Fermented Ginsenosides Alleviate Acute Liver Injury Induced by CCl4 in Mice by Regulating the AKT/mTOR Signaling Pathway.

This study investigated the alleviating effect of fermented ginsenosides obtained through yeast strain fermentation transformation on acute liver injury (ALI) induced by CCl4. Strains were screened for their ability to produce ß-glucosidase, the transformation ability of the strain was verified by high-performance liquid chromatography, and the Saccharomyces cerevisiae strain F6 was obtained by 26S rRNA sequencing. After fermentation by F6 strain, it was found that the content of ginsenosides Re, Rb1, and Rb2 was significantly decreased (P < 0.05), and rare ginsenosides were detected, with the content of Rh4 and Rg5 reaching 2.65 mg·g-1 and 2.56 mg·g-1. We also explored the preventive effect of fermented ginsenoside extract (FGE) on ALI. Mice were evenly divided into 9 groups as follows: control group, ALI model group, positive drug bifendate group, and treatment group, which included 3 ginsenoside extract (GE) groups and 3 FGE groups (dosage of 150, 300, and 450 mg·kg-1 b.w.). The results showed that compared with the ALI model group, FGE significantly increased the levels of glutathione peroxidase, hydroperoxidase, and superoxide dismutase and also decreased the malondialdehyde level. The levels of alanine aminotransferase, aspartate aminotransferase, and total bilirubin markers were significantly reduced, and the levels of inflammatory cytokines TNF-α, IL-6, and IL-1ß were significantly decreased. Bioinformatics analysis combined with Western blot validation explored the molecular mechanism of the effect of FGE. It was found that FGE could downregulate the expression of the p-AKT/AKT and the p-mTOR/mTOR ratios. These results suggested that FGE played an alleviative role in ALI by promoting autophagy to inhibit the AKT/mTOR signaling pathway.

Four-Dimensional Label-Free Quantitative Proteomics of Ginsenoside Rg2 Ameliorated Scopolamine-Induced Memory Impairment in Mice through the Lysosomal Pathway.

Alzheimer's disease (AD) is a neurodegenerative disease. Ginsenoside Rg2 has shown potential in treating AD, but the underlying protein regulatory mechanisms associated with ginsenoside Rg2 treatment for AD remain unclear. This study utilized scopolamine to induce memory impairment in mice, and proteomics methods were employed to investigate the potential molecular mechanism of ginsenoside Rg2 in treating AD model mice. The Morris water maze, hematoxylin and eosin staining, and Nissl staining results indicated that ginsenoside Rg2 enhanced cognitive ability and decreased neuronal damage in AD mice. Proteomics, western blot, and immunofluorescence results showed that ginsenoside Rg2 primarily improved AD mice by downregulating the expression of LGMN, LAMP1, and PSAP proteins through the regulation of the lysosomal pathway. Transmission electron microscopy and network pharmacology prediction results showed a potential connection between the mechanism of ginsenoside Rg2 treatment for AD mice and lysosomes. The comprehensive results indicated that ginsenoside Rg2 may improve AD by downregulating LGMN, LAMP1, and PSAP through the regulation of the lysosomal pathway.

Investigation of the structure, rheology and 3D printing characteristics of corn starch regulated by glycyrrhizic acid.

This study aimed to construct a novel corn starch-glycyrrhizic acid (CS-GA) ink and systematically investigate the effects of GA on the water distribution, microstructure, rheology and 3D printing properties of CS hydrogels. The results showed that the CS chains could form strong hydrogen bonds with GA molecules, inhibit the formation of short-range ordered structure of CS and reduce the content of B-type starch. The low-field nuclear magnetic results showed that the introduction of GA could increase bound water content in CS-GA hydrogels. With the increase of GA content, the CS-GA hydrogel changed from CS-dominated to a GA-dominated gel network system. Rheological results showed that all samples exhibited typical shear thinning behavior. High GA concentration was beneficial to increasing the self-supporting properties and thixotropic recovery of CS-GA hydrogels. Compared with the pure CS hydrogel, the 3D printing characteristics of CS-GA hydrogels were significantly enhanced due to the increased bound water content and the enhancement of rheological properties. At 40 % GA content, CS-GA hydrogel showed the highest printing accuracy of 96.4 % ± 0.30 %. The printed product could perfectly replicate the preset model. Therefore, this study provided a theoretical basis for regulating starch's rheology and 3D printing characteristics and developing novel food-grade 3D printing inks.

Salt-Supported Nickel Oxides for Boosted Hydrogen Production: The Critical Role of Halogen.

Hydrogen production from organic waste by gasification and reforming technologies offers major benefits to both the environment and climate. The long-term stability and regeneration of the reforming catalyst are still the biggest challenges because of carbon deposition. Here we report a recyclable salt-supported nickel oxide NiO/NaX (X: F, Cl, Br) catalyst for effective autothermal reforming of the oxygenated volatile organic compound (OVOC) ethyl acetate to hydrogen. The optimal hydrogen selectivity achieved 82.0% at 650 °C and the durability reached 43 h. Interestingly, with the decreasing of halogen electronegativity (F > Cl > Br) in NaX, the corresponding hydrogen selectivity of the catalysts decreased. Although NiO/NaX catalysts possess a very small specific surface area and a dense microstructure, their catalytic performance is better than that of normal Ni-based catalysts loaded on high-specific-surface-area supports. Detailed investigations revealed the critical roles played by halogen during the reforming reaction. First, the strong electronegative halogen in NaX induced the formation of hydrogen bonds with the reactants and reaction intermediates, which may prolong the surface residence time of such species, thus ensuring efficient hydrogen production over small-specific-surface-area catalysts under high-temperature conditions. Second, the halogen of the support NaX weakening the Ni-O bonds of the exposed Ni atoms in NiO/NaX made it easier for NiO to be reduced to Ni0, thus reducing the reaction activation energy and prompting the rapid catalytic reaction. The strength of such metal-support interaction can be easily modulated by varying the halogen electronegativity. This study provides a new prospect for the design of innovative recyclable heterogeneous catalysts with low specific surface area but high activity.

Effect of fermentation with Lactobacillus fermentum FL-0616 on probiotic-rich bean powders.

Plant-based diets have received considerable attention for balancing human health and environmental sustainability. This study investigated the effects of fermentation with Lactobacillus fermentum FL-0616 on probiotic-rich mung bean, chickpea and tiger skin kidney bean powders. A particle size distribution experiment showed that the particle size of probiotic-rich bean powder was significantly reduced and the specific surface area was increased. This was critical for improving the dissolution rate, wettability and dispersibility. Simultaneously, the angles of repose and slide of the fermented bean powder were significantly reduced. Scanning electron microscopy confirmed that particle size of the bean powder decreased and became more uniform after fermentation. The results of dynamic and static rheology jointly demonstrated that fermentation improved the flowability of probiotic-rich bean powder, which was related to its decreased particle size. This study provides a technical foundation for the deep processing of bean resources. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-023-05668-5.

Structure and properties of Pickering emulsions stabilized solely with novel buckwheat protein colloidal particles.

In this paper, buckwheat protein colloidal particles (BPCPs) were prepared by heat treatment to stabilize oil-water interface. The results of particle size, surface hydrophobicity and wettability indicated that the prepared BPCPs could be used as novel Pickering emulsifier. The effects of BPCPs concentration, ionic strength and heat treatment on the structure and properties of Pickering emulsions were explored. The microstructure results showed that BPCPs could tightly coated on the surface of oil droplets to form a tight interfacial film, confirming that BPCPs could be used as an effective Pickering-like stabilizer. With the increase of BPCPs concentration, the droplet size of the Pickering emulsion gradually decreased, and the viscoelasticity and storage stability of the emulsion were effectively improved. Different from the effect of ionic strength, heat treatment was beneficial to increasing the viscoelasticity of BPCPs-stabilized Pickering emulsion. The Pickering emulsions exhibited certain flocculation at different temperatures and ionic strengths, while still maintained good solid-like behavior. These results suggest that the structure and properties of BPCPs-stabilized Pickering emulsion could be regulated by changing the ionic strength and temperature.

Polysaccharides-based delivery system for efficient encapsulation and controlled release of food-derived active peptides.

A polysaccharides-based delivery system was designed to encapsulate and control the release of peanut peptide (PP). The PP-loaded polyelectrolyte complex (TMC-PP-SA) was fabricated based on the electrostatic self-assembly between n-trimethy chitosan (TMC) and sodium alginate (SA). The complex exhibited uniform spherical morphology, satisfactory stability and high encapsulation efficiency. In vitro release behavior indicated that TMC-PP-SA polyelectrolyte complex could inhibit the release of PP at simulated gastric medium and enhance the release of PP at simulated intestinal medium. Moreover, the antioxidant activity of PP after encapsulation was significantly improved compared with that of directly digested PP. Ex vivo intestinal permeation study confirmed that about 41.76 ± 1.43% PP in TMC-PP-SA could be absorbed in the intestinal. The cytotoxicity measurement indicated that the fabricated TMC-PP-SA polyelectrolyte complex was biocompatible and nontoxic. Therefore, these results indicated that the polysaccharides-based delivery system had great potential in protecting active peptides from degradation and facilitating their absorption.

Unsupervised Monocular Depth Estimation Method Based on Uncertainty Analysis and Retinex Algorithm.

Depth estimation of a single image presents a classic problem for computer vision, and is important for the 3D reconstruction of scenes, augmented reality, and object detection. At present, most researchers are beginning to focus on unsupervised monocular depth estimation. This paper proposes solutions to the current depth estimation problem. These solutions include a monocular depth estimation method based on uncertainty analysis, which solves the problem in which a neural network has strong expressive ability but cannot evaluate the reliability of an output result. In addition, this paper proposes a photometric loss function based on the Retinex algorithm, which solves the problem of pulling around pixels due to the presence of moving objects. We objectively compare our method to current mainstream monocular depth estimation methods and obtain satisfactory results.

Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model.

Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people's lives and property.

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios.

Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance.

Changing the Phosphorus Allotrope from a Square Columnar Structure to a Planar Zigzag Nanoribbon by Increasing the Diameter of Carbon Nanotube Nanoreactors.

Elemental phosphorus nanostructures are notorious for a large number of allotropes, which limits their usefulness as semiconductors. To limit this structural diversity, we synthesize selectively quasi-1D phosphorus nanostructures inside carbon nanotubes (CNTs) that act both as stable templates and nanoreactors. Whereas zigzag phosphorus nanoribbons form preferably in CNTs with an inner diameter exceeding 1.4 nm, a previously unknown square columnar structure of phosphorus is observed to form inside narrower nanotubes. Our findings are supported by electron microscopy and Raman spectroscopy observations as well as ab initio density functional theory calculations. Our computational results suggest that square columnar structures form preferably in CNTs with an inner diameter around 1.0 nm, whereas black phosphorus nanoribbons form preferably inside CNTs with a 4.1 nm inner diameter, with zigzag nanoribbons energetically favored over armchair nanoribbons. Our theoretical predictions agree with the experimental findings.

Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles.

Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.

A flexible, adhesive and self-healable hydrogel-based wearable strain sensor for human motion and physiological signal monitoring.

The advent of hydrogel-based strain sensors has attracted immense research interest in artificial intelligence, wearable devices, and health-monitoring systems. However, the integration of the synergistic characteristics of good mechanical properties, self-adhesiveness, self-healing capability and high strain sensitivity for fabricating hydrogel-based strain sensors is still a challenge. Here, a multifunctional conductive hydrogel composed of a polyacrylamide (PAAm)/chitosan (CS) hybrid network is fabricated for wearable strain sensors. The PAAm network is cross-linked by hydrophobic associations, and the CS network is ionically cross-linked by carboxyl-functionalized multi-walled carbon nanotubes (c-MWCNTs). These two networks are further interlocked by physical entanglement and hydrogen bond interactions. The obtained hydrogels exhibit excellent flexibility, puncture resistance and self-healing capability because of the efficient energy dissipation of the dynamic cross-linking network. Moreover, the hydrogels exhibit self-adhesive behavior on various materials, including polytetrafluoroethylene, wood, glass, aluminum, rubber and skin. Notably, the hydrogels can be applied as soft human-motion sensors for real-time and accurate detection of both large-scale and small human activities, including joint motions, speaking, breathing, and even subtle blood pulsation. Therefore, it is anticipated that the flexible, self-adhesive, self-healing and conductive hydrogel-based strain sensor will have promising applications in artificial intelligence, soft robots, biomimetic prostheses, and personal health care.

Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments.

Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.

A Facile Strategy to Enhance the Dielectric and Mechanical Properties of MWCNTs/PVDF Composites with the Aid of MMA-co-GMA Copolymer.

A facile strategy is adopted to prepare carboxylic functionalized multiwalled carbon nanotube (c-MWCNT) modified high dielectric constant (high-k) poly(vinylidene fluoride) (PVDF) composites with the aid of methyl methacrylate-co-glycidyl methacrylate copolymer (MG). The MG is miscible with PVDF and the epoxy groups of the copolymer can react with the carboxylic groups of c-MWCNT, which induce the uniform dispersion of c-MWCNT and a form insulator layer on the surface of c-MWCNT. The c-MWCNTs/MG/PVDF composites with 8 vol % c-MWCNT present excellent dielectric properties with high dielectric constant (~448) and low dielectric loss (~2.36) at the frequency of 1 KHz, the dielectric loss is much lower than the c-MWCNT/PVDF composites without MG. The obvious improvement in dielectric properties ascribes to the existence of MG, which impede the direct contact of c-MWCNTs and PVDF and avoid the formation of conductive network. Therefore, we propose a practical and simple strategy for preparing composites with excellent dielectric properties, which are promising for applications in electronics devices.

Construction of a quaternary ammonium salt platform with different alkyl groups for antibacterial and biosensor applications.

An electrochemical platform was designed using biocompatible quaternary ammonium salts containing alkyl groups with different chain lengths as electrode materials for visible protein immobilization on a glassy carbon (GC) electrode. The electrode was constructed using a simple self-assembly method relying on the electrostatic interaction between negatively charged hemoglobin (Hb) and positively charged quaternary ammonium materials. The Hb/quaternary ammonium salts/GC assembly exhibited excellent catalytic and electrochemical activities. Additionally, the structure-function properties of the quaternary ammonium salts on the electrochemical behavior of Hb was systematically investigated for various alkyl chain lengths between monomer and polymeric structures. Meanwhile, the corresponding bactericidal activities of the monomers and related polymers were evaluated by determining the minimum bactericidal concentration (MBC), minimum inhibitory concentration (MIC), and inhibitory zone diameters against bacteria. The results of these studies demonstrated that the quaternary ammonium monomers not only immobilized more proteins, but also displayed better antibacterial activity as alkyl chain length increased. Moreover, polymers possessed higher antimicrobial activities than their monomeric counterparts. However, the efficiency of the direct electron transfer process and the antibacterial properties of long-chain polymers were limited because they were prone to aggregation and blistering. In summary, the present results provide convenient access to direct electrochemistry using an immobilized redox protein. Furthermore, the potential to use the obtained materials in the construction of third-generation electrochemical biosensors was evaluated.

Highly tough, anti-fatigue and rapidly self-recoverable hydrogels reinforced with core-shell inorganic-organic hybrid latex particles.

The introduction of SiO2 particles as crosslinking points into hydrogels has been recognized as a suitable way for toughening hydrogels, due to their versatile functionalization and large specific surface area. However, chemically linked SiO2 nanocomposite hydrogels often exhibited negligible fatigue resistance and poor self-recoverable properties due to the irreversible cleavage of covalent bonds. Here, we proposed a novel strategy to improve stretchability, fatigue resistance and self-recoverable properties of hydrogels by using SiO2-g-poly(butyl acrylate) core-shell inorganic-organic hybrid latex particles as hydrophobic crosslinking centers for hydrophobic association. The obtained hydrogel could distribute the surrounding applied stress by disentanglement of the hybrid latex particles from hydrophobic segments. Based on this strategy, the formulated hydrogels showed an excellent tensile strength of 1.48 MPa, superior stretchability of 2511% and remarkable toughness of 12.62 MJ m-3. Moreover, the hydrogels owned extraordinary anti-fatigue, rapid self-recovery and puncture resistance properties. Therefore, this strategy provided a novel pathway for developing advanced soft materials with potential applications in biomedical engineering, such as tendons, muscles, cartilages, etc.

Grafting Modification of the Reactive Core-Shell Particles to Enhance the Toughening Ability of Polylactide.

In order to overcome the brittleness of polylactide (PLA), reactive core-shell particles (RCS) with polybutadiene as core and methyl methacrylate-co-styrene-co-glycidyl methacrylate as shell were prepared to toughen PLA. Tert-dodecyl mercaptan (TDDM) was used as chain transfer agent to modify the grafting properties (such as grafting degree, shell thickness, internal and external grafting) of the core-shell particles. The introduction of TDDM decreased the grafting degree, shell thickness and the Tg of the core phase. When the content of TDDM was lower than 1.15%, the RCS particles dispersed in the PLA matrix uniformly-otherwise, agglomeration took place. The addition of RCS particles induced a higher cold crystallization temperature and a lower melting temperature of PLA which indicated the decreased crystallization ability of PLA. Dynamic mechanical analysis (DMA) results proved the good miscibility between PLA and the RCS particles and the increase of TDDM in RCS induced higher storage modulus of PLA/RCS blends. Suitable TDDM addition improved the toughening ability of RCS particles for PLA. In the present research, PLA/RCS-T4 (RCS-T4: the reactive core-shell particles with 0.76 wt % TDDM addition) blends displayed much better impact strength than other blends due to the easier cavitation/debonding ability and good dispersion morphology of the RCS-T4 particles. When the RCS-T4 content was 25 wt %, the impact strength of PLA/RCS-T4 blend reached 768 J/m, which was more than 25 times that of the pure PLA.