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search.filters.applied.f.access: openAccess × End date: 2024 × Start date: 2020 × DDC: 500 × Type: Text × Subject: Humans × Subject: human ×

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    Scanning electron microscopy preparation of the cellular actin cortex: A quantitative comparison between critical point drying and hexamethyldisilazane drying
    (San Francisco, California, US : PLOS, 2021) Schu, Moritz; Terriac, Emmanuel; Koch, Marcus; Paschke, Stephan; Lautenschläger, Franziska; Flormann, Daniel A.D.
    The cellular cortex is an approximately 200-nm-thick actin network that lies just beneath the cell membrane. It is responsible for the mechanical properties of cells, and as such, it is involved in many cellular processes, including cell migration and cellular interactions with the environment. To develop a clear view of this dense structure, high-resolution imaging is essential. As one such technique, electron microscopy, involves complex sample preparation procedures. The final drying of these samples has significant influence on potential artifacts, like cell shrinkage and the formation of artifactual holes in the actin cortex. In this study, we compared the three most used final sample drying procedures: critical-point drying (CPD), CPD with lens tissue (CPD-LT), and hexamethyldisilazane drying. We show that both hexamethyldisilazane and CPD-LT lead to fewer artifactual mesh holes within the actin cortex than CPD. Moreover, CPD-LT leads to significant reduction in cell height compared to hexamethyldisilazane and CPD. We conclude that the final drying procedure should be chosen according to the reduction in cell height, and so CPD-LT, or according to the spatial separation of the single layers of the actin cortex, and so hexamethyldisilazane.
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    Evolutionary design of explainable algorithms for biomedical image segmentation
    ([London] : Nature Publishing Group UK, 2023) Cortacero, Kévin; McKenzie, Brienne; Müller, Sabina; Khazen, Roxana; Lafouresse, Fanny; Corsaut, Gaëlle; Van Acker, Nathalie; Frenois, François-Xavier; Lamant, Laurence; Meyer, Nicolas; Vergier, Béatrice; Wilson, Dennis G.; Luga, Hervé; Staufer, Oskar; Dustin, Michael L.; Valitutti, Salvatore; Cussat-Blanc, Sylvain
    An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.
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    Computational design and optimization of electro-physiological sensors
    ([London] : Nature Publishing Group UK, 2021) Nittala, Aditya Shekhar; Karrenbauer, Andreas; Khan, Arshad; Kraus, Tobias; Steimle, Jürgen
    Electro-physiological sensing devices are becoming increasingly common in diverse applications. However, designing such sensors in compact form factors and for high-quality signal acquisition is a challenging task even for experts, is typically done using heuristics, and requires extensive training. Our work proposes a computational approach for designing multi-modal electro-physiological sensors. By employing an optimization-based approach alongside an integrated predictive model for multiple modalities, compact sensors can be created which offer an optimal trade-off between high signal quality and small device size. The task is assisted by a graphical tool that allows to easily specify design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. They demonstrate that generated designs can achieve an optimal balance between the size of the sensor and its signal acquisition capability, outperforming expert generated solutions.
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    Non-thermal plasma modulates cellular markers associated with immunogenicity in a model of latent HIV-1 infection
    (San Francisco, California, US : PLOS, 2021) Mohamed, Hager; Clemen, Ramona; Freund, Eric; Lackmann, Jan-Wilm; Wende, Kristian; Connors, Jennifer; Haddad, Elias K.; Dampier, Will; Wigdahl, Brian; Miller, Vandana; Bekeschus, Sander; Krebs, Fred C.; Kashanchi, Fatah
    Effective control of infection by human immunodeficiency virus type 1 (HIV-1), the causative agent of the acquired immunodeficiency syndrome (AIDS), requires continuous and life-long use of anti-retroviral therapy (ART) by people living with HIV-1 (PLWH). In the absence of ART, HIV-1 reemergence from latently infected cells is ineffectively suppressed due to suboptimal innate and cytotoxic T lymphocyte responses. However, ART-free control of HIV-1 infection may be possible if the inherent immunological deficiencies can be reversed or restored. Herein we present a novel approach for modulating the immune response to HIV-1 that involves the use of non-thermal plasma (NTP), which is an ionized gas containing various reactive oxygen and nitrogen species (RONS). J-Lat cells were used as a model of latent HIV-1 infection to assess the effects of NTP application on viral latency and the expression of pro-phagocytic and pro-chemotactic damage-associated molecular patterns (DAMPs). Exposure of J-Lat cells to NTP resulted in stimulation of HIV-1 gene expression, indicating a role in latency reversal, a necessary first step in inducing adaptive immune responses to viral antigens. This was accompanied by the release of pro-inflammatory cytokines and chemokines including interleukin-1β (IL-1β) and interferon-γ (IFN-γ); the display of pro-phagocytic markers calreticulin (CRT), heat shock proteins (HSP) 70 and 90; and a correlated increase in macrophage phagocytosis of NTP-exposed J-Lat cells. In addition, modulation of surface molecules that promote or inhibit antigen presentation was also observed, along with an altered array of displayed peptides on MHC I, further suggesting methods by which NTP may modify recognition and targeting of cells in latent HIV-1 infection. These studies represent early progress toward an effective NTP-based ex vivo immunotherapy to resolve the dysfunctions of the immune system that enable HIV-1 persistence in PLWH.