia A1 - Thielemann, Christiane T1 - Electrophysiological characteristics of cardiomyocytes after heavy ion exposure T2 - Jahrestagung der Gesellschaft für biologische Strahlenforschung KW - Herzmuskelzelle Y1 - 2013 CY - Darmstadt, Deutschland ER - TY - JOUR A1 - Nick, Christoph A1 - Thielemann, Christiane T1 - Are Carbon Nanotube Microelectrodes Manufactured from Dispersion Stable Enough for Neural Interfaces? JF - BioNanoScience KW - Mikroelektrode KW - Neuronales Netz Y1 - 2014 VL - 2014 IS - 4 / 3 SP - 216 EP - 225 ER - TY - CHAP A1 - Körbitzer, Berit Silke A1 - Krauß, Peter A1 - Schneider, Jörg A1 - Thielemann, Christiane T1 - Characterization Of Graphene-Coated Microelectrode Arrays For Recording And Stimulation Of Neuronal Cells T2 - Frontiers KW - Mikroelektrode KW - Array KW - Nervenstimulation Y1 - 2016 U6 - https://doi.org/10.3389/conf.fnins.2016.93.00013 VL - 2016 IS - MEA Meeting 2016 ER - TY - CHAP A1 - Thielemann, Christiane A1 - Ritter, Sylvia A1 - Layer, Paul A1 - Frieß, Johannes A1 - Daus, Andreas A1 - Steger, Frederik A1 - Heselich, Anja T1 - IBER10 - Cellular effects of space radiation with relevance to cardiovascular diseases T2 - European Space Agency - Life in Space for Life on Earth KW - Kosmische Strahlung KW - Kardiovaskuläre Krankheit Y1 - 2012 CY - Aberdeen, Schottland, UK ER - TY - CHAP A1 - Ciba, Manuel A1 - Bahmer, Andreas A1 - Thielemann, Christiane T1 - Application of spike train synchrony measure Spike‑contrast to quantify the effect of bicuculline on cortical networks grown on microelectrode arrays T2 - BMC Neuroscience KW - Mikroelektrode KW - Array Y1 - 2017 U6 - https://doi.org/10.1186/s12868-017-0372-1 VL - 2017 IS - 18 ER - TY - RPRT A1 - Steger, Frederik A1 - Ritter, Sylvia A1 - Daus, Andreas A1 - Frieß, Johannes A1 - Thielemann, Christiane T1 - Analysis of electrophysiological characteristics of cardiomyocytes following radiation exposure KW - Herzmuskelzelle KW - Strahlenbelastung Y1 - 2011 ER - TY - CHAP A1 - Nick, Christoph A1 - Heinlein, Thorsten A1 - Schneider, Jörg A1 - Thielemann, Christiane T1 - 3D Carbon Nanotube Electrodes for Sensing of Neural Activity T2 - Elsevier World Congress on Biosensors KW - Kohlenstoff-Nanoröhre Y1 - 2012 CY - Cancun, Mexiko ER - TY - CHAP A1 - Köhler, Tim A1 - Wölfel, Maximilian A1 - Bochtler, Ulrich A1 - Thielemann, Christiane T1 - TETRA specific long-term exposure of neuronal in vitro networks T2 - BioEM2016 KW - Neuronales Netz Y1 - 2016 ER - TY - JOUR A1 - Thielemann, Christiane A1 - Krstić, Nenad A1 - Jüttner, Jens A1 - Giegerich, Lars A1 - Mayer, Margot A1 - Knuth, Monika A1 - Müller, Achim T1 - 3D printed biosensor for continuous glucose measurement in cell cultures JF - Annals of 3D Printed Medicine N2 - A novel 3D-printed glucose sensor is presented for cell culture application. Glucose sensing was performed using a fluorescence resonance energy transfer (FRET)-based assay principle based on ConA and dextran. Both molecules are encapsulated in alginate microspheres and embedded in the UV-curable, stable hydrogel polyvinyl alcohol (PVA). The rheology of the formulation was adapted to obtain good properties for an extrusion-based printing process. The printed sensor structures were tested for their ability to detect glucose in vitro. A proportional increase in fluorescence intensity was observed in a concentration range of 0 - 2 g/L glucose. Tests with HEK cell cultures also showed good cell compatibility and excellent adhesion properties on plasma-treated Petri dishes. The printed sensors were able to detect the glucose decay associated with the metabolic activities of the fast-growing HEK cells in the cell culture medium over ten days. The proof-of-principle study shows that metabolic processes in cell cultures can be monitored with the new printed sensor using a standard fluorescence wide-field microscope. KW - Bioprinting, FRET, Enzyme-free glucose sensor, PVA hydrogel KW - Biosensor KW - 3D-Druck KW - Zellkultur Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1016/j.stlm.2023.100111 VL - 2023 IS - - SP - - EP - - ER - TY - RPRT A1 - Helm, Alexander A1 - Frank, Simon A1 - Frieß, Johannes A1 - Thielemann, Christiane A1 - Durante, Marco A1 - Ritter, Sylvia T1 - Electrophysiological response of mouse embryonic stem cell-derived cardiomyocytes after X-ray and C-ion exposure T2 - GSI SCIENTIFIC REPORT 2014 KW - Embryonale Stammzelle KW - Herzmuskelzelle Y1 - 2014 U6 - https://doi.org/10.15120/GR-2015-1-APPA-HEALTH-21 ER - TY - RPRT A1 - Frieß, Johannes A1 - Heselich, Anja A1 - Ritter, Sylvia A1 - Layer, Paul A1 - Thielemann, Christiane T1 - Combined effects of ionizing radiation and cardio-active drugs on human iPSC-derived cardiomyocytes T2 - GSI SCIENTIFIC REPORT 2014 KW - Herzmuskelzelle KW - Ionisierende Strahlung Y1 - 2014 U6 - https://doi.org/10.15120/GR-2015-1-APPA-HEALTH-37 ER - TY - RPRT A1 - Mayer, Margot A1 - Arrizabalaga, Onetsine A1 - Ritter, Sylvia A1 - Thielemann, Christiane T1 - Human embryonic stem cell derived neurospheres form functional networks on microelectrode arrays T2 - GSI Scientific Report 2015 KW - Embryonale Stammzelle KW - Mikroelektrode KW - Array Y1 - 2015 U6 - https://doi.org/10.15120/GR-2016-1 VL - 2015 IS - GSI Report 2016-1 SP - 205 EP - 205 ER - TY - JOUR A1 - Ciba, Manuel A1 - Petzold, Marc A1 - Alves, Caroline L. A1 - Rodrigues, Francisco A. A1 - Jimbo, Yasuhiko A1 - Thielemann, Christiane T1 - Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data JF - Scientific Reports N2 - Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. Microelectrode array recordings of neuronal networks exposed to bicuculline, a GABA $$_A$$ receptor antagonist known to induce hypersynchrony, demonstrated the workflow’s ability to detect and characterize pharmacological effects. The workflow integrates network-based features with synchrony, optimizing preprocessing parameters, including spike train bin sizes, segmentation window sizes, and correlation methods. It achieved high classification accuracy (AUC up to 90%) and used Shapley Additive Explanations to interpret feature importance rankings. Significant reductions in network complexity and segregation, hallmarks of epileptiform activity induced by bicuculline, were revealed. While bicuculline’s effects are well established, this framework is designed to be broadly applicable for detecting both strong and subtle network alterations induced by neuroactive compounds. The results demonstrate the potential of this methodology for advancing biosensor applications in neuropharmacology and drug discovery. KW - Maschinelles Lernen KW - Biosensor KW - Mikroelektrode Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1038/s41598-025-99479-7 SN - 2045-2322 VL - 15 IS - 1 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Sallum, Loriz Francisco A1 - Alves, Caroline L. A1 - de O Toutain, Thaise Graziele L A1 - Porto, Joel Augusto Moura A1 - Thielemann, Christiane A1 - Rodrigues, Francisco A. T1 - Revealing patterns in major depressive disorder with machine learning and networks JF - Chaos, Solitons & Fractals N2 - Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks. KW - Depression KW - Maschinelles Lernen KW - Elektroencephalographie Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1016/j.chaos.2025.116163 SN - 0960-0779 VL - 194 PB - Elsevier BV ER - TY - JOUR A1 - Alves, Caroline L. A1 - Ciba, Manuel A1 - de O Toutain, Thaise Graziele L A1 - Moura Porto, Joel Augusto A1 - de Sena, Eduardo Pondé A1 - Thielemann, Christiane A1 - Rodrigues, Francisco A. T1 - On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments JF - Journal of Physics: Complexity N2 - There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine. We found that both approaches could detect changes in the brain's automatic activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers' emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs. KW - Neuronales Netz KW - Halluzinogen KW - Psychische Störung Y1 - 2024 U6 - https://doi.org/10.1088/2632-072X/ad1c68 VL - 2024 IS - 1 SP - 1 EP - 2 ER -