Polyplexes of short DNA-fragments (300 b.p., 100 nm) with tailor-made amine-based polycations of different architectures (linear and hyperbranched) were investigated in buffer solution as a function of the mixing ratio with DNA. The resulting dispersed polyplexes were characterized using small-angle neutron and X-ray scattering (SANS, SAXS) as well as cryo-TEM with respect to their mesoscopic structure and their colloidal stability. The linear polyimines form rather compact structures that have a high tendency for precipitation. In contrast, the hyperbranched polycation with enzymatic-labile pentaethylenehexamine arms (PEHA) yields polyplexes colloidally stable for months. Here the polycation coating of DNA results in a homogeneous dispersion based on a fractal network with low structural organization at low polycation amount. With increasing polycation, bundles of tens of aligned DNA rods appear that are interconnected in a fractal network with a typical correlation distance on the order of 100 nm, the average length of the DNA used. With higher organization comes a decrease in stability. The 3D network built by these beams can still exhibit some stability as long as the material concentration is large enough, but the structure collapses upon dilution. SAXS shows that the complexation does not affect the local DNA structure. Interestingly, the structural findings on the DNA polyplexes apparently correlate with the transfection efficiency of corresponding siRNA complexes. In general, these finding not only show systematic trends for the colloid stability, but may allow for rational approaches to design effective transfection carriers.

Download full-text PDF

Source
http://dx.doi.org/10.1021/bm201184wDOI Listing

Publication Analysis

Top Keywords

fractal network
8
dna
6
stability
5
polyplexes
5
colloidal structure
4
structure stability
4
stability dna/polycations
4
dna/polycations polyplexes
4
polyplexes investigated
4
investigated small
4

Similar Publications

Incorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent motion model in diffusion-weighted MRI.

Med Image Anal

November 2024

Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.

In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge lies in accurately estimating model parameters from the acquired data due to the inherently low signal-to-noise ratio (SNR) of the signal measurements and the complexity of solving the ill-posed inverse problem. Conventional model fitting approaches treat individual voxels as independent.

View Article and Find Full Text PDF

Colloidal properties of nanoparticles are intricately linked to their morphology. Traditionally, achieving high-concentration dispersions of two-dimensional (2D) nanosheets has proven challenging as they tend to agglomerate or re-stack under increased surface contact and Van der Waals attraction. Here, we unveil an excluded volume effect enabled by 2D morphology, which can be coupled with electrostatic repulsion to synthesize high-concentration aqueous graphene dispersions.

View Article and Find Full Text PDF

Applications of Deep Neural Networks with Fractal Structure and Attention Blocks for 2D and 3D Brain Tumor Segmentation.

J Stat Theory Pract

September 2024

Statistics Online Computational Resource, University of Michigan, 426 North Ingalls Str, Ann Arbor, Michigan 48109-2003.

In this paper, we propose a novel deep neural network (DNN) architecture with fractal structure and attention blocks. The new method is tested to identify and segment 2D and 3D brain tumor masks in normal and pathological neuroimaging data. To circumvent the problem of limited 3D volumetric datasets with raw and ground truth tumor masks, we utilized data augmentation using affine transformations to significantly expand the training data prior to estimating the network model parameters.

View Article and Find Full Text PDF

Benefits of swaying while standing to higher selective attention in goal-directed visual tasks.

Hum Mov Sci

December 2024

Univ. Lille, CNRS, UMR 9193 - SCALab, Sciences Cognitives et Sciences Affectives, F-59000 Lille, France. Electronic address:

Background And Aim: Sit-stand desks allow individuals to work in either sitting or standing position. While previous studies have reported better performance on the attention network test (ANT) while standing compared to sitting, the relationship between body sway induced by these positions and ANT performance remains unclear. In this study, we aimed to test and expect benefits of body sway (in terms of magnitude and complexity) and improvements in ANT performance when standing (e.

View Article and Find Full Text PDF

This paper introduces a novel class of chaotic attractors by lever- aging different activation functions within neurons possessing multiple dendrites. We propose a comprehensive framework where the activation functions in neurons are varied, allowing for diverse behaviors such as amplification, fluctuation, and folding of scrolls within the resulting chaotic attractors. By employing wavelet functions and other model-specific activation functions, we demonstrate the capability to modify scroll characteristics, including size and direction.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!