The escalating number of new therapeutic biopharmaceuticals being developed and their high value increases the need for the development of novel analytical technologies. Faster analysis time, high accuracy, low sample consumption and the ability to monitor process flow are all essential prerequisites. We evaluate a novel analytical instrument that combines UV area imaging and Taylor dispersion analysis (TDA) to determine the hydrodynamic radius of proteins and small molecules in solution. Benchmarking the results against dynamic light scattering, we report the influence of injection system, injection volume, flow rates, analyte concentration and highlight the importance of washing procedures. Issues arising from the manual injection valve in the alpha laboratory system that led to high standard deviations were eliminated by incorporating an automated injector in a beta system. The hydrodynamic radii obtained show good correlation with literature values and in most cases a relative standard deviation of less than 5%. The system is fully automated after coupling to the CE which allows for multiple injections and sample/buffer changes without operator intervention. The small sample size (approx. 60 nL), the lack of sample preparation required, and the speed of analysis (approx. 2-3 mins) makes this instrument highly applicable to the real-time analysis of inherently unstable, high cost biopharmaceutical materials where understanding their aggregation state and size is important.
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http://dx.doi.org/10.1016/j.ijpharm.2011.03.040 | DOI Listing |
Transl Cancer Res
December 2024
School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Background: Regulatory T cells (Tregs) play a pivotal role in the development, prognosis, and treatment of breast cancer. This study aimed to develop a Treg-associated gene signature that contributes to predict prognosis and therapy benefits in breast cancer.
Methods: Treg-associated genes were screened based on single-cell RNA-sequencing (RNA-seq) in TISCH2 database and the bulk RNA-seq in The Cancer Genome Atlas (TCGA) database.
Environ Int
January 2025
Department of Physics, University of Girona, Campus de Montilivi, Girona 17003 Spain.
Using lock-exchange experiments, this study investigates the transport and sedimentation of microplastics (MPs) via turbidity currents. Two hypotheses were tested: MP sedimentation is influenced by suspended sediment concentration and grain size. Utilizing flows with different sediment concentrations and grain sizes in combination with three different MPs (PET fibers, melamine, and PVC fragments), the experiments revealed distinct sedimentation patterns: higher sediment concentrations enhance MP transport, and turbidity currents with finer sediments transported MPs over greater distances, highlighting the importance of sediment characteristics to predict MP distribution by such flows.
View Article and Find Full Text PDFActa Neurochir (Wien)
January 2025
Department of Neurosurgery, College of Medicine, University of Michigan, Ann Arbor, MI, USA.
Background: Wall shear stress (WSS) plays a crucial role in the natural history of intracranial aneurysms (IA). However, spatial variations among WSS have rarely been utilized to correlate with IAs' natural history. This study aims to establish the feasibility of using spatial patterns of WSS data to predict IAs' rupture status (i.
View Article and Find Full Text PDFChempluschem
January 2025
Kaiserslautern University of Technology: Rheinland-Pfalzische Technische Universitat Kaiserslautern-Landau, Chemistry, 67663, Kaiserslautern, GERMANY.
We report the synthesis of a series of detergents with a lactobionamide polar head group and a tail containing four to seven perfluorinated carbon atoms. Critical micellar concentrations (CMCs) were determined using isothermal titration calorimetry (ITC) and surface tension (SFT) measurements, showing a progressive decrease from 27 mM to about 0.2 mM across the series.
View Article and Find Full Text PDFPart Fibre Toxicol
January 2025
State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Suzhou Medical School, Soochow University, Suzhou, Jiangsu, 215123, China.
Background: The advancement of nanotechnology underscores the imperative need for establishing in silico predictive models to assess safety, particularly in the context of chronic respiratory afflictions such as lung fibrosis, a pathogenic transformation that is irreversible. While the compilation of predictive descriptors is pivotal for in silico model development, key features specifically tailored for predicting lung fibrosis remain elusive. This study aimed to uncover the essential predictive descriptors governing nanoparticle-induced pulmonary fibrosis.
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