BIOMEMS Lab
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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.
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.
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
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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.
3D compartmentilisation for analyzing functional long-range connectivity between brain regions
(2025)
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.
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.
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.
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.
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