TY - CHAP A1 - Ritter, Sylvia A1 - Durante, Marco A1 - Helm, Alexander A1 - Steger, Frederik A1 - Daus, Andreas A1 - Frieß, Johannes T1 - Assessment of cardiotoxicity of heavy ions in vitro: generation and electrophysiology of cardiomyocytes T2 - 39th COSPAR Scientific Assembly, Mysore, India KW - Herzmuskelzelle KW - Kardiotoxizität Y1 - 2014 ER - TY - JOUR A1 - Daus, Andreas T1 - Leben auf dem Mikrochip – Biosensoren auf der Basis von Mikroelektroden-Arrays JF - Labor & More KW - Biosensoren KW - Biosensor KW - Mikroelektrode KW - Array Y1 - 2012 VL - 2012 IS - 8 SP - 20 EP - 24 ER - TY - GEN A1 - Kück, Nico-Wei T1 - In Vitro Platform for Acoustic and Electrophysiological Investigations of Ultrasound Neuromodulation N2 - The quality of life has increased dramatically over the past decades. Approximately one billion individuals worldwide will be aged 65 years or older by 2030. With the increasing age of human population, prevalence of neurodegenerative ailments, such as Parkinson’s disease or Alzheimer’s disease, increases. Brain pacemakers are proposed to treat their symptoms. They provide the electrical stimulation of neuronal regions of the central or peripheral nervous system. But for the implantation of brain pacemakers, surgery is needed. Those surgical interventions can be avoided by using transcranial stimulation methods such as Transcranial Magnetic Stimulation (TMS). However, magnetic fields lack the possibility of focusing. Ultrasound (US) offers transcranial characteristics as well and allows focusing on a specific target area in the brain. Over 60 years ago, the Hodgkin-Huxley (H-H) model was presented to simulate the electrophysiological activity of neurons but it lacks to describe the impact of mechanical soundwaves on action potentials in a neuron. N2 - Poster KW - In Vitro, Ultrasound, Neuromodulation, Neuron KW - Nervenstimulation KW - Ultraschall Y1 - 2017 ER - TY - THES A1 - Daus, Andreas T1 - Zellbasierte Biosensoren – Hybride Systeme aus dreidimensionalen in vitro Netzwerken und Mikroelektroden Arrays N2 - Mit der Entwicklung hybrider Strukturen aus Mikroelektroden Arrays und dreidimensionalen Sphäroiden wurde in dieser Arbeit ein funktionaler, deskriptiver Ansatz der Biosensorik adressiert. Während biochemische Sensoren ausschließlich analytische Informationen über die Wechselwirkung von Liganden und Rezeptoren liefern, erlauben zellbasierte Biosensoren eine physiologische Beschreibung biologischer Systeme in nativen bzw. chemisch oder physikalisch provozierten Zuständen. Dies impliziert für Anwendungen der Pharmakologie, Toxikologie und Grundlagenforschung hohes Potenzial, evoziert jedoch eine multidisziplinäre Aufgabe. Es werden zum einen in vitro Systeme benötigt, die den Ansprüchen eines physiologisch repräsentativen Abbilds eines Organismus genügen. Zum anderen sind technische Systeme erforderlich, die Zellreaktionen qualitativ und quantitativ verwertbar machen. KW - Array KW - Biosensor Y1 - 2013 ER - TY - THES A1 - Frieß, Johannes T1 - Einfluss ionisierender Strahlung auf die elektrophysiologischen Eigenschaften kardialer Zellen N2 - Die zentrale Fragestellung der Strahlenbiologie behandelt die Wirkung von ionisierender Strahlung auf biologische Systeme. Zwar sind die bei Exposition mit ionisierender Strahlung entstehenden Gesundheitsrisiken bekannt, dennoch sind die Strahlungseffekte auf das Herz immer noch nicht sehr gut verstanden. Dabei ist die Exposition mit hohen Dosen ionisierender Strahlung ein anerkannter Risikofaktor in Bezug auf kardiovaskuläre Erkrankungen. KW - Elektrophysiologie KW - Herzmuskelzelle Y1 - 2016 ER - TY - JOUR A1 - Sallum, Loriz Francisco A1 - Alves, Caroline L. A1 - Thielemann, Christiane A1 - Rodrigues, Francisco A. T1 - Revealing patterns in major depressive disorder with machine learning and networks JF - medrxiv 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 - Elektroencephalographie KW - Maschinelles Lernen Y1 - 2024 U6 - https://doi.org/doi.org/10.1101/2024.06.07.24308619 VL - 2024 IS - 1 SP - 1 EP - 17 ER - TY - JOUR A1 - Alves, Caroline L. A1 - Cury, Rubens G. A1 - Roster, Kirstin A1 - Pineda, Aruane M. A1 - Rodrigues, Francisco A. A1 - Thielemann, Christiane A1 - Ciba, Manuel T1 - Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments JF - PLOS ONE N2 - 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 KW - Ayahuasca KW - Elektroencephalographie KW - Alzheimerkrankheit KW - Gehirn Y1 - 2022 U6 - https://doi.org/https://doi.org/10.1371/journal. pone.0277257 VL - 2022 IS - 12 SP - 1 EP - 26 ER - TY - JOUR A1 - Monici, Monica A1 - Thielemann, Christiane T1 - How do gravity alterations affect animal and human systems at a cellular/tissue level? JF - nature npj Microgravity N2 - 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. KW - Raumfahrt KW - Schwere KW - Raumfahrtbiologie Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1038/s41526-023-00330-y N1 - Artikel hat 62 Autoren VL - 2023 IS - 9/84 SP - 1 EP - 9 ER - TY - THES A1 - Nick, Christoph T1 - Mikrointegrierte Nanostrukturen mit hohem Aspektverhältnis als neuronale Schnittstelle KW - Neuronales Netz KW - Mikroelektrode Y1 - 2015 UR - http://tuprints.ulb.tu-darmstadt.de/4595/7/Dissertation%20-%20Christoph%20Nick.pdf ER - TY - GEN A1 - Uhrig, Janis A1 - Emmerich, Florian T1 - Study on different template-based production processes for magnetic nanowires T2 - 22nd International Student Conference on Electrical Engineering POSTER 2018 N2 - Nickel-iron (NiFe) nanowires with a high contentof nickel and large aspect-ratio show promising characteris-tics for applications in modern micro-systems. In this study,nanowires with well-controlled diameter, height and compo-sition are fabricated using nanoporous poly-carbonate (PC)and anodized aluminum-oxide (AAO) membranes. Differ-ences between potentiostatic and pulsed deposition methodswere studied with regard to their flexibility and a recommen-dation for the best use of the individual types is given. Withthe application of the presented processes, future technolo-gies as magnetic energy-harvesting or nano-fluxgate sensorscan be improved. N2 - Poster KW - magnetic nanowires KW - MEMS KW - template-based KW - electroplating KW - Nanodraht Y1 - 2018 VL - 2018 IS - POSTER 2018 SP - 1 EP - 5 ER - TY - GEN A1 - Emmerich, Florian A1 - Ciba, Stefan T1 - Thin-film Micro-Heater for Fusion Bonding of Teflon-FEP Foils T2 - 21th International Student Conference on Electrical Engineering POSTER 2017 N2 - MEMS-based Micro-Heaters, in combination with thin-film temperature sensors, are often used for providing the necessary amount of thermal energy for sensor-applications. In this work, we propose an integrated micro-heater as actuator for fusion bonding of polymers, which can optimize the production process of electret-based micro-energy-harvesters. By adjusting the design parameters of thermoforming-molds, we can implement thin-film micro-heaters that are capable of generating temperatures of above 300°C for numerous cycles. Utilizing the integrated micro-heater allows local fusion bonding of Fluoroethylenepropylene (Teflon-FEP) foils on a micrometer-scale while reducing unnecessary thermal stress. This is beneficial for the longevity of the micro-energy-harvesters, while simultaneously improving its performance. N2 - Poster KW - Energy-Harvesting KW - Fusion Bonding KW - Micro-Heater KW - SU-8 KW - Teflon-FEP KW - Mikrosystemtechnik Y1 - 2017 VL - 2017 IS - POSTER 2017 SP - 1 EP - 5 ER - TY - THES A1 - Ciba, Manuel T1 - Synchrony Measurement and Connectivity Estimation of Parallel Spike Trains from in vitro Neuronal Networks KW - Neuronales Netz Y1 - 2021 U6 - https://doi.org/https://doi.org/10.25972/OPUS-22364 ER - TY - GEN A1 - De Blasi, Stefano T1 - Simulation of Large Scale Neural Networks for Evaluation Applications T2 - 22nd International Student Conference on Electrical Engineering POSTER 2018 N2 - Understanding the complexity of biological neural networks like the human brain is one of the scientific challenges of our century. The organization of the brain can be described at different levels, ranging from small neural networks to entire brain regions. Existing methods for the description of functionally or effective connectivity are based on the analysis of relations between the activities of different neural units by detecting correlations or information flow. This is a crucial step in understanding neural disorders like Alzheimer’s disease and their causative factors. To evaluate these estimation methods, it is necessary to refer to a neural network with known connectivity, which is typically unknown for natural biological neural networks. Therefore, network simulations, also in silico, are available. In this work, the in silico simulation of large scale neural networks is established and the influence of different topologies on the generated patterns of neuronal signals is investigated. The goal is to develop standard evaluation methods for neurocomputational algorithms with a realistic large scale model to enable benchmarking and comparability of different studies. N2 - Poster KW - biological neural network KW - evaluation method KW - in silico KW - spike trains KW - Neuronales Netz Y1 - 2018 VL - 2018 IS - POSTER 2018 SP - 1 EP - 6 ER - TY - JOUR A1 - Szewczyk, Nathaniel J. A1 - Monici, Monica A1 - Thielemann, Christiane T1 - How to obtain an integrated picture of the molecular networks involved in adaptation to microgravity in different biological systems? JF - npj Microgravity N2 - Periodically, the European Space Agency (ESA) updates scientific roadmaps in consultation with the scientific community. The ESA SciSpacE Science Community White Paper (SSCWP) 9, “Biology in Space and Analogue Environments”, focusses in 5 main topic areas, aiming to address key community-identified knowledge gaps in Space Biology. Here we present one of the identified topic areas, which is also an unanswered question of life science research in Space: “How to Obtain an Integrated Picture of the Molecular Networks Involved in Adaptation to Microgravity in Different Biological Systems?” The manuscript reports the main gaps of knowledge which have been identified by the community in the above topic area as well as the approach the community indicates to address the gaps not yet bridged. Moreover, the relevance that these research activities might have for the space exploration programs and also for application in industrial and technological fields on Earth is briefly discussed. KW - Weltraumforschung KW - Biowissenschaften Y1 - 2024 U6 - https://doi.org/DOI: 10.1038/s41526-024-00395-3 VL - 2024 IS - 10 SP - 1 EP - 5 ER - TY - GEN A1 - Allig, Sebastian T1 - Controlled Cell Formation Using Bioprinting T2 - 22nd International Student Conference on Electrical Engineering POSTER 2018 N2 - Applying technologies of additive manufacturing to the field of tissue engineering created a pioneering new approach tomodelcomplex cell systemsartificially. Regarding its huge potential, bioprinting is still in its infancies and many questions are still unanswered. To address this issue, an extrusion-based bioprinting process was used to deposit human embryonic kidney (HEK) cellsin a defined pattern.It wasshownthat cells remained viable in the bioprinted construct for up to 8 days after printing (DAP). This work displays a proof of principle for a controlled cell formation which shall later be appliedtoin vitro drug screening tests. N2 - Poster KW - Bioprinting KW - Bioink KW - Tissue Engineering KW - In vitro cell systems KW - Biomaterial Y1 - 2018 VL - 2018 IS - POSTER 2018 SP - 1 EP - 5 ER - TY - JOUR A1 - Alves, Caroline L. A1 - Toutain, Thaise A1 - Aguiar, Patricia A1 - Pineda, Aruane M. A1 - Roster, Kirstin A1 - Thielemann, Christiane A1 - Porto, Joel A1 - Rodrigues, Francisco A. T1 - Diagnosis of autism spectrum disorder based on functional brain networks and machine learning JF - Scientific Reports N2 - Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets. KW - Maschinelles Lernen KW - Gehirn KW - Neuronales Netz KW - Autismus Y1 - 2023 UR - https://rdcu.be/dcwIP U6 - https://doi.org/https://doi.org/10.1038/s41598-023-34650-6 VL - 2023 IS - 13/8072 SP - 1 EP - 20 ER - TY - JOUR A1 - Trent, Davis A1 - Thielemann, Christiane A1 - Monici, Monica T1 - How are cell and tissue structure and function influenced by gravity and what are the gravity perception mechanisms? JF - npj Microgravity N2 - Progress in mechanobiology allowed us to better understand the important role of mechanical forces in the regulation of biological processes. Space research in the field of life sciences clearly showed that gravity plays a crucial role in biological processes. The space environment offers the unique opportunity to carry out experiments without gravity, helping us not only to understand the effects of gravitational alterations on biological systems but also the mechanisms underlying mechanoperception and cell/tissue response to mechanical and gravitational stresses. Despite the progress made so far, for future space exploration programs it is necessary to increase our knowledge on the mechanotransduction processes as well as on the molecular mechanisms underlying microgravity-induced cell and tissue alterations. This white paper reports the suggestions and recommendations of the SciSpacE Science Community for the elaboration of the section of the European Space Agency roadmap “Biology in Space and Analogue Environments” focusing on “How are cells and tissues influenced by gravity and what are the gravity perception mechanisms?” The knowledge gaps that prevent the Science Community from fully answering this question and the activities proposed to fill them are discussed. KW - Zellkultur KW - Schwere KW - Raumfahrtbiologie Y1 - 0024 UR - https://rdcu.be/dyCjL U6 - https://doi.org/10.1038/s41526-024-00357-9 VL - 2014 IS - 10 / 16 SP - 1 EP - 7 ER - TY - THES A1 - Köhler, Tim T1 - Auswirkungen elektromagnetischer Strahlung auf die elektrophysiologischen Eigenschaften neuronaler und kardialer Zellnetzwerke KW - Neuronales Netz KW - Elektromagnetische Strahlung Y1 - 2019 UR - https://tuprints.ulb.tu-darmstadt.de/id/eprint/8544 ER - TY - JOUR A1 - Alves, Caroline L. A1 - Wissel, Lennart A1 - Capetian, Philipp A1 - Thielemann, Christiane T1 - Functional connectivity and convolutional neural networks for automatic classification of EEG data JF - Clinical Neurophysiology KW - Elektroencephalographie KW - Neuronales Netz Y1 - 2022 U6 - https://doi.org/https://doi.org/10.1016/j.clinph.2022.01.086 VL - 2022 IS - 137 SP - e37 EP - e47 ER - TY - JOUR A1 - Bouillet, Thomas A1 - Ciba, Manuel A1 - Alves, Caroline L. A1 - Rodrigues, Francisco A. A1 - Thielemann, Christiane A1 - Colin, Morvane A1 - Buée, Luc A1 - Halliez, Sophie T1 - Revisiting the involvement of tau in complex neural network remodeling: analysis of the extracellular neuronal activity in organotypic brain slice co-cultures JF - Journal of Neural Engineering N2 - Objective: Tau ablation has a protective effect in epilepsy due to inhibition of the hyperexcitability/hypersynchrony. Protection may also occur in transgenic models of Alzheimer's disease by reducing the epileptic activity and normalizing the excitation/inhibition imbalance. However, it is difficult to determine the exact functions of tau, because tau knockout (tauKO) brain networks exhibit elusive phenotypes. In this study, we aimed to further explore the physiological role of tau using brain network remodeling. Approach: The effect of tau ablation was investigated in hippocampal-entorhinal slice co-cultures during network remodeling. We recorded the spontaneous extracellular neuronal activity over two weeks in single-slice cultures and co-cultures from control and tauKO mice. We compared the burst parameters and applied concepts and analytical tools intended for the analysis of the network synchrony and connectivity. Main results: Comparison of the control and tauKO co-cultures revealed that tau ablation had an anti-synchrony effect on the hippocampal-entorhinal two-slice networks at late stages of culture, in line with the literature. Differences were also found between the single-slice and co-culture conditions, which indicated that tau ablation had differential effects at the sub-network scale. For instance, tau ablation was found to have an anti-synchrony effect on the co-cultured hippocampal slices throughout the culture, possibly due to a reduction in the excitation/inhibition ratio. Conversely, tau ablation led to increased synchrony in the entorhinal slices at early stages of the co-culture, possibly due to homogenization of the connectivity distribution. Significance: The new methodology presented here proved useful for investigating the role of tau in the remodeling of complex brain-derived neural networks. The results confirm previous findings and hypotheses concerning the effects of tau ablation on neural networks. Moreover, the results suggest, for the first time, that tau has multifaceted roles that vary in different brain sub-networks. KW - Neuronales Netz KW - Alzheimerkrankheit KW - Gehirn KW - Schnittpräparat Y1 - 2022 U6 - https://doi.org/DOI 10.1088/1741-2552/aca261 VL - 2022 IS - November SP - 1 EP - 2 ER - TY - JOUR A1 - Alves, Caroline L. A1 - Pineda, Aruane M. A1 - Roster, Kirstin A1 - Thielemann, Christiane A1 - Rodrigues, Francisco A. T1 - EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer’s disease and schizophrenia JF - Journal of Physics: complexity N2 - Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a possible solution to this problem, as we describe in this work. We present a method for the automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG time series and deep learning. We show that our approach can classify patients with Alzheimer’s disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases, that use raw EEG time series, shows that our method provides the highest precision. Therefore, the application of deep neural networks on data from brain connections is a very promising method for the diagnosis of neurological disorders. KW - complex networks KW - Machine learning KW - Hirnfunktionsstörung KW - Alzheimerkrankheit KW - Schizophrenie KW - Elektroencephalographie Y1 - 2022 U6 - https://doi.org/DOI 10.1088/2632-072X/ac5f8d VL - 2022 IS - 3 SP - 1 EP - 13 ER - TY - THES A1 - Mayer, Margot T1 - Einfluss ionisierender Strahlung auf die elektrophysiologischen Eigenschaften sich entwickelnder neuronaler Netzwerke KW - Ionisierende Strahlung KW - Neuronales Netz Y1 - 2017 UR - http://nbn-resolving.de/urn:nbn:de:tuda-tuprints-70283 ER - TY - JOUR A1 - Krstić, Nenad A1 - Jüttner, Jens A1 - Giegerich, Lars A1 - Mayer, Margot A1 - Knuth, Monika A1 - Müller, Achim A1 - Thielemann, Christiane T1 - 3D printed biosensor for continuous glucose measurement in cell cultures JF - Annals of 3D Printed Medicine KW - Biosensor KW - Zellkultur Y1 - 2023 U6 - https://doi.org/doi.org/10.1016/j.stlm.2023.100111 SN - 2666-9641 VL - 10 PB - Elsevier BV ER - TY - THES A1 - Bestel, Robert T1 - Simulation neuronaler Aktionspotentiale in Hinblick auf Zellform und Physiologie sowie deren Ableitung mittels extarzellulärer Mikroelektroden KW - Mikroelektrode KW - Nervenzelle Y1 - 2018 U6 - https://doi.org/10.18453/rosdok_id00002390 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 - INPR A1 - Mayer, Margot T1 - Aberrant choroid plexus formation in human cerebral organoids exposed to radiation N2 - Brain tumor patients are commonly treated with radiotherapy, but the efficacy of the treatment is limited by its toxicity, particularly the risk of radionecrosis. We used human cerebral organoids to investigate the mechanisms and nature of postirradiation brain image changes commonly linked to necrosis. Irradiation of cerebral organoids lead to increased formation of ZO1+/AQP1+/CLN3+-choroid plexus (CP) structures. Increased CP formation was triggered by radiation via the NOTCH/WNT signaling pathways and associated with delayed growth and neural stem cell differentiation, but not necrosis. The effect was more pronounced in immature than in mature organoids, reflecting the clinically-observed increased radiosensitivity of the pediatric brain. Protons were more effective than X-rays at the same dose, as also observed in clinical treatments. We conclude that radiation-induced brain image-changes can be attributed to aberrant CP formation, providing a new cellular mechanism and strategy for possible countermeasures. KW - CNS cancer KW - radio therapy KW - neuroscience KW - Hirntumor KW - Strahlentherapie KW - Neurowissenschaften Y1 - 2023 UR - https://www.researchsquare.com/article/rs-3445801/v1 U6 - https://doi.org/https://doi.org/10.21203/rs.3.rs-3445801/v1 ER - TY - THES A1 - Körbitzer, Berit Silke T1 - Untersuchung funktioneller Schnittstellen zwischen Graphen und neuronalen Zellen KW - Mikroelektrode KW - Array KW - Graphen KW - Elektrode Y1 - 2019 U6 - https://doi.org/10.25534/tuprints-00011462 ER - TY - THES A1 - Emmerich, Florian T1 - Eigenschaften und Anwendungen von Dünnschicht-Elektretmaterialien KW - Dünnschichttechnik KW - Elektret Y1 - 2020 U6 - https://doi.org/10.25534/tuprints-00012056 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 - 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 - TY - JOUR A1 - Alves, Caroline L. A1 - Rodrigues, Francisco A. A1 - Sallum, Loriz Francisco A1 - Toutain, Thaise A1 - Porto, Joel A1 - Aguiar, Patricia A1 - Thielemann, Christiane A1 - Möckel, Michael T1 - Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis JF - plos one N2 - Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML c KW - Aufmerksamkeitsdefizit-Syndrom KW - Autismus KW - Funktionelle Kernspintomografie Y1 - 2024 UR - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305630 U6 - https://doi.org/https://doi.org/10.1371/journal.pone.0305630 VL - 2024 IS - 19(10) SP - 1 EP - 35 ER - TY - INPR A1 - Schickel, Esther A1 - Bender, Tamara A1 - Kaysan, Leon A1 - Hufgard, Simone A1 - Mayer, Margot A1 - Grosshans, David R. A1 - Thielemann, Christiane A1 - Schroeder, Insa S. T1 - Human cerebral organoids model tumor infiltration and migration supported by astrocytes in an autologous setting T2 - bioRxiv N2 - SummaryEfforts to achieve precise and efficient tumor targeting of highly malignant brain tumors are constrained by the dearth of appropriate models to study the effects and potential side effects of radiation, chemotherapy, and immunotherapy on the most complex human organ, the brain. We established a cerebral organoid model of brain tumorigenesis in an autologous setting by overexpressing c-MYC as one of the most common oncogenes in brain tumors. GFP+/c-MYChighcells were isolated from tumor organoids and used in two different culture approaches: assembloids comprising of a normal cerebral organoid with a GFP+/c-MYChightumor sphere and co-culture of cerebral organoid slices at air-liquid interface with GFP+/c-MYChighcells. GFP+/c-MYChighcells used in both approaches exhibited tumor-like properties, including overexpression of the c-MYC oncogene, high proliferative and invasive potential, and an immature phenotype as evidenced by increased expression of Ki-67, VIM, and CD133. Organoids and organoid slices served as suitable scaffolds for infiltrating tumor-like cells. Using our highly reproducible and powerful model system that allows long-term culture, we demonstrated that the migratory and infiltrative potential of tumor-like cells is shaped by the environment in which glia cells provide support to tumor-like cells. KW - Hirntumor Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1101/2025.01.29.635456 VL - 2025 PB - Cold Spring Harbor Laboratory ER - TY - JOUR A1 - Schickel, Esther A1 - Bender, Tamara A1 - Kaysan, Leon A1 - Hufgard, Simone A1 - Mayer, Margot A1 - Grosshans, David R. A1 - Thielemann, Christiane A1 - Schroeder, Insa S. T1 - Human cerebral organoids model tumor initiation and infiltration in an autologous astrocyte-supported setting JF - iScience N2 - Efforts to efficiently target brain tumors are constrained by the dearth of appropriate models to study tumor behavior towards treatment approaches as well as potential side effects to the surrounding normal tissue. We established a reproducible cerebral organoid model of brain tumorigenesis in an autologous setting by overexpressing c-MYC, a common oncogene in brain tumors. GFP+/c-MYChigh cells were isolated from tumor organoids and used in two different approaches: GFP+/c-MYChigh cells co-cultured with cerebral organoid slices or fused as spheres to whole organoids. GFP+/c-MYChigh cells used in both approaches exhibited tumor-like properties, including an immature phenotype and a highly proliferative and invasive potential. We demonstrate that the latter is influenced by astrocytes supporting the GFP+/c-MYChigh cells while X-ray irradiation significantly kills and impairs tissue infiltration of GFP+/c-MYChigh cells. In summary, the model represents major features of tumorous and adjacent normal tissue and may be used to evaluate appropriate cancer treatments. KW - Hirntumor Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1016/j.isci.2025.113334 SN - 2589-0042 PB - Elsevier BV ER - TY - CHAP A1 - Hufgard, Simone A1 - Mayer, Margot A1 - Schroeder, Insa S. A1 - Thielemann, Christiane T1 - 3D compartmentilisation for analyzing functional long-range connectivity between brain regions T2 - 4th Microphysiological Systems World Summit, Brussels, Belgium KW - Hirnareal Y1 - 2025 IS - June 2025 ER - TY - INPR A1 - Alves, Caroline A1 - Paulo, Artur José Marques A1 - de Faria, Danilo Donizete A1 - Sato, João Ricardo A1 - Borges, Vanderci A1 - Silva, Sonia de Azevedo A1 - Ferraz, Henrique Ballalai A1 - Rodrigues, Francisco A. A1 - Thielemann, Christiane A1 - Moeckel, Michael A1 - Aguiar, Patricia de Carvalho T1 - Decoding Dystonia: unveiling neural patterns with interpretable EEG-Based Machine Learning N2 - Dystonia has a multifaceted and complex pathogenesis. Current diagnostic proce-dures, which focus primarily on clinical signs, may lack accuracy due to the variable presentationsof different dystonia types. There is a need for objective, interpretable, and non-invasive diagnostictools. This study aims to develop an interpretable electroencephalography (EEG)-basedmachine learning (ML) and deep learning (DL) approach to distinguish between focal upper limbdystonia (ULD), cervical dystonia (CD), and healthy controls (HC). EEG data were recorded during resting-state, writing-from-memory, and finger-tapping tasks. The EEG signals were segmented into windows to generate connectivity matricesusing various pairwise correlation metrics. Machine learning models were trained to classify thegroups, with performance evaluated using accuracy and area under the curve (AUC) metrics. Our approach achieved accuracy and AUC scores close to 100%. Transfer entropyemerged as the most effective connectivity metric, revealing altered brain connections in dystonia.Complex network measures outperformed traditional EEG features, highlighting the relevance offunctional connectivity. Resting-state EEG showed the highest classification performance for ULD,suggesting strong diagnostic potential. Conclusions: This study provides the first machine learning-based comparison between differenttypes of dystonia, introduces novel cervical dystonia EEG data, and yields medically interpretableinsights into altered brain connectivity. The findings enhance our understanding of dystonia and support using EEG as alow-cost, interpretable tool for diagnosing and developing brain-machine interfaces. KW - Dystonie KW - Elektroencephalographie KW - Maschinelles Lernen Y1 - 2025 U6 - https://doi.org/https://doi.org/10.21203/rs.3.rs-7483388/v1 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Flachs, Dennis A1 - Zhukov, Sergey A1 - Zech, Isabella A1 - Schreck, Timo A1 - Belle, Stefan A1 - Seggern, Heinz von A1 - Kupnik, Mario A1 - Altmann, Alexander Anton A1 - Thielemann, Christiane T1 - Enzymatic Self-Degradable PLA-Based Electrets JF - Journal of Polymers and the Environment N2 - In recent years, the demand for sustainable and degradable materials and electronic devices has increased significantly. Among a range of biodegradable polymers, poly(lactic acid) (PLA) is a good alternative to conventional petrol-based polymers because of its attractive mechanical properties and its easy processability. Recently, PLA has also been described as a promising dielectric material with piezoelectric and electret properties. We expect that PLA—after further optimization— will play an important role as a material for environmentally friendly sensors in the future, where first applications such as air filters and pressure sensors have already been proposed. However, degradation under normal ambient conditions is very slow, and an accelerated and controllable degradation process is highly desirable for any type of PLA-based sensors. Enzymatic hydrolysis with embedded enzymes has been proposed as an approach to accelerate and control degradation. In this work, we investigate the properties of PLA in terms of dielectric and mechanical properties with a focus on its ability to store charges after the enzyme proteinase K (Trit. album) has been incorporated. Results reveal that proteinase K has a positive effect on the charge stability of solvent-cast PLA electrets after complete evaporation of the solvent. Furthermore, we observed a concentration-dependent acceleration of mass loss in a Tris-HCl buffer. A fast degradation within only one day occurred at a concentration of 6 wt% proteinase K. KW - Biologisch abbaubarer Kunststoff Y1 - 2024 U6 - https://doi.org/Journal of https://doi.org/10.1007/s10924-024-03240-6 VL - 2024 IS - 1 SP - 1 EP - 11 ER - TY - JOUR A1 - Flachs, Dennis A1 - Emmerich, Florian A1 - Thielemann, Christiane T1 - Fabrication process for FEP piezoelectrets based on photolithographically structured thermoforming templates JF - Microsystem Technologies N2 - Piezoelectrets fabricated from fluoroethylenepropylene (FEP)-foils have shown drastic increase of their piezoelectric properties during the last decade. This led to the development of FEP-based energy harvesters, which are about to evolve into a technology with a power-generation-capacity of milliwatt per square-centimeter at their resonance frequency. Recent studies focus on piezoelectrets with solely negative charges, as they have a better charge stability and a better suitability for implementation in rising technologies, like the internet of things (IOT) or portable electronics. With these developments heading towards applications of piezoelectrets in the near future, there is an urgent need to also address the fabrication process in terms of scalability, reproducibility and miniaturization. In this study, we firstly present a comprehensive review of the literature for a deep insight into the research that has been done in the field of FEP-based piezoelectrets. For the first time, we propose the employment of microsystem-technology and present a process for the fabrication of thermoformed FEP piezoelectrets based on thermoforming SU-8 templates. Following this process, unipolar piezoelectrets were fabri� cated with air void dimensions in the range of 300–1000 lm in width and approx. 90 lm in height. For samples with a void size of 1000 lm, a d33-coefficient up to 26,508 pC/N has been achieved, depending on the applied seismic mass. Finally, the properties as energy harvester were characterized. At the best, an electrical power output of 0.51 mW was achieved for an acceleration of 1 � g with a seismic mass of 101 g. Such piezoelectrets with highly defined dimensions show good energy output in relation to volume, with high potential for widespread applications. KW - Mikrosystemtechnik Y1 - 2022 UR - Microsystem Technologies https://doi.org/10.1007/s00542-022-05405-6 U6 - https://doi.org/doi.org/10.1007/s00542-022-05405-6 VL - 2022 IS - November SP - 0 EP - 0 ER - TY - CHAP A1 - Altmann, Alexander Anton A1 - Suppelt, Sven A1 - Ruhl, Max A1 - Schaumann, Stephan A1 - Latsch, Bastian A1 - Ben Dali, Omar A1 - Zhukov, Sergey A1 - Flachs, Dennis A1 - Zhang, Xiaoqing A1 - Thielemann, Christiane A1 - Seggern, Heinz von A1 - Kupnik, Mario T1 - Monolithic Wideband Air-Coupled Ultrasonic Transducer Based on Additively Manufactured Ferroelectrets T2 - 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS) N2 - Air-coupled ultrasonic transducers are widely used in non-destructive testing, acoustical sonar systems, and biomedical imaging. These applications require transducers that operate effectively across a broad acoustic frequency spectrum, offer adaptable geometric designs, and increasingly incorporate eco-friendly materials. In this work, we present a monolithic, 3D-printed air-coupled ultrasonic transducer based on ferroelectrets (FEs) and fabricated from biocompatible polylactic acid (PLA). We evaluated the transducer’s acoustical performance by measuring the surface velocity of its active area using laser Doppler vibrometry and assessed its robustness during continuous operation over a 19-day period. Additionally, we measured the sound pressure level (SPL) and wideband characteristics in an anechoic chamber across excitation frequencies from 1kHz to 100kHz. At a resonance frequency of 33kHz, our transducer achieved an SPL of 94.3dB and surface velocities up to 37mm/s. The measured bandwidth of 65.2kHz at the -6dB threshold corresponds to a fractional bandwidth of 189%. The observed exponential decay of the surface velocity, stabilizing at 15% of its initial amplitude, aligns with the isothermal surface potential decay typically observed in FE films made from PLA. These results demonstrate the effectiveness of the transducer, which features an adaptable backplate for tuning acoustic properties. The low-cost transducer, manufactured from biocompatible PLA, is particularly suited for imaging and biomedical applications furthering green electronics. KW - Ultraschallwandler KW - Biomedizinische Technik Y1 - 2024 U6 - https://doi.org/10.1109/UFFC-JS60046.2024.10793858 SP - 1 EP - 4 PB - IEEE ER - TY - CHAP A1 - Flachs, Dennis A1 - Bernhard, Levin A1 - Thielemann, Christiane T1 - Fully 3D-Printed Rotational Energy Harvester Based On Bipolar Charged PLA Electrets T2 - Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS) N2 - 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. KW - rotational energy harvesting KW - PLA KW - electret KW - 3D printing KW - Elektret KW - 3D-Druck KW - Energy Harvesting Y1 - 2024 UR - https://ieeexplore.ieee.org/abstract/document/10814467 SN - 979-8-3503-8020-0 U6 - https://doi.org/979-8-3503-8020-0 VL - 2024 PB - IEEE ER - TY - JOUR A1 - Flachs, Dennis A1 - Etzel, Johannes A1 - Mayer, Margot A1 - Harbecke, Frederic A1 - Belle, Stefan A1 - Rickmeyer, Tim A1 - Thielemann, Christiane T1 - Characterization of electrically conductive, printable ink based on alginate hydrogel and graphene nanoplatelets JF - Biomedical Engineering Advances N2 - In recent years, there has been an increasing interest in electrically conductive hydrogels for a wide range of biomedical applications, like tissue engineering or biosensors. In this study, we present a cost-effective conductive hydrogel based on alginate and graphene nanoplatelets for extrusion-based bioprinters. The hydrogel is prepared under ambient conditions avoiding high temperatures detrimental for cell culture environments. Investigation of the hydrogel revealed a conductivity of up to 7.5 S/cm, depending on the ratio of platelets. Furthermore, in vitro tests with human embyronic kidney cells - as an example cell type - showed good adhesion of the cells to the surface of the conductive hydrogel. Electrochemical measurements revealed a low electrode impedance which is desirable for the extracellular recording, but also low electrode capacitance, which is unfavorable for electrical stimulation purposes. Therefore, future experiments with the graphene nanoplatelets-based hydrogels will focus on electrodes for biosensors and extracellular recordings of neurons or cardiac myocytes. KW - Hydrogel KW - Biosensor KW - Alginate Y1 - 2022 U6 - https://doi.org/https://doi.org/10.1016/j.bea.2022.100045 VL - 2022 IS - 4 SP - 1 EP - 2 ER - TY - JOUR A1 - Flachs, Dennis A1 - Ciba, Manuel T1 - Cell-based sensor chip for neurotoxicity measurements in drinking water JF - Lékař a technika - Clinician and Technology N2 - Our drinking water contains residues of pharmaceuticals. A sub-group of these contaminants are neuro-active substances, the antiepileptic carbamazepine being one of the most relevant. For assessment of the neurotoxicity of this drug at a sub-therapeutic level, a cell-based sensor chip platform has been realized and characterized. For this purpose, a microelectrode array chip was designed and processed in a clean room and optimized in terms of low processing costs and good recording properties. For characterization of the system neuronal cells were plated on microelectrode array chips and electrical activity was measured as a function of applied carbamazepine concentration. We found that the relative spike rate decreased with increasing drug concentration resulted in IC50 values of around 36 μM. This value is five orders of magnitude higher than the maximal dose found in drinking water. IC50 values for burst rate, burst duration and synchrony were slightly higher, suggesting spike rate being a more sensitive parameter to carbamazepine. KW - Microelectrode array KW - Carbamazepine KW - Neurotoxicity KW - Cell-based biosensor KW - Mikroelektrode KW - Array KW - Biosensor KW - Neurotoxizität Y1 - 2016 VL - 46 IS - 2 SP - 46 EP - 50 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 -