@inproceedings{FlachsBernhardThielemann2024, author = {Flachs, Dennis and Bernhard, Levin and Thielemann, Christiane}, title = {Fully 3D-Printed Rotational Energy Harvester Based On Bipolar Charged PLA Electrets}, series = {Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)}, volume = {2024}, booktitle = {Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)}, publisher = {IEEE}, isbn = {979-8-3503-8020-0}, doi = {979-8-3503-8020-0}, year = {2024}, abstract = {Rotational energy harvesters have emerged as a promising solution for sustainable power generation in a variety of applications, ranging from small-scale devices to large-scale industrial systems. In this work, we present a fully 3D-printed electret rotational harvester based on the biodegradable and compostable polymer polylactic acid (PLA). The 3D-printed harvester consists of a rotor made from a bipolar-charged PLA electret, electrode pairs of conductive PLA that function as the stator, and a PLA bearing. An output power of 61μ W was achieved at a rotational speed of 400 rpm and a load resistance of 28.2MΩ. To assess the durability of the harvester, particularly the 3D-printed bearing, the extent of mechanical wear was examined after one million rotations at a rotational speed of 400 rpm. The findings indicate that the rollers undergo a 1.2\% loss in mass, yet the functionality remains intact. The charge decay of PLA electrets is influenced by external factors such as elevated humidity and temperature. Future research will focus on improving the charge stability of PLA by addressing these factors, expanding the potential applications of this rotational energy harvester.}, subject = {Elektret}, language = {en} } @article{SallumAlvesThielemannetal.2024, author = {Sallum, Loriz Francisco and Alves, Caroline L. and Thielemann, Christiane and Rodrigues, Francisco A.}, title = {Revealing patterns in major depressive disorder with machine learning and networks}, series = {medrxiv}, volume = {2024}, journal = {medrxiv}, number = {1}, doi = {doi.org/10.1101/2024.06.07.24308619}, pages = {1 -- 17}, year = {2024}, abstract = {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.}, subject = {Depression}, language = {en} } @article{NickJoshiSchneideretal.2012, author = {Nick, Christoph and Joshi, Ravi and Schneider, J{\"o}rg and Thielemann, Christiane}, title = {Three-Dimensional Carbon Nanotube Electrodes for Extracellular Recording of Cardiac Myocytes}, series = {Biointerphases, 2012}, volume = {7}, journal = {Biointerphases, 2012}, publisher = {Springer-Verl.}, doi = {10.1007/s13758-012-0058-2}, pages = {58}, year = {2012}, abstract = {Three-Dimensional Carbon Nanotube Electrodes for Extracellular Recording of Cardiac Myocytes}, subject = {Kohlenstoff-Nanor{\"o}hre}, language = {en} } @article{AlvesCuryRosteretal.2022, author = {Alves, Caroline L. and Cury, Rubens G. and Roster, Kirstin and Pineda, Aruane M. and Rodrigues, Francisco A. and Thielemann, Christiane and Ciba, Manuel}, title = {Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments}, series = {PLOS ONE}, volume = {2022}, journal = {PLOS ONE}, number = {12}, doi = {https://doi.org/10.1371/journal. pone.0277257}, pages = {1 -- 26}, year = {2022}, abstract = {Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demon� strated to be a viable therapy for a variety of neurological and mental diseases. EEG experi� ments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92\%), followed by (A) (88\%) and (C) (83\%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most impor� tant brain connections, a significant new discovery for psychedelic literature. This connec� tion may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assorta� tivity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer's disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and sug� gested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action}, subject = {Ayahuasca}, language = {en} } @article{MayerArrizabalagaLiebetal.2018, author = {Mayer, Margot and Arrizabalaga, Onetsine and Lieb, Florian and Ciba, Manuel and Ritter, Sylvia and Thielemann, Christiane}, title = {Electrophysiological investigation of human embryonic stem cell derived neurospheres using a novel spike detection algorithm}, series = {Biosensors and Bioelectronics}, volume = {2018}, journal = {Biosensors and Bioelectronics}, number = {100}, doi = {10.1016/j.bios.2017.09.034}, pages = {462 -- 468}, year = {2018}, abstract = {Microelectrode array (MEA) technology in combination with three-dimensional (3D) neuronal cell models derived from human embryonic stem cells (hESC) provide an excellent tool for neurotoxicity screening. Yet, there are significant challenges in terms of data processing and analysis, since neuronal signals have very small amplitudes and the 3D structure enhances the level of background noise. Thus, neuronal signal analysis requires the application of highly sophisticated algorithms. In this study, we present a new approach optimized for the detection of spikes recorded from 3D neurospheres (NS) with a very low signal-to-noise ratio. This was achieved by extending simple threshold-based spike detection utilizing a highly sensitive algorithm named SWTTEO. This analysis procedure was applied to data obtained from hESC-derived NS grown on MEA chips. Specifically, we examined changes in the activity pattern occurring within the first ten days of electrical activity. We further analyzed the response of NS to the GABA receptor antagonist bicuculline. With this new algorithm method we obtained more reliable results compared to the simple threshold-based spike detection.}, subject = {Embryonale Stammzelle}, language = {en} } @article{KoerbitzerKraussBelleetal.2019, author = {K{\"o}rbitzer, Berit Silke and Krauß, Peter and Belle, Stefan and Schneider, J{\"o}rg and Thielemann, Christiane}, title = {Electrochemical Characterization of Graphene Microelectrodes for Biological Applications}, series = {ChemNanoMat}, volume = {2019}, journal = {ChemNanoMat}, number = {5:4}, doi = {10.1002/cnma.201800652}, pages = {427 -- 435}, year = {2019}, abstract = {Graphene is a promising material both as a coating for existing neural electrodes as well as for transparent electrodes made exclusively from graphene. We studied graphene-based microelectrodes by investigating their recording and stimulation properties in order to evaluate their suitability for neuronal implants. In this work, we compare three different electrode material compositions. Microelectrode arrays (MEA) with an electrode size of about 700 μm2 were prepared of gold, graphene on gold, and plain graphene on glass substrate. In order to reduce polymer contamination during graphene transfer, we employed a polymer-free transfer and lift-off process. Impedance studies revealed a value of 2.3 MΩ at 1 kHz for plain, and 0.88 MΩ for graphene on gold. Neuronal recording experiments showed a sufficient SNR for both graphene-based materials and a stable impedance, unaffected by surface degradation metal electrodes are known for. Stimulation measurements yielded a charge injection capacity of 0.15 mC/cm2 using biphasic pulses of 1 ms and 1 μA transparent graphene electrodes. Cyclic voltammetry revealed a large voltage range of -1.4 V to +1.6 V before water electrolysis occurs. Graphene-coated gold microelectrodes show enhanced recording properties, whereas plain graphene electrodes might be better suited for stimulation applications.}, subject = {Mikroelektrode}, language = {en} } @article{MoniciThielemann2023, author = {Monici, Monica and Thielemann, Christiane}, title = {How do gravity alterations affect animal and human systems at a cellular/tissue level?}, series = {nature npj Microgravity}, volume = {2023}, journal = {nature npj Microgravity}, number = {9/84}, doi = {https://doi.org/10.1038/s41526-023-00330-y}, pages = {1 -- 9}, year = {2023}, abstract = {The present white paper concerns the indications and recommendations of the SciSpacE Science Community to make progress in filling the gaps of knowledge that prevent us from answering the question: "How Do Gravity Alterations Affect Animal and Human Systems at a Cellular/Tissue Level?" This is one of the five major scientific issues of the ESA roadmap "Biology in Space and Analogue Environments". Despite the many studies conducted so far on spaceflight adaptation mechanisms and related pathophysiological alterations observed in astronauts, we are not yet able to elaborate a synthetic integrated model of the many changes occurring at different system and functional levels. Consequently, it is difficult to develop credible models for predicting long-term consequences of human adaptation to the space environment, as well as to implement medical support plans for long-term missions and a strategy for preventing the possible health risks due to prolonged exposure to spaceflight beyond the low Earth orbit (LEO). The research activities suggested by the scientific community have the aim to overcome these problems by striving to connect biological and physiological aspects in a more holistic view of space adaptation effects.}, subject = {Raumfahrt}, language = {en} } @article{KoehlerWoelfelCibaetal.2018, author = {K{\"o}hler, Tim and W{\"o}lfel, Maximilian and Ciba, Manuel and Bochtler, Ulrich and Thielemann, Christiane}, title = {Terrestrial Trunked Radio (TETRA) exposure of neuronal in vitro networks}, series = {Environmental Research}, volume = {2018}, journal = {Environmental Research}, number = {162}, doi = {10.1016/j.envres.2017.12.007}, pages = {1 -- 7}, year = {2018}, abstract = {Terrestrial Trunked Radio (TETRA) is a worldwide common mobile communication standard, used by authorities and organizations with security tasks. Previous studies reported on health effects of TETRA, with focus on the specific pulse frequency of 17.64 Hz, which affects calcium efflux in neuronal cells. Likewise among others, it was reported that TETRA affects heart rate variability, neurophysiology and leads to headaches. In contrast, other studies conclude that TETRA does not affect calcium efflux of cells and has no effect on people's health. In the present study we examine whether TETRA short- and long-term exposure could affect the electrophysiology of neuronal in vitro networks. Experiments were performed with a carrier frequency of 395 MHz, a pulse frequency of 17.64 Hz and a differential quaternary phase-shift keying (π/4 DQPSK) modulation. Specific absorption rates (SAR) of 1.17 W/kg and 2.21 W/kg were applied. In conclusion, the present results do not indicate any effect of TETRA exposure on electrophysiology of neuronal in vitro networks, neither for short-term nor long-term exposure. This applies to the examined parameters spike rate, burst rate, burst duration and network synchrony.}, subject = {Neuronales Netz}, language = {en} } @article{EmmerichThielemann2016, author = {Emmerich, Florian and Thielemann, Christiane}, title = {Real-space measurement of potential distribution in PECVD ONO electrets by Kelvin probe force microscopy}, series = {Nanotechnology}, volume = {2016}, journal = {Nanotechnology}, number = {27}, pages = {1 -- 10}, year = {2016}, abstract = {Multilayers of silicon oxide/silicon nitride/silicon oxide (ONO) are known for their good electret properties due to deep energy traps near the material interfaces, facilitating charge storage. However, measurement of the space charge distribution in such multilayers is a challenge for conventional methods if layer thickness dimensions shrink below 1 μm. In this paper, we propose an atomic force microscope based method to determine charge distributions in ONO layers with spatial resolution below 100 nm. By applying Kelvin probe force microscopy (KPFM) on freshly cleaved, corona-charged multilayers, the surface potential is measured directly along the z-axis and across the interfaces. This new method gives insights into charge distribution and charge movement in inorganic electrets with a high spatial resolution.}, subject = {Kelvin-Sonde}, language = {en} } @article{AlligMayerThielemann2018, author = {Allig, Sebastian and Mayer, Margot and Thielemann, Christiane}, title = {Workflow for bioprinting of cell-laden bioink}, series = {Lekar a technika - Clinician and Technology}, volume = {48}, journal = {Lekar a technika - Clinician and Technology}, number = {2}, pages = {46 -- 51}, year = {2018}, abstract = {Applying technologies of additive manufacturing to the field of tissue engineering created a pioneering new approach to model complex cell systems artificially. Regarding its huge potential, bioprinting is still in its infancies and many questions are still unanswered. To address this issue, an extrusion-based bioprinting (EBB) process was used to deposit human embryonic kidney (HEK) cells in a defined pattern. It was shown that the bioprinted construct featured a high degree in viability reaching up to 77\% 10 days after printing (DAP). This work displays a proof of principle for a controlled cell formation which shall later be applied to in vitro drug screening tests using various types of cells.}, subject = {Biomaterial}, language = {en} }