To assess the safety of engineered nanomaterials (ENMs) and to evaluate and improve ENMs' targeting ability for medical application, it is necessary to analyze the fate of these materials in biological media. This protocol presents a workflow that allows researchers to determine, characterize and quantify metal-bearing ENMs (M-ENMs) in biological tissues and cells and quantify their dynamic behavior at trace-level concentrations. Sample preparation methods to enable analysis of M-ENMs in a single cell, a cell layer, tissue, organ and physiological media (e.g., blood, gut content, hemolymph) of different (micro)organisms, e.g., bacteria, animals and plants are presented. The samples are then evaluated using fit-for-purpose analytical techniques e.g., single-cell inductively coupled plasma mass spectrometry, single-particle inductively coupled plasma mass spectrometry and synchrotron X-ray absorption fine structure, providing a protocol that allows comprehensive characterization and quantification of M-ENMs in biological matrices. Unlike previous methods, the protocol uses no fluorescent dyes or radiolabels to trace M-ENMs in biota and enables analysis of most M-ENMs at cellular, tissue and organism levels. The protocols can be applied by a wide variety of users depending on the intended purpose of the application, e.g., to correlate toxicity with a specific particle form, or to understand the absorption, distribution and excretion of M-ENMs. The results facilitate an understanding of the biological fate of M-ENMs and their dynamic behavior in biota. Performing the protocol may take 7-30 d, depending on which combination of methods is applied.
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http://dx.doi.org/10.1038/s41596-022-00701-x | DOI Listing |
S-glutathionylation (SSG) is increasingly recognized as a critical signaling mechanism in the heart, yet SSG modifications in cardiac sarcomeric proteins remain understudied. Here we identified SSG of the ventricular isoform of myosin light chain 1 (MLC-1v) in human, swine, and mouse cardiac tissues using top-down mass spectrometry (MS)-based proteomics. Our results enabled the accurate identification, quantification, and site-specific localization of SSG in MLC-1v across different species.
View Article and Find Full Text PDFFront Pharmacol
December 2024
Qinghai-Tibetan Plateau Ethnic Medicinal Resources Protection and Utilization Key Laboratory of National Ethnic Affairs Commission of the People's Republic of China, Southwest Minzu University, Chengdu, China.
Background: Regel (MQ) is a traditional Chinese medicine (TCM) used for clearing liver and lung heat in Tibetan medicine for over a thousand years. However, the lack of quality markers that correlate with pharmacological actions and absorption limits the safety and efficacy in its clinical application and on the drug market. Furthermore, a universal and rapid method for simultaneously determining quality markers in the MQ crude drug is still lacking.
View Article and Find Full Text PDFPhys Chem Chem Phys
December 2024
Department of Chemistry and Center for Materials Science (WZMW), University of Marburg, Hans-Meerwein-Straße 4, 35032 Marburg, Germany.
Highly concentrated salt solutions are promising electrolytes for battery applications due to their low flammability, their high thermal stability, and their good compatibility with electrode materials. Understanding transport processes in highly concentrated electrolytes is a challenging task, since strong ion-ion and ion-solvent interactions lead to highly correlated movements on the microscopic scale. Here, we use an experimental overdetermination method to obtain accurate Onsager transport coefficients for concentrated binary electrolytes composed of either sulfolane (SL) or dimethyl carbonate (DMC) as solvent and either LiTFSI or LiFSI as salt.
View Article and Find Full Text PDFSmall
December 2024
UMR CNRS 5629, laboratoire LCPO, Université de Bordeaux, Pessac, F-33600, France.
This work addresses the structural quantification of multiphase materials, here nanostructured polymer solid precursors and their micro/nano sized foamed counterparts. It is based on a strategy of contrast/edge enhancement, locally adaptive to image data in digital images of materials. The method allows to binarize straightforwardly the structures (the phases) in TEM and SEM images after edge identification, edge choice, and image virtual reconstruction.
View Article and Find Full Text PDFMagn Reson Imaging
December 2024
Weill Cornell Graduate School of Medical Sciences, New York, NY, United States; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States; Department of Radiology, Memorial Sloan Kettering Cancer Center, NY, New York, USA.
Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption of quantitative DCE-MRI remains limited due to challenges in acquisition and quantification performance, and lack of automated tools. This study presents an end-to-end deep learning pipeline that exploits a novel deep reconstruction network called DCE-Movienet with a previously developed deep quantification network called DCE-Qnet for fast and quantitative DCE-MRI.
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