Pectic Galactan Polysaccharide-Based Gene Delivery System for Targeting Neuroinflammation

Treating neuroinflammation-related injuries and disorders through manipulation of neuroinflammation functions is being heralded as a new therapeutic strategy. In this study, a novel pectic galactan (PG) polysaccharide based gene therapy approach is developed for targeting reactive gliosis in neuroinflammation. Galectin-3 (Gal-3) is a cell protein with a high affinity to β-galactoside sugars and is highly expressed in reactive gliosis. Since PG carries galactans, it can target reactive gliosis via specific carbohydrate interaction between galactan and Gal-3 on the cell membrane, and therefore can be utilized as a carrier for delivering genes to these cells. The carrier is synthesized by modifying quaternary ammonium groups on the PG. The resulting quaternized PG (QPG) is found to form complexes with plasmid DNA with a mean diameter of 100 nm and have the characteristics required for targeted gene therapy. The complexes efficiently condense large amounts of plasmid per particle and successfully bind to Gal-3. The in vivo study shows that the complexes are biocompatible and safe for administration and can selectively transfect reactive glial cells of an induced cortical lesion. The results confirm that this PG-based delivery system is a promising platform for targeting Gal-3 overexpressing neuroinflammation cells for treating neuroinflammation-related injuries and neurodegenerative diseases.     

Optimization of vacuum casting process parameters to enhance tensile strength of components using design of experiments approach

Vacuum casting is one of the widely used methods for small-volume production of plastic parts. The main challenge of this method is to choose the optimal w     

A comparative study on hygroscopic and physiochemical properties of chicken powders obtained by different drying methods

Abstract

Different drying methods (spray drying (SD), vacuum drying (VD), microwave vacuum drying (MVD), and infrared vacuum drying (IFVD)) were applied in order to compare the hygroscopicity behavior of chicken powders. The hygroscopicity curves and glass transition temperature were used to evaluate the influence of ambient humidity and temperature on moisture absorption of powders. The results showed that the chicken powder dried by MVD had the lowest moisture absorption, followed by IFVD, VD, and SD. The hygroscopicity of SD chicken powders was different from other three kinds of chicken powders due to the physical properties of particles and the changes of protein secondary structure as detected by the Fourier transform-infrared spectrometer. For the three vacuum drying methods, the difference of protein secondary structure was the main reason of differences in hygroscopicity. Although MVD chicken powders were slightly inferior to SD chicken powders in taste, MVD chicken powders were the best in terms of smell and color as suggested by instrumental sensory parameter evaluations. It was found that MVD had a positive effect on reducing moisture absorption and maintaining sensory quality of chicken powders.     

iRGD-reinforced,photo-transformable nanoclusters toward cooperative enhancement of intratumoral penetration and antitumor efficacy

Nano Research - Insufficient intratumoral penetration greatly hurdles the anticancer performance of nanomedicine. To realize highly efficient tumor penetration in a precisely and spatiotemporally...     

Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps

Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANN_FCM-GRN. In MANN_FCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANN_FCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANN_FCM-GRN and show that MANN_FCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANN_FCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.

     

MPE: a mobility pattern embedding model for predicting next locations

World Wide Web - The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each...