Thematische Bibliographien / Label free identification

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Auswahl der wissenschaftlichen Literatur zum Thema „Label free identification“

Autor: Grafiati

Veröffentlicht am 29. März 2025

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Zeitschriftenartikel zum Thema "Label free identification"

Li, Jing, Hua Xu, Graham M. West und Lyn H. Jones. „Label-free technologies for target identification and validation“. MedChemComm 7, Nr. 5 (2016): 769–77. http://dx.doi.org/10.1039/c6md00045b.

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Chemical probes have been instrumental in revealing new targets and confirming target engagement. However, substantial effort and resources are required to design and synthesize these probes. In contrast, label-free technologies have the advantage of bypassing the need for chemical probes. Here we highlight the recent developments in label-free methods and discuss the pros and cons of each approach.

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Nickelsen, Anna, und Joachim Jose. „Label-free flow cytometry-based enzyme inhibitor identification“. Analytica Chimica Acta 1179 (September 2021): 338826. http://dx.doi.org/10.1016/j.aca.2021.338826.

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Lloyd, William R., Shailesh Agarwal, Sagar U. Nigwekar, Karen Esmonde-White, Shawn Loder, Shawn Fagan, Jeremy Goverman et al. „Raman spectroscopy for label-free identification of calciphylaxis“. Journal of Biomedical Optics 20, Nr. 8 (11.08.2015): 080501. http://dx.doi.org/10.1117/1.jbo.20.8.080501.

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Li, Huafeng, Qingsong Hu und Zhanxuan Hu. „Catalyst for Clustering-Based Unsupervised Object Re-identification: Feature Calibration“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 4 (24.03.2024): 3091–99. http://dx.doi.org/10.1609/aaai.v38i4.28092.

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Clustering-based methods are emerging as a ubiquitous technology in unsupervised object Re-Identification (ReID), which alternate between pseudo-label generation and representation learning. Recent advances in this field mainly fall into two groups: pseudo-label correction and robust representation learning. Differently, in this work, we improve unsupervised object ReID from feature calibration, a completely different but complementary insight from the current approaches. Specifically, we propose to insert a conceptually simple yet empirically powerful Feature Calibration Module (FCM) before pseudo-label generation. In practice, FCM calibrates the features using a nonparametric graph attention network, enforcing similar instances to move together in the feature space while allowing dissimilar instances to separate. As a result, we can generate more reliable pseudo-labels using the calibrated features and further improve subsequent representation learning. FCM is simple, effective, parameter-free, training-free, plug-and-play, and can be considered as a catalyst, increasing the ’chemical reaction’ between pseudo-label generation and representation learning. Moreover, it maintains the efficiency of testing time with negligible impact on training time. In this paper, we insert FCM into a simple baseline. Experiments across different scenarios and benchmarks show that FCM consistently improves the baseline (e.g., 8.2% mAP gain on MSMT17), and achieves the new state-of-the-art results. Code is available at: https://github.com/lhf12278/FCM-ReID.

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Choi, Junseo, Zheng Jia, Ramin Riahipour, Collin J. McKinney, Charuni A. Amarasekara, Kumuditha M. Weerakoon‐Ratnayake, Steven A. Soper und Sunggook Park. „Label‐Free Identification of Single Mononucleotides by Nanoscale Electrophoresis“. Small 17, Nr. 42 (23.09.2021): 2102567. http://dx.doi.org/10.1002/smll.202102567.

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Dannhauser, David, Paolo Antonio Netti und Filippo Causa. „Label-free scattering snapshot classification for living cell identification“. EPJ Web of Conferences 309 (2024): 10021. http://dx.doi.org/10.1051/epjconf/202430910021.

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A scattering snapshot hold an enormous potential for cell class and state classification, allowing to avoid costly fluorescence labelling. Beside convolutional neural networks show outstanding image classification performance compared to other state-of-the-art methods, regarding accuracy and speed. Therefore, we combined the two techniques (Light Scattering and Deep Learning) to identify living cells with high precision. Neural Networks show high prediction performance for known classes but struggles when unknown classes need to be identified. In such a scenario no prior knowledge of the unknown cell class can be used for the model training, which inevitably results in a misclassification. To overcome the hurdle, of identifying unknown cell classes, we must first define an in-distribution of known snapshots to afterwards detect out of distribution snapshots as unknowns. Ones, such a new cell class is identified, we can retrain our cell classifier with the obtained knowledge, so we dynamically update the cell class database. We applied this measurement approach to scattering pattern snapshots of different classes of living cells. Our outcome shows a precise cell classification, which can be applied to a wide range of single cell classification approaches.

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Paidi, Santosh Kumar, Soumik Siddhanta, Robert Strouse, James B. McGivney, Christopher Larkin und Ishan Barman. „Rapid Identification of Biotherapeutics with Label-Free Raman Spectroscopy“. Analytical Chemistry 88, Nr. 8 (08.04.2016): 4361–68. http://dx.doi.org/10.1021/acs.analchem.5b04794.

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Faria, Henrique Antonio Mendonça, und Valtencir Zucolotto. „Label-free electrochemical DNA biosensor for zika virus identification“. Biosensors and Bioelectronics 131 (April 2019): 149–55. http://dx.doi.org/10.1016/j.bios.2019.02.018.

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Bae, Euiwon, Nan Bai, Amornrat Aroonnual, Arun K. Bhunia und E. Daniel Hirleman. „Label-free identification of bacterial microcolonies via elastic scattering“. Biotechnology and Bioengineering 108, Nr. 3 (10.11.2010): 637–44. http://dx.doi.org/10.1002/bit.22980.

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Mehr Quellen

Dissertationen zum Thema "Label free identification"

Wang, Yunmiao. „Microgap Structured Optical Sensor for Fast Label-free DNA Detection“. Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/32875.

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DNA detection technology has developed rapidly due to its extensive application in clinical diagnostics, bioengineering, environmental monitoring, and food science areas. Currently developed methods such as surface Plasmon resonance (SPR) methods, fluorescent dye labeled methods and electrochemical methods, usually have the problems of bulky size, high equipment cost and time-consuming algorithms, so limiting their application for in vivo detection. In this work, an intrinsic Fabry-Perot interferometric (IFPI) based DNA sensor is presented with the intrinsic advantages of small size, low cost and corrosion-tolerance. This sensor has experimentally demonstrated its high sensitivity and selectivity. In theory, DNA detection is realized by interrogating the sensorâ s optical cavity length variation resulting from hybridization event. First, a microgap structure based IFPI sensor is fabricated with simple etching and splicing technology. Subsequently, considering the sugar phosphate backbone of DNA, layer-by-layer electrostatic self-assembly technique is adopted to attach the single strand capture DNA to the sensor endface. When the target DNA strand binds to the single-stranded DNA successfully, the optical cavity length of sensor will be increased. Finally, by demodulating the sensor spectrum, DNA hybridization event can be judged qualitatively. This sensor can realize DNA detection without attached label, which save the experiment expense and time. Also the hybridization detection is finished within a few minutes. This quick response feature makes it more attractive in diagnose application. Since the sensitivity and specificity are the most widely used statistics to describe a diagnostic test, so these characteristics are used to evaluate this biosensor. Experimental results demonstrate that this sensor has a sensitivity of 6nmol/ml and can identify a 2 bp mismatch. Since this sensor is optical fiber based, it has robust structure and small size ( 125Î¼m ). If extra etching process is applied to the sensor, the size can be further reduced. This promises the sensor potential application of in-cell detection. Further investigation can be focused on the nanofabrication of this DNA sensor, and this is very meaningful topic not only for diagnostic test but also in many other applications such as food industry, environment monitoring.
Master of Science

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Mohammed, Kader Hamno. „Development of a label-free biosensor method for the identification of sticky compounds which disturb GPCR-assays“. Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-220645.

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It is widely known that early estimates about the binding properties of drug candidates are important in the drug discovery process. Surface plasmon resonance (SPR) biosensors have become a standard tool for characterizing interactions between a great variety of biomolecules and it offers a unique opportunity to study binding activity. The aim of this project was to develop a SPR based assay for pre-screening of low molecular weight (LMW) drug compounds, to enable filtering away disturbing compounds when interacting with drugs. The interaction between 47 LMW compounds and biological ligands were investigated using the instrument BiacoreTM, which is based on SPR-technology.

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Hughes, Juanita Maree. „A novel identification method for ultra trace detection of biomolecules using functionalised Surface Enhanced Raman Spectroscopy (SERS)“. Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/72864/2/Juanita_Hughes_Thesis.pdf.

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This thesis developed a new method for measuring extremely low amounts of organic and biological molecules, using Surface enhanced Raman Spectroscopy. This method has many potential applications, e.g. medical diagnosis, public health, food provenance, antidoping, forensics and homeland security. The method development used caffeine as the small molecule example, and erythropoietin (EPO) as the large molecule. This method is much more sensitive and specific than currently used methods; rapid, simple and cost effective. The method can be used to detect target molecules in beverages and biological fluids without the usual preparation steps.

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Jemfer, Charlotte. „Couplage SdFFF et UHF-DEP : Technologie innovante d'isolement et de caractérisation des CSC appliquée au diagnostic et à la thérapie du cancer colorectal“. Electronic Thesis or Diss., Limoges, 2024. http://www.theses.fr/2024LIMO0112.

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Les cellules souches cancéreuses (CSC) jouent un rôle central dans l’hétérogénéité cellulaire et la progression tumorale dans le cancer colorectal (CCR). Cependant, leur isolement est un défi en raison des méthodes classiques, basées sur le marquage fluorescent ou magnétique. Ces méthodes restent incertaines en raison de la plasticité des CSC, limitant ainsi leur utilité clinique. Dans cette étude, nous proposons un couplage innovant entre de tri cellulaire de fractionnement par couplage flux-force de sédimentation (SdFFF) et la méthode de détection par biocapteur à ultra-haute fréquence (UHF-DEP), toutes deux méthodes sans marquage. Cette approche a déjà montré son efficacité dans le cadre du glioblastome, et notre objectif est alors de démontrer son caractère universel et son application à d'autre types de cancer tel que le CCR. Ce couplage nécessite une adaptation instrumentale et méthodologique à la phase mobile des deux technologies, l'analyse fonctionnelle et phénotypique ainsi que pour la première fois une analyse transcriptomique à révéler que la SdFFF était capable d’isolée une sous population enrichie en CSC. Ces caractéristiques sont corrélées à des signatures électromagnétiques (SEM) spécifiques obtenue par le biocapteur UHF-DEP, démontrant ainsi l’efficacité du couplage SdFFF/UHF-DEP pour l'isolement et la caractérisation des CSC dans le CCR. Ces signatures étant corrélées non seulement à l'état de souchitude des populations, mais aussi à l'évolution des propriétés membranaires comme cela a été révélé par l'analyse transcriptomique.Pour approfondir l'intérêt de ce couplage, nous avons exploré son potentiel analyser les effets du 5-fluorouracile (5-FU, chimiothérapie clé dans le traitement du CCR), sur les sous-populations isolées. Ainsi nous avons comparé les SEM, et l’analyse transcriptomique de ces sous populations de CSC, dans le but d’identifier les modifications induites, ouvrant des possibilités d’applications en diagnostic et en suivi thérapeutique. Enfin, l'analyse de la SEM et du RNA-Seq d'une population cellulaire hétérogène traitée au 5-FU, triée puis caractérisée, a permis d'évaluer la capacité du couplage à identifier les cellules souches cancéreuses (CSC) résiduelles après traitement. Les résultats suggèrent une réduction de la population de CSC après traitement, soulignant le potentiel de cette approche pour évaluer l’efficacité thérapeutique et les changements induits par la chimiothérapie sur les CSC.Ces travaux démontrent le potentiel du couplage SdFFF/UHF-DEP en tant qu’outil de diagnostic et de personnalisation des traitements en oncologie, offrant des perspectives prometteuses pour une évaluation plus précise de la réponse thérapeutique et une optimisation des stratégies de traitement en fonction du profil cellulaire
Cancer stem cells (CSCs) play a central role in cellular heterogeneity and tumour progression in colorectal cancer (CRC). However, their isolation is a challenge using conventional methods based on fluorescent or magnetic labelling. These methods remain uncertain due to the plasticity of CSCs, thus limiting their clinical usefulness. In this study, we propose an innovative coupling between cell sorting fractionation by sedimentation flow-force coupling (SdFFF) and the ultra-high frequency biosensor detection method (UHF-DEP), both label-free methods. This approach has already demonstrated its effectiveness in glioblastoma, and our aim is to demonstrate its universality and its application to other types of cancer such as CRC. This coupling requires instrumental and methodological adaptation to the mobile phase of the two technologies. Functional and phenotypic analysis and, for the first time, transcriptomic analysis revealed that SdFFF was capable of isolating a CSC-enriched subpopulation. These characteristics are correlated with specific electromagnetic signatures (SEM) obtained by the UHF-DEP biosensor, thus demonstrating the effectiveness of the SdFFF/UHF-DEP coupling for the isolation and characterisation of CSCs in the CRC. These signatures correlate not only with the strain status of the populations, but also with changes in membrane properties, as revealed by transcriptomic analysis.To further explore the interest of this coupling, we explored its potential to analyse the effects of 5-fluorouracil (5-FU, a key chemotherapy in the treatment of CRC) on isolated sub-populations. We compared the SEM and transcriptomic analysis of these CSC sub-populations, with the aim of identifying the changes induced, opening up potential applications in diagnosis and therapeutic monitoring. Finally, SEM and RNA-Seq analysis of a heterogeneous cell population treated with 5-FU, sorted and then characterised, made it possible to assess the coupling's ability to identify residual cancer stem cells (CSCs) after treatment. The results suggest a reduction in the CSC population after treatment, underlining the potential of this approach for assessing therapeutic efficacy and the changes induced by chemotherapy on CSCs. This work demonstrates the potential of SdFFF/UHF-DEP coupling as a diagnostic and treatment personalisation tool in oncology, offering promising prospects for more accurate assessment of therapeutic response and optimisation of treatment strategies according to cell profile

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Chan, Janet Nga Yung. „A label- and immobilization-free proteomic approach for identification of targets of drugs“. 2009. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=958044&T=F.

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Wen-ChangTzeng und 曾文璋. „Identification of metastasis related phosphotyrosine proteins in response to tyrosine kinase inhibitor treatment in human lung cancer cells using label-free quantitative analysis“. Thesis, 2011. http://ndltd.ncl.edu.tw/handle/53742724473299540703.

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Seibert, C., B. R. Davidson, B. J. Fuller, Laurence H. Patterson, W. J. Griffiths und Y. Wang. „Multiple-approaches to the identification and quantification of cytochromes P450 in human liver tissue by mass spectrometry“. 2009. http://hdl.handle.net/10454/6179.

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Here we report the identification and approximate quantification of cytochrome P450 (CYP) proteins in human liver microsomes as determined by nano-LC-MS/MS with application of the exponentially modified protein abundance index (emPAI) algorithm during database searching. Protocols based on 1D-gel protein separation and 2D-LC peptide separation gave comparable results. In total, 18 CYP isoforms were unambiguously identified based on unique peptide matches. Further, we have determined the absolute quantity of two CYP enzymes (2E1 and 1A2) in human liver microsomes using stable-isotope dilution mass spectrometry, where microsomal proteins were separated by 1D-gel electrophoresis, digested with trypsin in the presence of either a CYP2E1- or 1A2-specific stable-isotope labeled tryptic peptide and analyzed by LC-MS/MS. Using multiple reaction monitoring (MRM) for the isotope-labeled tryptic peptides and their natural unlabeled analogues quantification could be performed over the range of 0.1-1.5 pmol on column. Liver microsomes from four individuals were analyzed for CYP2E1 giving values of 88-200 pmol/mg microsomal protein. The CYP1A2 content of microsomes from a further three individuals ranged from 165 to 263 pmol/mg microsomal protein. Although, in this proof-of-concept study for CYP quantification, the two CYP isoforms were quantified from different samples, there are no practical reasons to prevent multiplexing the method to allow the quantification of multiple CYP isoforms in a single sample.

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Buchteile zum Thema "Label free identification"

Hendriks, Ivo A., und Alfred C. O. Vertegaal. „Label-Free Identification and Quantification of SUMO Target Proteins“. In Methods in Molecular Biology, 171–93. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6358-4_13.

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Higgs, Richard E., Michael D. Knierman, Valentina Gelfanova, Jon P. Butler und John E. Hale. „Label-Free LC-MS Method for the Identification of Biomarkers“. In Methods in Molecular Biology™, 209–30. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-117-8_12.

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Liang, Xinmiao, Jixia Wang, Xiuli Zhang und Ye Fang. „Label-Free Cell Phenotypic Identification of Active Compounds in Traditional Chinese Medicines“. In Methods in Pharmacology and Toxicology, 233–52. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2617-6_13.

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Hu, Heidi, Huayun Deng und Ye Fang. „Label-Free Cell Phenotypic Identification of d-Luciferin as an Agonist for GPR35“. In Bioluminescence, 3–17. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3813-1_1.

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Li, Shalan, Haitao Zan, Zhe Zhu, Dandan Lu und Leonard Krall. „Plant Phosphopeptide Identification and Label-Free Quantification by MaxQuant and Proteome Discovery Software“. In Plant Phosphoproteomics, 179–87. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1625-3_13.

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Sharma, Kanika, Prashant Kaushal und Vikas Kumar. „Proteomic Identification and Label-Free Quantification of Proteins Implicated in Neurite and Spine Formation“. In Methods in Molecular Biology, 133–43. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3969-6_10.

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Holtz, Anja, Nathan Basisty und Birgit Schilling. „Quantification and Identification of Post-Translational Modifications Using Modern Proteomics“. In Methods in Molecular Biology, 225–35. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1024-4_16.

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AbstractPost-translational modifications (PTMs) occur dynamically, allowing cells to quickly respond to changes in the environment. Lysine residues can be targeted by several modifications including acylations (acetylation, succinylation, malonylation, glutarylation, and others), methylation, ubiquitination, and other modifications. One of the most efficient methods for the identification of post-translational modifications is utilizing immunoaffinity enrichment followed by high-resolution mass spectrometry. This workflow can be coupled with comprehensive data-independent acquisition (DIA) mass spectrometry to be a high-throughput, label-free PTM quantification approach. Below we describe a detailed protocol to process tissue by homogenization and proteolytically digest proteins, followed by immunoaffinity enrichment of lysine-acetylated peptides to identify and quantify relative changes of acetylation comparing different conditions.

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Li, Shalan, Haitao Zan, Zhe Zhu, Dandan Lu und Leonard Krall. „Correction to: Plant Phosphopeptide Identification and Label-Free Quantification by MaxQuant and Proteome Discoverer Software“. In Plant Phosphoproteomics, C1. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1625-3_18.

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Vis, Bradley, Jonathan J. Powell und Rachel E. Hewitt. „Label-Free Identification of Persistent Particles in Association with Primary Immune Cells by Imaging Flow Cytometry“. In Methods in Molecular Biology, 135–48. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3020-4_8.

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Née, Guillaume, Priyadarshini Tilak und Iris Finkemeier. „A Versatile Workflow for the Identification of Protein–Protein Interactions Using GFP-Trap Beads and Mass Spectrometry-Based Label-Free Quantification“. In Methods in Molecular Biology, 257–71. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0528-8_19.

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Konferenzberichte zum Thema "Label free identification"

Pirone, Daniele, Beatrice Cavina, Martina Mugnano, Vittorio Bianco, Lisa Miccio, Anna Myriam Perrone, Anna Maria Porcelli et al. „Label-free identification of T-lymphocytes in holographic microscopy empowered by machine learning“. In Digital Holography and Three-Dimensional Imaging, W4A.15. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/dh.2024.w4a.15.

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The precise count of T-lymphocytes is a challenging topic since whose number is demonstrated to correlate to disease severity. Here we report a method for label-free identification of T-lymphocytes through holographic microscopy and machine learning.

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Yang, Bin, Jianyu Ren und Wei Xiong. „Label-free identification of tumor tissues by coherent nonlinear vibrational mode imaging“. In Ultrafast Nonlinear Imaging and Spectroscopy XII, herausgegeben von Zhiwen Liu, Demetri Psaltis und Kebin Shi, 16. SPIE, 2024. http://dx.doi.org/10.1117/12.3027676.

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Eltigani, Faihaa Mohammed, Nebras Ahmed Mohamed und Xuantao Su. „Light scattering imaging combined with machine learning for label-free identification of live yeast cells“. In Third Conference on Biomedical Photonics and Cross-Fusion (BPC 2024), herausgegeben von Zhenxi Zhang, Junle Qu und Buhong Li, 19. SPIE, 2024. http://dx.doi.org/10.1117/12.3039875.

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Hahm, Tae-Hun, Kristine Glunde und Alison Scott. „FluoMALDI imaging of the immune response for label-free in situ identification of phagocytes in Francisella novicida-infected mouse tissues“. In Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXIII, herausgegeben von Attila Tarnok, Jessica P. Houston und Xuantao Su, 19. SPIE, 2025. https://doi.org/10.1117/12.3041947.

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Bélanger, Erik, Gabrielle Jess, Jean-Honoré Laurent, Corentin Soubeiran, Céline Larivière-Loiselle, Sara Mattar, Niraj Patel et al. „Progress and advances in the development of a label-free optofluidic platform based on quantitative phase digital holographic microscopy and microfluidics for the identification of human disease-specific cell phenotypes (Conference Presentation)“. In Microfluidics, BioMEMS, and Medical Microsystems XXIII, herausgegeben von Bastian E. Rapp und Colin Dalton, 11. SPIE, 2025. https://doi.org/10.1117/12.3042226.

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Gesley, Mark A., Robert Goldsby, Stephen M. Lane und Romin Puri. „Spectral image microscopy for label-free blood and cancer cell identification“. In Label-free Biomedical Imaging and Sensing (LBIS) 2019, herausgegeben von Natan T. Shaked und Oliver Hayden. SPIE, 2019. http://dx.doi.org/10.1117/12.2507474.

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Pourrahimi, Monireh, Samaneh Ghazanfarpour, Alireza Sheikhsofla, Rafael Pena, Jennifer Morrissey, Sujith Chander Reddy Kollampally, Anna Sharikova, Yubing Xie, Melinda Larsen und Alexander Khmaladze. „Detection and identification of alginate in tissue-freezing media samples using Raman spectroscopy“. In Label-free Biomedical Imaging and Sensing (LBIS) 2024, herausgegeben von Natan T. Shaked und Oliver Hayden. SPIE, 2024. http://dx.doi.org/10.1117/12.3003876.

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Le Galudec, Joel, Mathieu Dupoy und Pierre Marcoux. „Multispectral lensless imaging in the mid-infrared for label-free identification of Staphylococcus species“. In Label-free Biomedical Imaging and Sensing (LBIS) 2021, herausgegeben von Natan T. Shaked und Oliver Hayden. SPIE, 2021. http://dx.doi.org/10.1117/12.2578264.

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Bruno, Giulia, Koseki J. Kobayashi-Kirschvink, Michal Lipinski, Christian Tentellino, Peter T. C. So, Paola Arlotta, Jeon Woong Kang und Francesco De Angelis. „Label-free identification of biochemical variations in brain organoid maturation stages through Raman spectroscopy“. In Label-free Biomedical Imaging and Sensing (LBIS) 2024, herausgegeben von Natan T. Shaked und Oliver Hayden. SPIE, 2024. http://dx.doi.org/10.1117/12.3001590.

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Marzi, Anne, Ilona Nordhorn, Kai Eder, Martin Wiemann, Uwe Karst, Björn Kemper und Jürgen Schnekenburger. „Label-free identification and quantification of nanoparticles in single cells by combining digital holographic microscopy and mass spectrometry“. In Label-free Biomedical Imaging and Sensing (LBIS) 2022, herausgegeben von Natan T. Shaked und Oliver Hayden. SPIE, 2022. http://dx.doi.org/10.1117/12.2609700.

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