DNA nanostructures (DNs) have gained popularity in various biomedical applications due to their unique properties, including structural programmability, ease of synthesis and functionalization, and low cytotoxicity. Effective utilization of DNs in biomedical applications requires a fundamental understanding of their interactions with living cells and the mechanics of cellular uptake. Current knowledge primarily focuses on how the physicochemical properties of DNs, such as mass, shape, size, and surface functionalization, affect uptake efficacy. However, the role of cellular mechanics and morphology in DN uptake remains largely unexplored. In this work, we show that cells subjected to geometric constraints remodel their actin cytoskeleton, resulting in differential mechanical force generation that facilitates DN uptake. The length, number, and orientation of F-actin fibers are influenced by these constraints, leading to distinct mechanophenotypes. Overall, DN uptake is governed by F-actin forces arising from filament reorganisation under geometric constraints. These results underscore the importance of actin dynamics in the cellular uptake of DNs and suggest that leveraging geometric constraints to induce specific cell morphology adaptations could enhance the uptake of therapeutically designed DNs.
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http://dx.doi.org/10.1039/d5tb00074b | DOI Listing |
J Mater Chem B
January 2025
Department of Optical and Biophysical Systems, Institute of Physics of the Czech Academy of Sciences, Prague, 18200, Czech Republic.
DNA nanostructures (DNs) have gained popularity in various biomedical applications due to their unique properties, including structural programmability, ease of synthesis and functionalization, and low cytotoxicity. Effective utilization of DNs in biomedical applications requires a fundamental understanding of their interactions with living cells and the mechanics of cellular uptake. Current knowledge primarily focuses on how the physicochemical properties of DNs, such as mass, shape, size, and surface functionalization, affect uptake efficacy.
View Article and Find Full Text PDFMath Program
July 2024
Department of Mathematics and Computer Science, Philipps-Universität Marburg, 35032 Marburg, Germany.
As a starting point of our research, we show that, for a fixed order , each local minimizer of a rather general nonsmooth optimization problem in Euclidean spaces is either M-stationary in the classical sense (corresponding to stationarity of order 1), satisfies stationarity conditions in terms of a coderivative construction of order , or is asymptotically stationary with respect to a critical direction as well as order in a certain sense. By ruling out the latter case with a constraint qualification not stronger than directional metric subregularity, we end up with new necessary optimality conditions comprising a mixture of limiting variational tools of orders 1 and . These abstract findings are carved out for the broad class of geometric constraints and , and visualized by examples from complementarity-constrained and nonlinear semidefinite optimization.
View Article and Find Full Text PDFBiomater Adv
January 2025
School of Chemical Engineering, University of Birmingham, Edgbaston B15 2TT, United Kingdom. Electronic address:
Laser-powder bed fusion (PBF-LB) has enabled production of customised skeletal implants that incorporate porous lattices structures to enable bone ingrowth. However, the inherent surface roughness of PBF-LB, characterised by partially adhered particles and undulating sub-topography, remains a barrier to adoption. As such PBF-LB surfaces require several time-consuming post-processing steps, nevertheless, conventional finishing techniques are often limited by geometrical part complexity, making them unsuitable for porous PBF-LB parts.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, London, Surrey, SM2 5PT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
This study aims to develop and evaluate a fast and robust deep learningbased auto-segmentation approach for organs at risk in MRI-guided radiotherapy of pancreatic cancer to overcome the problems of time-intensive manual contouring in online adaptive workflows. The research focuses on implementing novel data augmentation techniques to address the challenges posed by limited datasets. Approach: This study was conducted in two phases.
View Article and Find Full Text PDFPNAS Nexus
January 2025
School of Physical Science and Engineering, Tongji University, Shanghai 200092, P. R. China.
Wiring patterns of brain networks embody a trade-off between information transmission, geometric constraints, and metabolic cost, all of which must be balanced to meet functional needs. Geometry and wiring economy are crucial in the development of brains, but their impact on artificial neural networks (ANNs) remains little understood. Here, we adopt a wiring cost-controlled training framework that simultaneously optimizes wiring efficiency and task performance during structural evolution of sparse ANNs whose nodes are located at arbitrary but fixed positions.
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