A model-based rational strategy for the selection of chromatographic resins is presented. The main question being addressed is that of selecting the most optimal chromatographic resin from a few promising alternatives. The methodology starts with chromatographic modeling,parameters acquisition, and model validation, followed by model-based optimization of the chromatographic separation for the resins of interest. Finally, the resins are rationally evaluated based on their optimized operating conditions and performance metrics such as product purity, yield, concentration, throughput, productivity, and cost. Resin evaluation proceeds by two main approaches. In the first approach, Pareto frontiers from multi-objective optimization of conflicting objectives are overlaid for different resins, enabling direct visualization and comparison of resin performances based on the feasible solution space. The second approach involves the transformation of the resin performances into weighted resin scores, enabling the simultaneous consideration of multiple performance metrics and the setting of priorities. The proposed model-based resin selection strategy was illustrated by evaluating three mixed mode adsorbents (ADH, PPA, and HEA) for the separation of a ternary mixture of bovine serum albumin, ovalbumin, and amyloglucosidase. In order of decreasing weighted resin score or performance, the top three resins for this separation were ADH [PPA[HEA. The proposed model-based approach could be a suitable alternative to column scouting during process development, the main strengths being that minimal experimentation is required and resins are evaluated under their ideal working conditions, enabling a fair comparison. This work also demonstrates the application of column modeling and optimization to mixed mode chromatography.
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http://dx.doi.org/10.1002/btpr.691 | DOI Listing |
Transl Lung Cancer Res
December 2024
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: Visceral pleural invasion (VPI) is associated with a poor outcome in early-stage non-small cell lung cancer (NSCLC). Preoperative prediction of VPI could have an impact on surgical planning. The aim of this study was to establish a nomogram model based on computed tomography (CT) features to predict VPI in early-stage NSCLC.
View Article and Find Full Text PDFCommun Phys
December 2024
Institut für Theoretische Physik, TU Wien, Wiedner Hauptstraße 8-10, A-1040 Wien, Austria.
Despite the intrinsic charge heterogeneity of proteins plays a crucial role in the liquid-liquid phase separation (LLPS) of a broad variety of protein systems, our understanding of the effects of their electrostatic anisotropy is still in its early stages. We approach this issue by means of a coarse-grained model based on a robust mean-field description that extends the DLVO theory to non-uniformly charged particles. We numerically investigate the effect of surface charge patchiness and net particle charge on varying these features independently and with the use of a few parameters only.
View Article and Find Full Text PDFNanoscale
January 2025
Department of Physics, McGill University, 845 Sherbrooke West, Montréal, Canada.
Solid-state nanopores exhibit dynamically variable sizes influenced by buffer conditions and applied electric field. While dynamical pore behavior can complicate biomolecular sensing, it also offers opportunities for controlled, modification of pore size post-fabrication. In order to optimally harness solid-state pore dynamics for controlled growth, there is a need to systematically quantify pore growth dynamics and ideally develop quantitative models to describe pore growth.
View Article and Find Full Text PDFCommun Psychol
January 2025
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
How do people model the world's dynamics to guide mental simulation and evaluate choices? One prominent approach, the Successor Representation (SR), takes advantage of temporal abstraction of future states: by aggregating trajectory predictions over multiple timesteps, the brain can avoid the costs of iterative, multi-step mental simulation. Human behavior broadly shows signatures of such temporal abstraction, but finer-grained characterization of individuals' strategies and their dynamic adjustment remains an open question. We developed a task to measure SR usage during dynamic, trial-by-trial learning.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russian Federation; Frank Laboratory of Neutron Physics, Joint Institute for Nuclear Research, Dubna, 141980, Russian Federation. Electronic address:
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