Development of high-efficiency superparamagnetic drug delivery system with MPI imaging capability.

In this study, a high-efficiency superparamagnetic drug delivery system was developed for preclinical treatment of bladder cancer in small animals. Two types of nanoparticles with magnetic particle imaging (MPI) capability, i.e., single- and multi-core superparamagnetic iron oxide nanoparticles (SPIONs), were selected and coupled with bladder anti-tumor drugs by a covalent coupling scheme. Owing to the minimal particle size, magnetic field strengths of 270 mT with a gradient of 3.2 T/m and 260 mT with a gradient of 3.7 T/m were found to be necessary to reach an average velocity of 2 mm/s for single- and multi-core SPIONs, respectively. To achieve this, a method of constructing an in vitro magnetic field for drug delivery was developed based on hollow multi-coils arranged coaxially in close rows, and magnetic field simulation was used to study the laws of the influence of the coil structure and parameters on the magnetic field. Using this method, a magnetic drug delivery system of single-core SPIONs was developed for rabbit bladder therapy. The delivery system consisted of three coaxially and equidistantly arranged coils with an inner diameter of Φ50 mm, radial height of 85 mm, and width of 15 mm that were positioned in close proximity to each other. CCK8 experimental results showed that the three types of drug-coupled SPION killed tumor cells effectively. By adjusting the axial and radial positions of the rabbit bladder within the inner hole of the delivery coil structure, the magnetic drugs injected could undergo two-dimensional delivery motions and were delivered and aggregated to the specified target location within 12 s, with an aggregation range of about 5 mm × 5 mm. In addition, the SPION distribution before and after delivery was imaged using a home-made open-bore MPI system that could realistically reflect the physical state. This study contributes to the development of local, rapid, and precise drug delivery and the visualization of this process during cancer therapy, and further research on MPI/delivery synchronization technology is planned for the future.

Effective synthesis of high-content fructooligosaccharides in engineered Aspergillus niger.

BACKGROUND: Aspergillus niger ATCC 20611 is an industrially important fructooligosaccharides (FOS) producer since it produces the ß-fructofuranosidase with superior transglycosylation activity, which is responsible for the conversion of sucrose to FOS accompanied by the by-product (glucose) generation. This study aims to consume glucose to enhance the content of FOS by heterologously expressing glucose oxidase and peroxidase in engineered A. niger. RESULTS: Glucose oxidase was successfully expressed and co-localized with ß-fructofuranosidase in mycelia. These mycelia were applied to synthesis of FOS, which possessed an increased purity of 60.63% from 52.07%. Furthermore, peroxidase was expressed in A. niger and reached 7.70 U/g, which could remove the potential inhibitor of glucose oxidase to facilitate the FOS synthesis. Finally, the glucose oxidase-expressing strain and the peroxidase-expressing strain were jointly used to synthesize FOS, which content achieved 71.00%. CONCLUSIONS: This strategy allows for obtaining high-content FOS by the multiple enzymes expressed in the industrial fungus, avoiding additional purification processes used in the production of oligosaccharides. This study not only facilitated the high-purity FOS synthesis, but also demonstrated the potential of A. niger ATCC 20611 as an enzyme-producing cell factory.

Expectations for employing Escherichia coli Nissle 1917 in food science and nutrition.

As a promising probiotic strain, Escherichia coli Nissle 1917 (EcN) has been demonstrated to confer beneficial effects on intestinal health, immune function, and pathogen prevention. Additionally, EcN has also been widely studied due to its clear genomic information, tractable gene regulation, and simple growth conditions. This review summarizes the various applications potential of EcN in food science and nutrition, including inflammation prevention, tumor-targeting therapy, antibacterial agents for food, and nutrient production with a focus on specific case studies. Moreover, we highlight the major challenges of employing EcN in food science and nutrition, including regulatory approval, stability during food processing, and consumer acceptance. Finally, we conclude with a discussion on perspectives related to employing EcN in food science and nutrition.

Correction: Sheng et al. Antibacterial and Angiogenic Poly(ionic liquid) Hydrogels. Gels 2022, 8, 476.

In the original publication [...].

Human milk oligosaccharide lacto-N-tetraose: Physiological functions and synthesis methods.

Human milk oligosaccharides (HMOs) have attracted considerable attention due to their unique role in boosting infant health. Among the HMOs, lacto-N-tetraose (LNT) is a significant constituent associated with various health benefits, such as prebiotic effects, antiadhesive antimicrobials, antiviral protection, and immune modulators. LNT has received a "Generally Recognized as Safe" status by the American Food and Drug Administration and was approved as a food ingredient for infant formula. However, the limited availability of LNT poses a major challenge for its application in food and medicine. In this review, we first explored the physiological functions of LNT. Next, we describe several synthesis methods for production of LNT, including chemical, enzymatic, and cell factory approaches, and summarize the pivotal research results. Finally, challenges and opportunities for the large-scale synthesis of LNT were discussed.

Adaptive NN Optimal Consensus Fault-Tolerant Control for Stochastic Nonlinear Multiagent Systems.

This article investigates the problem of adaptive neural network (NN) optimal consensus tracking control for nonlinear multiagent systems (MASs) with stochastic disturbances and actuator bias faults. In control design, NN is adopted to approximate the unknown nonlinear dynamic, and a state identifier is constructed. The fault estimator is designed to solve the problem raised by time-varying actuator bias fault. By utilizing adaptive dynamic programming (ADP) in identifier-critic-actor construction, an adaptive NN optimal consensus fault-tolerant control algorithm is presented. It is proven that all signals of the controlled system are uniformly ultimately bounded (UUB) in probability, and all states of the follower agents can remain consensus with the leader's state. Finally, simulation results are given to illustrate the effectiveness of the developed optimal consensus control scheme and theorem.

Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy.

The traditional Grey Wolf Optimization algorithm (GWO) has received widespread attention due to features of strong convergence performance, few parameters, and easy implementation. However, in actual optimization projects, there are problems of slow convergence speed and easy to fall into local optimal solution. The paper proposed a Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy (CG-GWO) in response to the above problems. The Cauchy-Gaussian mutation operator is introduced to increase the population diversity of the leader wolves and improve the global search ability of the algorithm. This work retains outstanding grey wolf individuals through the greedy selection mechanism to ensure the convergence speed of the algorithm. An improved search strategy was proposed to expand the optimization space of the algorithm and improve the convergence accuracy. Experiments are performed with 16 benchmark functions covering unimodal functions, multimodal functions, and fixed-dimension multimodal functions to verify the effectiveness of the algorithm. Experimental results show that compared with four classic optimization algorithms, particle swarm optimization algorithm (PSO), whale optimization algorithm (WOA), sparrow optimization algorithm (SSA), and farmland fertility algorithm (FFA), the CG-GWO algorithm shows better convergence accuracy, convergence speed, and global search ability. The proposed algorithm shows the same better performance compared with a series of improved algorithms such as the improved grey wolf algorithm (IGWO), modified Grey Wolf Optimization algorithm (mGWO), and the Grey Wolf Optimization algorithm inspired by enhanced leadership (GLF-GWO).

Removal of starch granule-associated surface and channel lipids alters the properties of sodium trimetaphosphate crosslinked maize starch.

Starch granule-associated surface and channel lipids (SGALs) were effectively removed from waxy maize starch (WMS) and normal maize starch (NMS), then the starches were crosslinked by different levels of sodium trimetaphosphate (STMP) (0.25 %, 0.5 %, 1 % and 2 %). The effective removal of SGALs and successful crosslinking, were evidenced by the disappearance of surface-fluorescence and channel-fluorescence of Pro-Q Diamond-stained granules, and the increased phosphorus content respectively. STMP crosslinking increased peak and final viscosity for WMS and NMS. Crosslinking at high STMP levels (0.5 %, 1 % and 2 %) transformed the starch pastes from thixotropic to anti-thixotropic. STMP crosslinking significantly decreased the tan δ values of maize starches, enhancing the elastic structure of the gel. Crosslinked maize starches without SGALs had lower breakdown than crosslinked starches at same STMP level, indicating higher tightened crosslinked starch granules after SGALs removal. Removal of SGALs increased the anti-thixotropy of crosslinked starches, facilitating the reorientation of crosslinked amylopectin/amylose molecules during shearing. Removal of SGALs increased the tan δ values from frequency sweep of WMS and NMS during STMP crosslinking, indicating the presence of surface-lipids and channel-lipids could enhance the elastic gel network structure of crosslinked maize starch.

Antibacterial and Angiogenic Poly(ionic liquid) Hydrogels.

Wounds, particularly under low-hydration conditions, require more time to repair successfully. Therefore, there is an urgent need to develop wound dressings that can accelerate wound healing. Hydrogels, which can maintain a moist environment around the wound and allow gas to pass through the material, act as antibacterial hydrogels as dressings and have great application value in the treatment of wounds. In addition, wound dressings (hydrogels) containing antibacterial capacity have lasting antibacterial effects and reduce damage to cells. In this work, we firstly synthesized two antibacterial agents: imidazolium poly(ionic liquids) containing sulfhydryl (Imidazole-SH) and ε-Poly(lysine) containing SH (EPL-SH). Then, lysine as a cross-linking agent, by "thiol-ene" click reaction, was mixed with Deferoxamine (DFO) to prepare the antibacterial hydrogels. The in vitro assays showed that the hydrogels could effectively kill Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). In addition, it also could reduce the inflammatory response produced by Lipopolysaccharide (LPS). More importantly, according to the transwell and angiogenesis assays, DFO-incorporated hydrogels promoted the migration and vascular repair of human umbilical vein endothelial cells (HUVECs). All the results revealed that the hydrogels provided new strategies for wound dressings.

Adaptive Optimized Backstepping Control-Based RL Algorithm for Stochastic Nonlinear Systems With State Constraints and Its Application.

This article investigates the adaptive neural-network (NN) tracking optimal control problem for stochastic nonlinear systems, which contain state constraints and uncertain dynamics. First, to avoid the violation of state constraints in achieving optimal control, the novel barrier optimal performance index functions for subsystems are developed. Second, under the framework of the identifier-actor-critic, the virtual and actual optimal controllers are presented based on the backstepping technique, in which the unknown nonlinear dynamics are learned by the NN approximators. Moreover, the quartic barrier Lyapunov functions are constructed instead of square ones to cope with the Hessian term to ensure the stability of the systems with stochastic disturbance. The proposed optimal control strategy can guarantee the boundedness of closed-loop signals, and the output can follow the given reference signal. Meanwhile, the system states are restricted within some preselected compact sets all the while. Finally, both numerical and practical systems are carried out to further illustrate the validity of the proposed optimal control approach.

A survey of adaptive optimal control theory.

This paper makes a survey about the recent development of optimal control based on adaptive dynamic programming (ADP). First of all, based on DP algorithm and reinforcement learning (RL) algorithm, the origin and development of the optimization idea and its application in the control field are introduced. The second part introduces achievements in the optimal control direction, then we classify and summarize the research results of optimization method, constraint problem, structure design in control algorithm and practical engineering process based on optimal control. Finally, the possible future research topics are discussed. Through a comprehensive and complete investigation of its application in many existing fields, this survey fully demonstrates that the optimal control algorithms via ADP with critic-actor neural network (NN) structure, which also have a broad application prospect, and some developed optimal control design algorithms have been applied to practical engineering fields.

An Enhanced Scheme for Multiscale Modeling of Thermomechanical Properties of Polymer Bulks.

While multiscale modeling significantly enhances the capability of molecular simulations of polymer systems, it is well realized that the systematically derived coarse-grained (CG) models generally underestimate the thermomechanical properties. In this work, a charge-based mapping scheme has been adopted to include explicit electrostatic interactions and benchmarked against two typical polymers, atactic poly(methyl methacrylate) (PMMA) and polystyrene (PS). The CG potentials are parameterized against the oligomer bulks of nine monomers per chain to match the essential structural features and the two basic pressure-volume-temperature (PVT) properties, which are obtained from the all-atomistic (AA) molecular dynamics (MD) simulations at a single elevated temperature. The so-parameterized CG potentials are extended with the MD method to simulate the two polymer bulks of one hundred monomers per chain over a wide temperature range. Without any scaling, all the simulated results, including mass densities and bulk moduli at room temperature, thermal expansion coefficients at rubbery and glassy states, and glass transition temperatures (Tg), compare well with the corresponding experimental data. The proposed scheme not only contributes to realistically simulating various thermomechanical properties of both apolar and polar polymers but also allows for directly simulating their electrical properties.

Adaptive Neural Network Finite-Time Dynamic Surface Control for Nonlinear Systems.

This article addresses the problem of finite-time neural network (NN) adaptive dynamic surface control (DSC) design for a class of single-input single-output (SISO) nonlinear systems. Such designs adopt NNs to approximate unknown continuous system functions. To avoid the "explosion of complexity" problem, a novel nonlinear filter is developed in control design. Under the framework of adaptive backstepping control, an NN adaptive finite-time DSC design algorithm is proposed by adopting a smooth projection operator and finite-time Lyapunov stable theory. The developed control algorithm means that the tracking error converges to a small neighborhood of origin within finite time, which further verifies that all the signals of the controlled system possess globally finite-time stability (GFTS). Finally, both numerical and practical simulation examples and comparing results are provided to elucidate the superiority and effectiveness of the proposed control algorithm.

Modeling the Effects of Module Size and Material Property on Thermoelectric Generator Power.

It is known that thermoelectric power generators (TEGs) can utilize geothermal resources and recycle waste heat. It is vital to improve the thermoelectric power generation efficiency to economically and efficiently use these thermal resources. In this paper, ANSYS was used to build a three-dimensional model of a very simple TEG with only one pair of p- and n-legs (1-PN-TEG) to find the optimal design. The thickness of the semiconductor elements, the cross-sectional area of p- and n-type semiconductor elements, the heat insulation material, the thickness of copper sheet, and other factors were analyzed to study their effects on the power output of 1-PN-TEG. The results show that the power of TEG increases first and then decreases with the thickness of p- and n-legs (H); the maximum power existed at a specific value of H. The power increases when the cross-sectional areas of p- and n-type semiconductor elements become more extensive, but the power per area decreases. Furthermore, the power increases with the volume of p- and n-type semiconductor elements and tends to be stabilized finally. This observation may be used to estimate how much thermoelectric material is required to generate a specific value of TEG power. The gaps between p- and n-type semiconductor elements were filled with different heat insulation materials. The heat insulation material with lower thermal conductivity had a greater power output. The thickness of the copper sheet, as a conductor between p- and n-type semiconductor elements, was also investigated. The maximum power value was reached when the thickness of the copper sheet was equal to about 1.0 mm. All of the results obtained in this paper might provide a theoretical basis for the configuration and design optimization of a thermoelectric generator, making more efficient use of geothermal resources and the waste heat.

Adaptive Neural Network Finite-Time Control for Multi-Input and Multi-Output Nonlinear Systems With Positive Powers of Odd Rational Numbers.

This article investigates the adaptive neural network (NN) finite-time output tracking control problem for a class of multi-input and multi-output (MIMO) uncertain nonlinear systems whose powers are positive odd rational numbers. Such designs adopt NNs to approximate unknown continuous system functions, and a controller is constructed by combining backstepping design and adding a power integrator technique. By constructing new iterative Lyapunov functions and using finite-time stability theory, the closed-loop stability has been achieved, which further verifies that the entire system possesses semiglobal practical finite-time stability (SGPFS), and the tracking errors converge to a small neighborhood of the origin within finite time. Finally, a simulation example is given to elaborate the effectiveness and superiority of the developed.

Long non­coding RNA RP1­163G9.1 is downregulated in gastric adenocarcinoma and is associated with a poor prognosis.

The aim of the present study was to investigate the expression, function and underlying molecular mechanism of the long non­coding (lnc) RNA RP1­163G9.1 in patients with gastric adenocarcinoma (GA). The expression levels of lncRNA RP1­163G9.1 were determined in 112 paired clinical GA tissues by reverse transcription­quantitative polymerase chain reaction analysis. Subsequently, the potential clinical values of lncRNA RP1­163G9.1 were analyzed with statistical methods. Additionally, the function of lncRNA RP1­163G9.1 was explored at the cellular level using the Cell Counting Kit­8 proliferation assay, Transwell experiments, fluorescence in situ hybridization (FISH), colony formation assay and flow cytometry. Furthermore, the function of lncRNA RP1­163G9.1 was assessed in vivo using subcutaneous tumorigenesis experiments in nude mice. lncRNA RP1­163G9.1 expression in GA tissues and cells was significantly decreased when compared with that in control gastric tissues (P<0.001) or gastric epithelial cells GES­1 (P<0.05). This finding was associated with the depth of invasion (P=0.001), lymph node metastasis (P=0.009), tumor size (P=0.037) and immunocytochemistry marker Ki­67 (P=0.010). FISH detection demonstrated that lncRNA RP1­163G9.1 was primarily located in the cytoplasm. Notably, overexpression of lncRNA RP1­163G9.1 significantly decreased cell proliferation (P<0.01), colony formation (P<0.01), invasion (P<0.01) and the number of cells at the S­phase of the cell cycle (P<0.05); However, it did not exert a significant effect on apoptosis (P>0.05). Furthermore, tumor formation experiments revealed that overexpression of lncRNA RP1­163G9.1 inhibited cancer cell proliferation in nude mice. The present research indicated that low expression of lncRNA RP1­163G9.1 may be associated with enhanced tumor proliferation and invasion in GA.

Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data.

The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error calculation performance of the AdaBoost algorithm by comprehensively considering the effects of misclassification probability and AUC. To prevent redundant or useless weak classifiers the traditional AdaBoost algorithm generated from consuming too much system resources, this paper proposes an ensemble algorithm, PSOPD-AdaBoost-A, which can re-initialize parameters to avoid falling into local optimum, and optimize the coefficients of AdaBoost weak classifiers. Experiment results show that the proposed algorithm is effective for processing imbalanced data, especially the data with relatively high imbalances.

Enhancement of surface bioactivity on carbon fiber-reinforced polyether ether ketone via graphene modification.

BACKGROUND AND OBJECTIVE: The modulus of carbon fiber-reinforced polyether ether ketone (CFR-PEEK), a composite containing layers of carbon fiber sheets, can be precisely controlled to match bone. However, CFR-PEEK is biologically inert and cannot promote bone apposition. The objective of this study was to investigate whether graphene modification could enhance the bioactivity of CFR-PEEK. METHODS AND RESULTS: In vitro, the proliferation and differentiation of rat bone marrow stromal cells on scaffolds were quantified via cell-counting kit-8 assay and Western blotting analysis of osteoblast-specific proteins. Graphene modification significantly promoted bone marrow stromal cell proliferation and accelerated induced differentiation into osteogenic lineages compared to cells seeded onto nongraphene-coated CFR-PEEK. An in vivo rabbit extraarticular graft-to-bone healing model was established. At 4, 8, and 12 weeks after surgery, microcomputed tomography analyses and histological observations revealed significantly better microstructural parameters and higher average mineral apposition rates for graphene-modified CFR-PEEK implants than CFR-PEEK implants (P<0.05). van Gieson staining indicated more new bone was formed around graphene-modified CFR-PEEK implants than CFR-PEEK implants. CONCLUSION: Graphene may have considerable potential to enhance the bioactivity and osseointegration of CFR-PEEK implants for clinical applications.

A Conductive Self-Healing Double Network Hydrogel with Toughness and Force Sensitivity.

Mechanically tough and electrically conductive self-healing hydrogels may have broad applications in wearable electronics, health-monitoring systems, and smart robotics in the following years. Herein, a new design strategy is proposed to synthesize a dual physical cross-linked polyethylene glycol/poly(acrylic acid) (PEG/PAA) double network hydrogel, consisting of ferric ion cross-linked linear chain extensions of PEG (2,6-pyridinedicarbonyl moieties incorporated into the PEG backbone, PEG-H2 pdca) as the first physical network and a PAA-Fe3+ gel as the second physical network. Metal-ion coordination and the double network structure enable the double network hydrogel to withstand up to 0.4 MPa tensile stress and 1560 % elongation at breakage; the healing efficiency reaches 96.8 % in 12 h. In addition, due to dynamic ion transfer in the network, the resulting hydrogels exhibit controllable conductivity (0.0026-0.0061 S cm-1 ) and stretching sensitivity. These functional self-healing hydrogels have potential applications in electronic skin. It is envisioned that this strategy can also be employed to prepare other high-performance, multifunctional polymers.

Graphene coating on the surface of CoCrMo alloy enhances the adhesion and proliferation of bone marrow mesenchymal stem cells.

The objective was to investigate whether a graphene coating could improve the surface bioactivity of a cobalt-chromium-molybdenum-based alloy (CoCrMo). Graphene was produced by chemical vapor deposition and transferred to the surface of the CoCrMo alloy using an improved wet transfer approach. The morphology of the samples was observed, and the adhesion force and stabilization of graphene coating were analyzed by a nanoscratch test and ultrasonication test. In an in vitro study, the adhesion and proliferation of bone marrow mesenchymal stem cells (BMSCs) cultured on the samples were quantified via an Alamar Blue assay and cell counting kit-8 (CCK-8) assay. The results showed that it is feasible to apply graphene to modify the surface of a CoCrMo alloy, and the enhancement of the adhesion and proliferation of BMSCs was also shown in the present study. In conclusion, graphene exhibits considerable potential for enhancing the surface bioactivity of CoCrMo alloy.