Medizin

Permanent URI for this collectionhttps://oa.tib.eu/renate/handle/123456789/3667

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Digitalisierung in den Gesundheitsberufen
([Leverkusen] : Verlag Barbara Budrich, 2024) Weyland, Ulrike; Koschel, Wilhelm; Reiber, Karin; Dorin, Lena; Peters, Miriam; Arndt, Laura; Behr, Dominik; Bergmann, Dana; Buchmann, Ulrike; Ebbighausen, Marc; Engl, Anna-Teresa; Ettl, Katrin; Fathi, Madjid; Fischer, Andreas; Freese, Christiane; Haussmann, Andreas; Hiestand, Stefanie; Hofstetter, Sebastian; Hüttner, Aneli; Jahn, Patrick; Jürgensen, Anke; Kaiser, Sophie; Kaufhold, Marisa; Kismihók, Gábor; Klus, Christina; Kobus, Julia; Köhler, Sonja; Kraft, Bernhard; Makowsky, Katja; Meng, Michael; Michel, Natalie; Nagel, Lisa; Nauerth, Annette; Nerdel, Claudia; Paulicke, Denny; Preißler, Ronja; Rasheed, Hasan A.; Rechl, Friederike; Richter, Katja E.; Richter, Patrick; Schröder, Martina; Schröer, Laura; Schwarz, Karsten; Seltrecht, Astrid; Steindorff, Jenny-Victoria; Stirner, Alexander; Stoevesandt, Dietrich; Völz, Silke; Wagner-Herrbach, Cornelia; Weber, Christian; Wittmann, Eveline; Zepelin, Lyn Anne von; Ziegler, Sven; Zilezinski, Max
Digitale Technologien führen zu veränderten Kommunikations-, Lern- und Arbeitsformen. Für die Gesundheitsberufe ergeben sich durch die Digitalisierung vielfältige Veränderungen und Herausforderungen, die bei positiver Wendung auch als Chance verstanden werden können. Wenn Digitalisierungsprozesse in den Gesundheitsberufen aktiv durch die Berufsgruppen mitgestaltet werden, so können positive Ansätze für die Versorgung hilfs- und pflegebedürftiger Menschen entwickelt werden, aber ebenso für die Professionalisierung der Fachkräfte und des beruflichen Bildungspersonals. Dieser Band dokumentiert die Beiträge zum AG-BFN-Forum „Digitalisierung in den Gesundheitsberufen“, das im Oktober 2021 an der Universität Münster stattfand. Im Fokus stehen aktuelle Entwicklungen in den Bereichen Digitalität in pflege- und gesundheitsberuflichen Handlungsfeldern, Professionalisierung des Bildungspersonals und digital gestützte Lehr-/Lernszenarien in den Gesundheitsberufen.
An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study
(Basel : MDPI, 2022) Torrente, María; Sousa, Pedro A.; Hernández, Roberto; Blanco, Mariola; Calvo, Virginia; Collazo, Ana; Guerreiro, Gracinda R.; Núñez, Beatriz; Pimentao, Joao; Sánchez, Juan Cristóbal; Campos, Manuel; Costabello, Luca; Novacek, Vit; Menasalvas, Ernestina; Vidal, María Esther; Provencio, Mariano
Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.
Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study
(Toronto : [Verlag nicht ermittelbar], 2022) Benítez-Andrades, José Alberto; Alija-Pérez, José-Manuel; Vidal, Maria-Esther; Pastor-Vargas, Rafael; García-Ordás, María Teresa
Background: Eating disorders affect an increasing number of people. Social networks provide information that can help. Objective: We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. Methods: We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. Results: A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer-based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%). Conclusions: Bidirectional encoder representations from transformer-based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder-related tweets.
Non Thermal Plasma Sources of Production of Active Species for Biomedical Uses: Analyses, Optimization and Prospect
(London : IntechOpen, 2011) Yousfi, M.; Merbahi, N.; Sarrette, P. J.; Eichwald, O.; Ricard, A.; Gardou, J.P.; Ducasse, O.; Benhenni, M.; Fazel-Rezai, Reza
[no abstract available]
The systems biology format converter
(London : BioMed Central, 2016) Rodriguez, Nicolas; Pettit, Jean-Baptiste; Dalle Pezze, Piero; Li, Lu; Henry, Arnaud; van Iersel, Martijn P.; Jalowicki, Gael; Kutmon, Martina; Natarajan, Kedar N.; Tolnay, David; Stefan, Melanie I.; Evelo, Chris T.; Le Novère, Nicolas
Background: Interoperability between formats is a recurring problem in systems biology research. Many tools have been developed to convert computational models from one format to another. However, they have been developed independently, resulting in redundancy of efforts and lack of synergy. Results: Here we present the System Biology Format Converter (SBFC), which provide a generic framework to potentially convert any format into another. The framework currently includes several converters translating between the following formats: SBML, BioPAX, SBGN-ML, Matlab, Octave, XPP, GPML, Dot, MDL and APM. This software is written in Java and can be used as a standalone executable or web service. Conclusions: The SBFC framework is an evolving software project. Existing converters can be used and improved, and new converters can be easily added, making SBFC useful to both modellers and developers. The source code and documentation of the framework are freely available from the project web site.
New Paradigm for a targeted cancer therapeutic approach: A short review on potential synergy of gold nanoparticles and Cold Atmospheric Plasma
(Basel : MDPI, 2017) Aryal, Sajesan; Bisht, Gunjan
Application of Gold nanoparticles and Cold Atmospheric plasma as a targeted therapeutic adjunct has been widely investigated separately in cancer therapy. Gold nanoparticles, with their biocompatibility, lower cytotoxicity and superior efficacy, are becoming substantially more significant in modern cancer therapy. Likewise, cold atmospheric plasma, with rich reactive species including reactive oxygen species (ROS) and reactive nitrogen species (RNS), is being explored to selectively target and kill cancer cells, making them a promising anticancer agent. Recent scientific studies have shown that there is a potential synergy between these two aspects. Induction of apoptosis/necrosis due to oxidative stress may be a probable mechanism of their cytotoxic effect. The synergetic effect of the two therapeutic approaches could be tantamount to maximized targeted efficacy on the treatment of diseases like cancer.
SciPy 1.0: fundamental algorithms for scientific computing in Python
(London [u.a.] : Nature Publishing Group, 2020) Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt; Reddy, Tyler; Cournapeau, David; Burovski, Evgeni; Peterson, Pearu; Weckesser, Warren; Bright, Jonathan; van der Walt, Stéfan J.; Brett, Matthew; Wilson, Joshua; Millman, K. Jarrod; Mayorov, Nikolay; Nelson, Andrew R. J.; Jones, Eric; Kern, Robert; Larson, Eric; Carey, C J; Polat, İlhan; Feng, Yu; Moore, Eric W.; VanderPlas, Jake; Laxalde, Denis; Perktold, Josef; Cimrman, Robert; Henriksen, Ian; Quintero, E. A.; Harris, Charles R.; Archibald, Anne M.; Ribeiro, Antônio H.; Pedregosa, Fabian; van Mulbregt, Paul; Vijaykumar, Aditya; Bardelli, Alessandro Pietro; Rothberg, Alex; Hilboll, Andreas; Kloeckner, Andreas; Scopatz, Anthony; Lee, Antony; Rokem, Ariel; Woods, C. Nathan; Fulton, Chad; Masson, Charles; Häggström, Christian; Fitzgerald, Clark; Nicholson, David A.; Hagen, David R.; Pasechnik, Dmitrii V.; Olivetti, Emanuele; Martin, Eric; Wieser, Eric; Silva, Fabrice; Lenders, Felix; Wilhelm, Florian; Young, G.; Price, Gavin A.; Ingold, Gert-Ludwig; Allen, Gregory E.; Lee, Gregory R.; Audren, Hervé; Probst, Irvin; Dietrich, Jörg P.; Silterra, Jacob; Webber, James T; Slavič, Janko; Nothman, Joel; Buchner, Johannes; Kulick, Johannes; Schönberger, Johannes L.; de Miranda Cardoso, José Vinícius; Reimer, Joscha; Harrington, Joseph; Rodríguez, Juan Luis Cano; Nunez-Iglesias, Juan; Kuczynski, Justin; Tritz, Kevin; Thoma, Martin; Newville, Matthew; Kümmerer, Matthias; Bolingbroke, Maximilian; Tartre, Michael; Pak, Mikhail; Smith, Nathaniel J.; Nowaczyk, Nikolai; Shebanov, Nikolay; Pavlyk, Oleksandr; Brodtkorb, Per A.; Lee, Perry; McGibbon, Robert T.; Feldbauer, Roman; Lewis, Sam; Tygier, Sam; Sievert, Scott; Vigna, Sebastiano; Peterson, Stefan; More, Surhud; Pudlik, Tadeusz; Oshima, Takuya; Pingel, Thomas J.; Robitaille, Thomas P.; Spura, Thomas; Jones, Thouis R.; Cera, Tim; Leslie, Tim; Zito, Tiziano; Krauss, Tom; Upadhyay, Utkarsh; Halchenko, Yaroslav O.; Vázquez-Baeza, Yoshiki
SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
Correcting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling
(San Francisco, California, US : PLOS, 2023) Mayerhöfer, Thomas G.; Noda, Isao; Pahlow, Susanne; Heintzmann, Rainer; Popp, Jürgen
Recently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.
Clonal Complexes Distribution of Staphylococcus aureus Isolates from Clinical Samples from the Caribbean Islands
(Basel : MDPI, 2023) Monecke, Stefan; Akpaka, Patrick Eberechi; Smith, Margaret R.; Unakal, Chandrashekhar G.; Thoms Rodriguez, Camille-Ann; Ashraph, Khalil; Müller, Elke; Braun, Sascha D.; Diezel, Celia; Reinicke, Martin; Ehricht, Ralf
The aim of this study was to comprehensively characterise S. aureus from the Caribbean Islands of Trinidad and Tobago, and Jamaica. A total of 101 S. aureus/argenteus isolates were collected in 2020, mainly from patients with skin and soft tissue infections. They were characterised by DNA microarray allowing the detection of ca. 170 target genes and assignment to clonal complexes (CC)s and strains. In addition, the in vitro production of Panton–Valentine leukocidin (PVL) was examined by an experimental lateral flow assay. Two isolates were identified as S. argenteus, CC2596. The remaining S. aureus isolates were assigned to 21 CCs. The PVL rate among methicillin-susceptible S. aureus (MSSA) isolates was high (38/101), and 37 of the 38 genotypically positive isolates also yielded positive lateral flow results. The isolate that did not produce PVL was genome-sequenced, and it was shown to have a frameshift mutation in agrC. The high rate of PVL genes can be attributed to the presence of a known local CC8–MSSA clone in Trinidad and Tobago (n = 12) and to CC152–MSSA (n = 15). In contrast to earlier surveys, the USA300 clone was not found, although one MSSA isolate carried the ACME element, probably being a mecA-deficient derivative of this strain. Ten isolates, all from Trinidad and Tobago, were identified as MRSA. The pandemic ST239–MRSA–III strain was still common (n = 7), but five isolates showed a composite SCCmec element not observed elsewhere. Three isolates were sequenced. That showed a group of genes (among others, speG, crzC, and ccrA/B-4) to be linked to its SCC element, as previously found in some CC5– and CC8–MRSA, as well as in S. epidermidis. The other three MRSA belonged to CC22, CC72, and CC88, indicating epidemiological connections to Africa and the Middle East.
PIEZO1-mediated mechanosensing governs NK cell killing efficiency and infiltration in three-dimensional matrices
([Cold Spring Harbor] : Cold Spring Harbor Laboratory (CSHL), 2024) Yanamandra, Archana K.; Zhang, Jingnan; Montalvo, Galia; Zhou, Xiangda; Biedenweg, Doreen; Zhao, Renping; Sharma, Shulagna; Hoth, Markus; Lautenschläger, Franziska; Otto, Oliver; del Campo, Aránzazu; Qu, Bin
Natural killer (NK) cells play a vital role in eliminating tumorigenic cells. Efficient locating and killing of target cells in complex three-dimensional (3D) environments are critical for their functions under physiological conditions. However, the role of mechanosensing in regulating NK cell killing efficiency in physiologically relevant scenarios is poorly understood. Here, we report that the responsiveness of NK cells is regulated by tumor cell stiffness. NK cell killing efficiency in 3D is impaired against softened tumor cells, while it is enhanced against stiffened tumor cells. Notably, the durations required for NK cell killing and detachment are significantly shortened for stiffened tumor cells. Furthermore, we have identified PIEZO1 as the predominantly expressed mechanosensitive ion channel among the examined candidates in NK cells. Perturbation of PIEZO1 abolishes stiffness-dependent NK cell responsiveness, significantly impairs the killing efficiency of NK cells in 3D, and substantially reduces NK cell infiltration into 3D collagen matrices. Conversely, PIEZO1 activation enhances NK killing efficiency as well as infiltration. In conclusion, our findings demonstrate that PIEZO1-mediated mechanosensing is crucial for NK killing functions, highlighting the role of mechanosensing in NK cell killing efficiency under 3D physiological conditions and the influence of environmental physical cues on NK cell functions.
Impact of mucus modulation by N-acetylcysteine on nanoparticle toxicity
(Amsterdam : Elsevier, 2023) Meziu, Enkeleda; Shehu, Kristela; Koch, Marcus; Schneider, Marc; Kraegeloh, Annette
Human respiratory mucus is a biological hydrogel that forms a protective barrier for the underlying epithelium. Modulation of the mucus layer has been employed as a strategy to enhance transmucosal drug carrier transport. However, a drawback of this strategy is a potential reduction of the mucus barrier properties, in particular in situations with an increased exposure to particles. In this study, we investigated the impact of mucus modulation on its protective role. In vitro mucus was produced by Calu-3 cells, cultivated at the air-liquid interface for 21 days and used for further testing as formed on top of the cells. Analysis of confocal 3D imaging data revealed that after 21 days Calu-3 cells secrete a mucus layer with a thickness of 24 ± 6 μm. Mucus appeared to restrict penetration of 500 nm carboxyl-modified polystyrene particles to the upper 5–10 μm of the layer. Furthermore, a mucus modulation protocol using aerosolized N-acetylcysteine (NAC) was developed. This treatment enhanced the penetration of particles through the mucus down to deeper layers by means of the mucolytic action of NAC. These findings were supported by cytotoxicity data, indicating that intact mucus protects the underlying epithelium from particle-induced effects on membrane integrity. The impact of NAC treatment on the protective properties of mucus was probed by using 50 and 100 nm amine-modified and 50 nm carboxyl-modified polystyrene nanoparticles, respectively. Cytotoxicity was only induced by the amine-modified particles in combination with NAC treatment, implying a reduced protective function of modulated mucus. Overall, our data emphasize the importance of integrating an assessment of the protective function of mucus into the development of therapy approaches involving mucus modulation.
Human glabrous skin contains crystallized urea dendriform structures in the stratum corneum which affect the hydration levels
(Oxford : Wiley-Blackwell, 2023) Infante, Victor Hugo Pacagnelli; Bennewitz, Roland; Kröger, Marius; Meinke, Martina C.; Darvin, Maxim E.
Glabrous skin is hair-free skin with a high density of sweat glands, which is found on the palms, and soles of mammalians, covered with a thick stratum corneum. Dry hands are often an occupational problem which deserves attention from dermatologists. Urea is found in the skin as a component of the natural moisturizing factor and of sweat. We report the discovery of dendrimer structures of crystalized urea in the stratum corneum of palmar glabrous skin using laser scanning microscopy. The chemical and structural nature of the urea crystallites was investigated in vivo by non-invasive techniques. The relation of crystallization to skin hydration was explored. We analysed the index finger, small finger and tenar palmar area of 18 study participants using non-invasive optical methods, such as laser scanning microscopy, Raman microspectroscopy and two-photon tomography. Skin hydration was measured using corneometry. Crystalline urea structures were found in the stratum corneum of about two-thirds of the participants. Participants with a higher density of crystallized urea structures exhibited a lower skin hydration. The chemical nature and the crystalline structure of the urea were confirmed by Raman microspectroscopy and by second harmonic generated signals in two-photon tomography. The presence of urea dendrimer crystals in the glabrous skin seems to reduce the water binding capacity leading to dry hands. These findings highlight a new direction in understanding the mechanisms leading to dry hands and open opportunities for the development of better moisturizers and hand disinfection products and for diagnostic of dry skin.
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.
Breast Cancer Stem Cell–Derived Tumors Escape from γδ T-cell Immunosurveillance In Vivo by Modulating γδ T-cell Ligands
(Philadelphia, Pa. : AACR, 2023) Raute, Katrin; Strietz, Juliane; Parigiani, Maria Alejandra; Andrieux, Geoffroy; Thomas, Oliver S.; Kistner, Klaus M.; Zintchenko, Marina; Aichele, Peter; Hofmann, Maike; Zhou, Houjiang; Weber, Wilfried; Boerries, Melanie; Swamy, Mahima; Maurer, Jochen; Minguet, Susana
There are no targeted therapies for patients with triple-negative breast cancer (TNBC). TNBC is enriched in breast cancer stem cells (BCSC), which play a key role in metastasis, chemoresistance, relapse, and mortality. γδ T cells hold great potential in immunotherapy against cancer and might provide an approach to therapeutically target TNBC. γδ T cells are commonly observed to infiltrate solid tumors and have an extensive repertoire of tumor-sensing mechanisms, recognizing stress-induced molecules and phosphoantigens (pAgs) on transformed cells. Herein, we show that patient-derived triple-negative BCSCs are efficiently recognized and killed by ex vivo expanded γδ T cells from healthy donors. Orthotopically xenografted BCSCs, however, were refractory to γ δ T-cell immunotherapy. We unraveled concerted differentiation and immune escape mechanisms: xenografted BCSCs lost stemness, expression of γ δ T-cell ligands, adhesion molecules, and pAgs, thereby evading immune recognition by γ δ T cells. Indeed, neither promigratory engineered γ δ T cells, nor anti–PD-1 checkpoint blockade, significantly prolonged overall survival of tumor-bearing mice. BCSC immune escape was independent of the immune pressure exerted by the γ δ T cells and could be pharmacologically reverted by zoledronate or IFNα treatment. These results pave the way for novel combinatorial immunotherapies for TNBC.
A high-throughput 3D kinetic killing assay
(Weinheim : Wiley-VCH, 2023) Zhao, Renping; Yanamandra, Archana K.; Qu, Bin
[No abstract available]
Dynamic fine-tuning of CAR-T cell therapy
(London : Nature Publishing Group, 2023) Trehin, Pierre V.M.; Muñoz-Guamuro, Geisler; Weber, Wilfried
[No abstract available]
Electroactive nanoinjection platform for intracellular delivery and gene silencing
(London : Biomed Central, 2023) Shokouhi, Ali-Reza; Chen, Yaping; Yoh, Hao Zhe; Murayama, Takahide; Suu, Koukou; Morikawa, Yasuhiro; Brenker, Jason; Alan, Tuncay; Voelcker, Nicolas H.; Elnathan, Roey
Background: Nanoinjection—the process of intracellular delivery using vertically configured nanostructures—is a physical route that efficiently negotiates the plasma membrane, with minimal perturbation and toxicity to the cells. Nanoinjection, as a physical membrane-disruption-mediated approach, overcomes challenges associated with conventional carrier-mediated approaches such as safety issues (with viral carriers), genotoxicity, limited packaging capacity, low levels of endosomal escape, and poor versatility for cell and cargo types. Yet, despite the implementation of nanoinjection tools and their assisted analogues in diverse cellular manipulations, there are still substantial challenges in harnessing these platforms to gain access into cell interiors with much greater precision without damaging the cell’s intricate structure. Here, we propose a non-viral, low-voltage, and reusable electroactive nanoinjection (ENI) platform based on vertically configured conductive nanotubes (NTs) that allows for rapid influx of targeted biomolecular cargos into the intracellular environment, and for successful gene silencing. The localization of electric fields at the tight interface between conductive NTs and the cell membrane drastically lowers the voltage required for cargo delivery into the cells, from kilovolts (for bulk electroporation) to only ≤ 10 V; this enhances the fine control over membrane disruption and mitigates the problem of high cell mortality experienced by conventional electroporation. Results: Through both theoretical simulations and experiments, we demonstrate the capability of the ENI platform to locally perforate GPE-86 mouse fibroblast cells and efficiently inject a diverse range of membrane-impermeable biomolecules with efficacy of 62.5% (antibody), 55.5% (mRNA), and 51.8% (plasmid DNA), with minimal impact on cells’ viability post nanoscale-EP (> 90%). We also show gene silencing through the delivery of siRNA that targets TRIOBP, yielding gene knockdown efficiency of 41.3%. Conclusions: We anticipate that our non-viral and low-voltage ENI platform is set to offer a new safe path to intracellular delivery with broader selection of cargo and cell types, and will open opportunities for advanced ex vivo cell engineering and gene silencing. Graphical abstract: [Figure not available: see fulltext.]
Werbung für den Apothekenbetrieb in örtlichen Festschriften
(Stuttgart : Deutscher Apotheker Verlag, 2004-09) Diebold, Steffen M.
Der vorliegende Beitrag widmet sich einem Aspekt der Sozial- und Kulturgeschichte, der bislang weder von der volkskundlichen noch von der sozialwissenschaftlichen Forschung aufgegriffen worden ist. Am Beispiel von Vereinsfestschriften des 20. Jahrhunderts wird demonstriert, wie sich Apotheken außerhalb von Fachkreisen der Öffentlichkeit präsentierten und dadurch für ihr Unternehmen warben. Über den alltagshistorischen Aspekt hinaus vermitteln die Annoncen der Festschriften en passant damit auch einen Einblick in wirtschaftliche, gesellschaftliche oder soziale Verhältnisse im Deutschland der Nachkriegszeit.
Ultrafast vibrational control of organohalide perovskite optoelectronic devices using vibrationally promoted electronic resonance
(Basingstoke : Nature Publishing Group, 2023) Gallop, Nathaniel. P.; Maslennikov, Dmitry R.; Mondal, Navendu; Goetz, Katelyn P.; Dai, Zhenbang; Schankler, Aaron M.; Sung, Woongmo; Nihonyanagi, Satoshi; Tahara, Tahei; Bodnarchuk, Maryna I.; Kovalenko, Maksym V.; Vaynzof, Yana; Rappe, Andrew M.; Bakulin, Artem A.
Vibrational control (VC) of photochemistry through the optical stimulation of structural dynamics is a nascent concept only recently demonstrated for model molecules in solution. Extending VC to state-of-the-art materials may lead to new applications and improved performance for optoelectronic devices. Metal halide perovskites are promising targets for VC due to their mechanical softness and the rich array of vibrational motions of both their inorganic and organic sublattices. Here, we demonstrate the ultrafast VC of FAPbBr3 perovskite solar cells via intramolecular vibrations of the formamidinium cation using spectroscopic techniques based on vibrationally promoted electronic resonance. The observed short (~300 fs) time window of VC highlights the fast dynamics of coupling between the cation and inorganic sublattice. First-principles modelling reveals that this coupling is mediated by hydrogen bonds that modulate both lead halide lattice and electronic states. Cation dynamics modulating this coupling may suppress non-radiative recombination in perovskites, leading to photovoltaics with reduced voltage losses.
Lebensqualität bei Krebserkrankungen – Integration in die Versorgung (working paper von Versorgern und Betroffenen)
(Hannover : Technische Informationsbibliothek, 2024) Beutter, C. N. L.; Block, N.; Berger, S.; Edo-Ferrando, P.; Heinz, B.; Läufer, K.; Lang, B.; Mächtlen, K.; Münkel, S.; Rannert, D.; Zwerenz-Kopp, F.; Fegeler, C.
Im Rahmen einer Projektgruppe haben sich sowohl Versorger (ÄrztInnen, KrebsberatungsstellenmitarbeiterInnen oder PsychoonkologInnen) sowie Betroffene mit einer regulären Integration von Lebensqualitätsdaten in der Versorgung befasst. Dabei wurde ein digitales System konzipiert, dass eine alltagsnahe und longitudinale Erhebung ermöglicht. Im working paper wurde über alle Teilnehmenden hinweg eine Problemidentifikation der derzeitigen IST-Situation integriert, um anhand dieser Probleme und Hemmschwellen ein übergreifendes Lösungskonzept zu erarbeiten. Im Lösungsraum wurden sowohl spezifische Anforderungen seitens der Versorger als auch PatientInnen zusammengefasst und gegenübergestellt. Dabei wurde ebenfalls die Vernetzung der einzelnen Akteure untereinander beleuchtet sowie die Thematik der Datenspende angerissen.

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