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Abstract Pumped storage plants (PSPs) play an important role in renewable energy consumption in power systems. Variable‐speed technology is a new and critical direction for the development of PSPs. In pump mode, variable‐speed pumped storage units (VSPSUs) have wider power regulation ranges and more flexible power responses than fixed‐speed pumped storage units (FSPSUs); however, the corresponding quantification study of VSPSUs is rare. Therefore, this study aims to quantify the static and dynamic power regulation characteristics of VSPSU in pump mode. First, the modeling of the fast speed control strategy of VSPSU in pump mode is improved. Then, the power regulation range of the VSPSU is explored. The factors (i.e., power command, governor, and converter) affecting the power response rapidity are quantified through the engineering case of a variable‐speed PSP adopting the doubly fed induction machine. Accordingly, suggestions on the settings of the parameters are provided. Quantification results show that parameter tuning based on this study effectively solves the problem of low regulation rate caused by great power fluctuations under typical power regulation cases in pump mode. This VSPSU has the fastest regulation rate of 13.75%/s when adjusting 40% power. This study could provide technical support to the practical operation of VSPSU.

In cold regions, the dynamic compressive strength (DCS) of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering. Nevertheless, testing the dynamic strength under freeze-thaw weathering conditions is often both time-consuming and expensive. Therefore, this study considers the effect of characteristic impedance on DCS and aims to quickly determine the DCS of frozen-thawed rocks through the application of machine-learning techniques. Initially, a database of DCS for frozen-thawed rocks, comprising 216 rock specimens, was compiled. Three external load parameters (freeze-thaw cycle number, confining pressure, and impact pressure) and two rock parameters (characteristic impedance and porosity) were selected as input variables, with DCS as the predicted target. This research optimized the kernel scale, penalty factor, and insensitive loss coefficient of the support vector regression (SVR) model using five swarm intelligent optimization algorithms, leading to the development of five hybrid models. In addition, a statistical DCS prediction equation using multiple linear regression techniques was developed. The performance of the prediction models was comprehensively evaluated using two error indexes and two trend indexes. A sensitivity analysis based on the cosine amplitude method has also been conducted. The results demonstrate that the proposed hybrid SVR-based models consistently provided accurate DCS predictions. Among these models, the SVR model optimized with the chameleon swarm algorithm exhibited the best performance, with metrics indicating its effectiveness, including root mean square error (RMSE) = 3.9675, mean absolute error (MAE) = 2.9673, coefficient of determination (R2) = 0.98631, and variance accounted for (VAF) = 98.634. This suggests that the chameleon swarm algorithm yielded the most optimal results for enhancing SVR models. Notably, impact pressure and characteristic impedance emerged as the two most influential parameters in DCS prediction. This research is anticipated to serve as a reliable reference for estimating the DCS of rocks subjected to freeze-thaw weathering.

Electromagnetic forming (EMF) is a high-speed forming process with significant potential for manufacturing bipolar plates. However, titanium's low conductivity requires the use of a driver sheet in traditional EMF, limiting its formability improvement. This study proposed a novel electromagnetic forming method with independent loading of the magnetic field and current (EMF-ILMFC), enabling separate application of electromagnetic force and Joule heating. The dynamic deformation behavior of ultra-thin titanium was investigated using three different loading methods: quasi-static (QS), electromagnetic bulging with driver sheets (EMB-DS), and electromagnetic bulging with the new method (EMB-ILMFC). The study revealed that the forming limit under EMB-ILMFC was significantly higher than under EMB-DS and QS, with a maximum improvement of 139.1%. Numerical simulations and experimental analyses were conducted to elucidate the thermal effects on formability during dynamic deformation. The results indicate that thermal effects reduce flow stress, extend the duration of inertia and high strain rates, and thereby improve the formability of titanium.

Abstract Background Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease. Its pathogenesis is not fully understood, and early diagnosis is challenging owing to the lack of effective biomarkers. This study aimed to analyze different samples to identify potential biomarkers and therapeutic targets. Methods Microarray datasets of RA, osteoarthritis (OA), and healthy control (HC) were downloaded from the Gene Expression Omnibus database. R software was used to identify differentially expressed genes (DEGs), which were visualized using volcano and heat maps. Venn diagrams, principal component analysis, gene set enrichment analysis, gene ontology, and Kyoto Encyclopedia of genes and genomes were used to analyze the data. A protein–protein interaction network was constructed, and synovial tissues from patients with RA and OA were collected for verification using the collagen‐induced arthritis mouse model. Results More DEGs were found in synovial tissues than in peripheral blood mononuclear cells or fibroblast‐like synoviocytes. Principal component analysis revealed significant differences between the RA and OA samples, highlighting the unique advantages of synovial tissue. Enrichment analysis revealed that metabolic and cytokine signaling pathways play crucial roles in the development of RA. Further analysis of the four synovial datasets identified 54 DEGs, of which signaling lymphocytic activation molecule family (SLAMF) 8 was identified as the key molecule. SLAMF8 levels were increased in the synovial tissue of patients with RA compared to those of patients with OA (0.38 ± 0.19 vs. 12.40 ± 1.66), and SLAMF8 levels were similarly elevated in collagen‐induced arthritis model mice compared with those in the healthy mice (1.13 ± 0.47 vs. 9.05 ± 2.52). Conclusions This study established the unique advantages of synovial tissue for RA research and identified metabolic and cytokine signaling pathways as important for RA development. Thus, SLAMF8 may be a potential therapeutic target for RA.

In current dual porosity/permeability models, there exists a fundamental assumption that the adsorption-induced swelling is distributed uniformly within the representative elementary volume (REV), irrespective of its internal structures and transient processes. However, both internal structures and transient processes can lead to the non-uniform swelling. In this study, we hypothesize that the non-uniform swelling is responsible for why coal permeability in experimental measurements is not only controlled by the effective stress but also is affected by the adsorption-induced swelling. We propose a concept of the swelling triangle composed of swelling paths to characterize the evolution of the non-uniform swelling and serve as a core link in coupled multiphysics. A swelling path is determined by a dimensionless volumetric ratio and a dimensionless swelling ratio. Different swelling paths have the same start and end point, and each swelling path represents a unique swelling case. The swelling path as the diagonal of the triangle represents the case of the uniform swelling while that as the two perpendicular boundaries represents the case of the localized swelling. The paths of all intermediate cases populate inside the triangle. The corresponding relations between the swelling path and the response of coal multiphysics are established by a non-uniform swelling coefficient. We define this method as the triangle approach and corresponding models as swelling path-based ones. The proposed concept and models are verified against a long-term experimental measurement of permeability and strains under constant effective stress. Our results demonstrate that during gas injection, coal multiphysics responses have a close dependence on the swelling path, and that in both future experiments and field predictions, this dependence must be considered.

Abstract Solute carriers (SLCs) regulate transfer of a wide range of molecules across cell membranes using facilitative or secondary active transport. In pregnancy, these transporters, expressed at the placental barrier, are important for delivery of nutrients to the fetus, whilst also limiting entry of potentially harmful substances, such as drugs. In the present study, RNA‐sequencing analysis was used to investigate expression of SLCs in the fetal (embryonic day 19) rat brain, choroid plexus and placenta in untreated control animals and following maternal paracetamol treatment. In the treated group, paracetamol (15 mg/kg) was administered to dams twice daily for 5 days (from embryonic day 15 to 19). In untreated animals, overall expression of SLCs was highest in the placenta. In the paracetamol treatment group, expression of several SLCs was significantly different compared with control animals, with ion, amino acid, neurotransmitter and sugar transporters most affected. The number of SLC transcripts that changed significantly following treatment was the highest in the choroid plexus and lowest in the brain. All SLC transcripts that changed in the placenta following paracetamol treatment were downregulated. These results suggest that administration of paracetamol during pregnancy could potentially disrupt fetal nutrient homeostasis and affect brain development, resulting in major consequences for the neonate and extending into childhood.

This study investigates the potential of using recycled concrete aggregate (RCA) derived from construction and demolition waste as a replacement for natural aggregate in cement-stabilized macadam (CSM). Various substitution rates of RCA, ranging from 0 % to 100 %, were examined with a focus on mechanical properties and durability, including unconfined compressive strength (UCS), indirect tensile strength (ITS), compressive elastic modulus, drying shrinkage, frost resistance, scour resistance, and water damage resistance. Nanoindentation was used to analyze the interfacial transition zones (ITZs) within the CSM. The results show that with a fixed cement content, the UCS, ITS, and compressive elastic modulus decrease as the RCA content increases. Higher RCA content also leads to reduced drying shrinkage, frost resistance, scour resistance, and water damage resistance. Nanoindentation revealed that ITZ1 and ITZ2 have similar thicknesses (∼50 μm), while ITZ3 is about 80 μm thick, highlighting the intricate physical composition of ITZs, including pores, low-density C-S-H, high-density C-S-H, and Ca(OH)2. These findings provide critical insights for optimizing sustainable CSM design using RCA, thereby promoting environmental sustainability in road construction.

Copper tailings are by-products of copper mining, and because of their rich content of SiO2 and Al2O3, it is considered as a possible raw material for the preparation of geopolymer composites. In this paper, the leaching kinetics and reactivity of reactive elements Si and Al in copper tailings under alkali hydrothermal environment are investigated, and the reasons for the difference in reactivity between copper tailings and granulated blast furnace slag (GBFS), fly ash (FA) are also introduced. The results show that the leached Si and Al in the precursor materials are involved in the formation of N/C-A-S-H. Similar to FA and GBFS, the leaching behavior of reactive elements in copper tailings is mainly controlled by the diffusion behavior through the surface product layer. Additionally, the activation energy of Al in the precursor materials is lower than that of Si, which makes the Al-O bond easier to break in alkaline environment, thus causing the leaching rate of Al in the raw materials to be higher than that of Si. Meanwhile, the activation energies of Si and Al in copper tailings are 69.29 kJ/mol and 33.91 kJ/mol, respectively, and higher elemental activation energy makes the efficiency of elemental dissolution lower, which is also the main reason for the lower reactivity of copper tailings. This study aims to elucidate the interrelationships between physicochemical properties, leaching behavior and reactivity of copper tailings.

In crisis management, quickly identifying and helping affected individuals is key, especially when there is limited information about the survivors’ conditions. Traditional emergency systems often face issues with reachability and handling large volumes of requests. Social media has become crucial in disaster response, providing important information and aiding in rescues when standard communication systems fail. Due to the large amount of data generated on social media during emergencies, there is a need for automated systems to process this information effectively and help improve emergency responses, potentially saving lives. Therefore, accurately understanding visual scenes and their meanings is important for identifying damage and obtaining useful information. Our research introduces a framework for detecting damage in social media posts, combining the Bidirectional Encoder Representations from Transformers (BERT) architecture with advanced convolutional processing. This framework includes a BERT-based network for analyzing text and multiple convolutional neural network blocks for processing images. The results show that this combination is very effective, outperforming existing methods in accuracy, recall, and F1 score. In the future, this method could be enhanced by including more types of information, such as human voices or background sounds, to improve its prediction efficiency.

The development of electrical measurement technology has brought high latitude residential electricity consumption data to power companies, which contains the characteristics of users' electricity consumption behavior and provides data support for the behavior classification. In order to improve the efficiency of data feature extraction and the accuracy of electricity consumption behavior identification, a classification model based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering is proposed in this paper. Firstly, the sparse denoising autoencoder (SDAE) and the manually defining electricity consumption characteristic indicators are deployed to extract features from the residential daily electricity consumption data, and then the spectral clustering is employed to classify the extracted electricity consumption characteristics. Secondly, the t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize and analyze the classification results, and on this basis, the secondary classification is implemented to fix the issue of the confused electricity consumption behaviors. Finally, the typical consumption behavior curves are calculated by Gaussian distance weighting method, and the characteristics of power consumption behavior are analyzed and summarized. The proposed approach is evaluated and verified by using the electricity dataset in Fujian, China.

BackgroundInteractions between the immune and metabolic systems may play a crucial role in the pathogenesis of metabolic syndrome-associated rheumatoid arthritis (MetS-RA). The purpose of this study was to discover candidate biomarkers for the diagnosis of RA patients who also had MetS.MethodsThree RA datasets and one MetS dataset were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) were employed to identify hub genes in MetS-RA. Enrichment analysis was used to explore underlying common pathways between MetS and RA. Receiver operating characteristic curves were applied to assess the diagnostic performance of nomogram constructed based on hub genes. Protein−protein interaction, Connectivity Map (CMap) analyses, and molecular docking were utilized to predict the potential small molecule compounds for MetS-RA treatment. qRT-PCR was used to verify the expression of hub genes in fibroblast-like synoviocytes (FLS) of MetS-RA. The effects of small molecule compounds on the function of RA-FLS were evaluated by wound-healing assays and angiogenesis experiments. The CIBERSORT algorithm was used to explore immune cell infiltration in MetS and RA.ResultsMetS-RA key genes were mainly enriched in immune cell-related signaling pathways and immune-related processes. Two hub genes (TYK2 and TRAF2) were selected as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning and proved to have a high diagnostic value (area under the curve, TYK2, 0.92; TRAF2, 0.90). qRT-PCR results showed that the expression of TYK2 and TRAF2 in MetS-RA-FLS was significantly higher than that in non-MetS-RA-FLS (nMetS-RA-FLS). The combination of CMap analysis and molecular docking predicted camptothecin (CPT) as a potential drug for MetS-RA treatment. In vitro validation, CPT was observed to suppress the cell migration capacity and angiogenesis capacity of MetS-RA-FLS. Immune cell infiltration results revealed immune dysregulation in MetS and RA.ConclusionTwo hub genes were identified in MetS-RA, a nomogram for the diagnosis of RA and MetS was established based on them, and a potential therapeutic small molecule compound for MetS-RA was predicted, which offered a novel research perspective for future serum−based diagnosis and therapeutic intervention of MetS-RA.

Metal ions play an essential role in regulating the functions of immune cells by transmitting intracellular and extracellular signals in tumor microenvironment (TME). Among these immune cells, we focused on the impact of metal ions on T cells because they can recognize and kill cancer cells and play an important role in immune-based cancer treatment. Metal ions are often used in nanomedicines for tumor immunotherapy. In this review, we discuss seven metal ions related to anti-tumor immunity, elucidate their roles in immunotherapy, and provide novel insights into tumor immunotherapy and clinical applications.

In this study, surface modification of basalt fibers (BFs) utilizing atmospheric pressure plasma deposition was carried out. Using this one-step deposition approach, three siloxane precursors with different structures including methyltrimethoxysilane (MTMS), hexamethyldisiloxane (HMDSO), and tetramethoxysilane (TMOS) were deposited on BFs surface, respectively. The physicochemical properties of the thin films from three different siloxane compounds are elucidated. In comparison with MTMS-coated sample, HMDSO-coated and TMOS-coated BFs surfaces feature an improved thermal insulation performance. The results demonstrate that atmospheric pressure plasma deposition is an efficient approach to modify flexible materials surface with improved thermal insulation. Moreover, it provides a reference to decide which precursor type is preferred for certain applications.

Abstract Background Overexpression of T-cell immunoglobulin and mucin domain-containing protein 3 (TIM3) is related to the exhaustion of CD8+ tumor-infiltrating lymphocytes (TILs) in diffuse large B-cell lymphoma (DLBCL). However, the mechanism of TIM3-mediated CD8+TILs exhaustion in DLBCL remains poorly understood. Therefore, we aimed to clarify the potential pathway involved in TIM3-mediated CD8+TILs exhaustion and its significance in DLBCL. Methods The expression of TIM3 and its correlation with CD8+TILs exhaustion, the key ligand of TIM3, and the potential pathway of TIM3-mediated CD8+TILs exhaustion in DLBCL were analyzed using single-cell RNA sequencing and validated by RNA sequencing. The biological significance of TIM3-related pathway in DLBCL was investigated based on RNA sequencing, immunohistochemistry, and reverse transcription-quantitative polymerase chain reaction data. Finally, the possible regulatory mechanism of TIM3-related pathway in DLBCL was explored using single-cell RNA sequencing and RNA sequencing. Results Our results demonstrated that CD8+TILs, especially the terminally exhausted state, were the major clusters that expressed TIM3 in DLBCL. Galectin-9, mainly expressed in M2 macrophages, is the key ligand of TIM3 and can induce the exhaustion of CD8+TILs through TIM3/Galectin-9 pathway. Meanwhile, high TIM3/Galectin-9 enrichment is related to immunosuppressive tumor microenvironment, severe clinical manifestations, inferior prognosis, and poor response to CHOP-based chemotherapy, and can predict the clinical efficacy of immune checkpoint blockade therapy in DLBCL. Furthermore, the TIM3/Galectin-9 enrichment in DLBCL may be regulated by the IFN-γ signaling pathway. Conclusions Our study highlights that TIM3/Galectin-9 pathway plays a crucial role in CD8+TILs exhaustion and the immune escape of DLBCL, which facilitates further functional studies and could provide a theoretical basis for the development of novel immunotherapy in DLBCL.

Battery hybridization in hydropower plants is a hydropower flexibility enhancement technology innovation that can potentially expand hydropower's contributions to the grid, but its fundamental characteristics and influencing mechanisms are still unclear. In this paper, primary frequency regulation (PFR) performance and the mechanism of this new technology are studied. A battery hybridized hydropower plant (BH-HPP) model, based on a field-measured-data-based hydropower plant (HPP) model and a verified battery simplified model, is established. Analysis of system stability and dynamics is undertaken for three different battery control strategies by root locus and participation factor methods. Compared to conventional HPPs, analysis results theoretically reveal BH-HPP can not only accelerate system regulation rapidity but also effectively enlarge HPP stability region during PFR process. Time domain simulation verifies the results and further shows synthetic control has better performance among introduced strategies. Besides, initial design ranges of control parameters considering battery capacity and a renewable energy source scenario case are also discussed. This work could provide theoretical support for flexibility enhancement solutions for hydropower systems.

Accurate sensing and localisation are considered as necessary features of future communication systems, including 6G. To harness the full potential of radio frequency (RF) and optical wireless communication (OWC), the localisation of user devices is essential, which further facilitates efficient beam steering, handover, and resource allocation. In this paper, we have considered a practical scenario where users are mobile with random device orientation. A convolutional neural network (CNN) is introduced to estimate the user position and orientation based on the received signal strength (RSS). CNN demonstrates superior performance in optical wireless positioning by proficiently extracting features from only RSS data. According to the simulation results it is observed that, by adjusting the structure of the dataset, a significant improvement in the estimation of the location is obtained in comparison with previous methods. We also consider having the noisy orientation data from the device sensors and investigate localisation performance in such a scenario. Finally, the impact of configuration of access points (APs) on the model is studied. This work demonstrates that a low-complexity accurate localisation, with average error as low as 1.8 cm, is indeed feasible.

Abstract Natural structural materials often possess unique combinations of strength and toughness resulting from their complex hierarchical assembly across multiple length scales. However, engineering such well-ordered structures in synthetic materials via a universal and scalable manner still poses a grand challenge. Herein, a simple yet versatile approach is proposed to design hierarchically structured hydrogels by flow-induced alignment of nanofibrils, without high time/energy consumption or cumbersome postprocessing. Highly aligned fibrous configuration and structural densification are successfully achieved in anisotropic hydrogels under ambient conditions, resulting in desired mechanical properties and damage-tolerant architectures, for example, strength of 14 ± 1 MPa, toughness of 154 ± 13 MJ m−3, and fracture energy of 153 ± 8 kJ m−2. Moreover, a hydrogel mesoporous framework can deliver ultra-fast and unidirectional water transport (maximum speed at 65.75 mm s−1), highlighting its potential for water purification. This scalable fabrication explores a promising strategy for developing bioinspired structural hydrogels, facilitating their practical applications in biomedical and engineering fields.

Wearable human-computer interactions in daily life are increasingly encouraged by the prevalence of intelligent wearables. It poses a demanding requirement of micro-interaction and minimizing social awkwardness. Our previous work demonstrated the feasibility of recognizing silent commands through around-ear biosensors with the limitation of user adaptation. In this work, we ease the limitation by a personalization framework that integrates spectral factorization of signals, temporal confidence rejection and commonly used transfer learning algorithms. Specifically, we first empirically formulate the user adaptation issue by presenting the accuracies of applying transfer learning algorithms to our previous method. Second, we improve the signal-to-noise ratio by proposing the supervised spectral factorization method that learns the amplitude and phase mappings between around-ear signals and the signals of articulated facial muscles. Third, we leverage the time continuity of commands and introduce the time decay into confidence rejection. Finally, extensive experiments have been conducted to evaluate the feasibility and improvements. The results indicate an average accuracy of 92.38% which is significantly larger than solely using transfer learning algorithms. And a comparable accuracy can be achieved with significantly reduced data of new users. The overall performance shows the framework can significantly improve the accuracy of user adaptations. The work would aid a further step toward commercial products for silent command recognition and inspire the solution to the user adaptation challenge of wearable human-computer interactions.

Neurons respond to external stimuli and form functional networks through pairwise interactions. A neural encoding model can describe a single neuron’s behavior, and brain-machine interfaces (BMIs) provide a platform to investigate how neurons adapt, functionally connect, and encode movement. Movement modulation and pairwise functional connectivity are modeled as high-dimensional tuning states, estimated from neural spike train observations. However, accurate estimation of this neural state vector can be challenging as pairwise neural interactions are highly dimensional, change in different temporal scales from movement, and could be non-stationary. We propose an Adam-based gradient descent method to online estimate high-dimensional pairwise neuronal functional connectivity and single neuronal tuning adaptation simultaneously. By minimizing negative log-likelihood based on point process observation, the proposed method adaptively adjusts the learning rate for each dimension of the neural state vectors by employing momentum and regularizer. We test the method on real recordings of two rats performing the brain control mode of a two-lever discrimination task. Our results show that our method outperforms existing methods, especially when the state is sparse. Our method is more stable and faster for an online scenario regardless of the parameter initializations. Our method provides a promising tool to track and build the time-variant functional neural connectivity, which dynamically forms the functional network and results in better brain control.

Gangliosides are glycosphingolipids composed of a sialylated glycan head group and a ceramide backbone. These anionic lipids form lipid rafts and play crucial roles in regulating various proteins involved in signal transduction, adhesion, and cell–cell recognition. Neuroblastoma, a pediatric cancer of the sympathetic nervous system, is treated with intensive chemotherapy, radiation, and an antibody targeting the GD2 ganglioside. Gangliosides are critical in neuroblastoma development and serve as therapeutic targets, making it essential to establish a reliable, rapid, and cost-effective method for profiling gangliosides, particularly one capable of isomeric separation of intact species. In this study, liquid chromatography–mass spectrometry (LC-MS) was optimized using standard gangliosides, followed by the optimization of sphingolipid extraction methods from cell lines by comparing Folch and absolute methanol extraction techniques. Percent recovery and the number of identified sphingolipids were used to evaluate the analytical merits of these methods. A standard gangliosides calibration curve demonstrated excellent linearity (R2 = 0.9961–0.9975). The ZIC-HILIC column provided the best separation of ganglioside GD1 isomers with a 25 min runtime. GD1a elutes before GD1b on the ZIC-HILIC column. Absolute methanol yielded better percent recovery (96 ± 7) and identified 121 different sphingolipids, the highest number between the two extraction methods. The optimized method was applied to profile gangliosides in neuroblastoma (COG-N-683), pancreatic cancer (PSN1), breast cancer (MDA-MB-231BR), and brain tumor (CRL-1620) cell lines. The ganglioside profile of the neuroblastoma cell line COG-N-683 showed an inverse relationship between GD1 and GD2. Ceramide, Hex1Cer, GM1, and GM3 were highly abundant in CRL-1620, PSN1, and MDA-MB-231BR, respectively. These results suggest that our method provides a sensitive, reliable, and high-throughput workflow for ganglioside profiling across different cell types.

Abstract Background Spinal cord injury (SCI) remains a significant health concern, with limited available treatment options. This condition poses significant medical, economic, and social challenges. SCI is typically categorized into primary and secondary injuries. Inflammation, oxidative stress, scar formation, and the immune microenvironment impede axon regeneration and subsequent functional restoration. Numerous studies have shown that the destruction of the blood–brain barrier (BBB) and microvessels is a crucial factor in severe secondary injury. Additionally, reactive oxygen species (ROS)-induced lipid peroxidation significantly contributes to endothelial cell death. Pericytes are essential constituents of the BBB that share the basement membrane with endothelial cells and astrocytes. They play a significant role in the establishment and maintenance of BBB. Results Immunofluorescence staining at different time points revealed a consistent correlation between pericyte coverage and angiogenesis, suggesting that pericytes promote vascular repair via paracrine signaling. Pericytes undergo alterations in cellular morphology and the transcriptome when exposed to hypoxic conditions, potentially promoting angiogenesis. We simulated an early ischemia-hypoxic environment following SCI using glucose and oxygen deprivation and BBB models. Co-culturing pericytes with endothelial cells improved barrier function compared to the control group. However, this enhancement was reduced by the exosome inhibitor, GW4869. In vivo injection of exosomes improved BBB integrity and promoted motor function recovery in mice following SCI. Subsequently, we found that pericyte-derived exosomes exhibited significant miR-210-5p expression based on sequencing analysis. Therefore, we performed a series of gain- and loss-of-function experiments in vitro. Conclusion Our findings suggest that miR-210-5p regulates endothelial barrier function by inhibiting JAK1/STAT3 signaling. This process is achieved by regulating lipid peroxidation levels and improving mitochondrial function, suggesting a potential mechanism for restoration of the blood-spinal cord barrier (BSCB) after SCI.

Sulfate transporters (SULTRs) are essential for the transport and absorption of sulfate in plants and serve as critical transport proteins within the sulfur metabolism pathway, significantly influencing plant growth, development, and stress adaptation. A bioinformatics analysis of SULTR genes in soybean was performed, resulting in the identification and classification of twenty-eight putative GmSULTRs into four distinct groups. In this study, the characteristics of the 28 GmSULTR genes, including those involved in collinearity, gene structure, protein motifs, cis-elements, tissue expression patterns, and the response to abiotic stress and plant hormone treatments, were systematically analyzed. This study focused on conducting a preliminary functional analysis of the GmSULTR3;1a gene, wherein a high expression level of GmSULTR3;1a in the roots, stems, and leaves was induced by a sulfur deficiency and GmSULTR3;1a improved the salt tolerance. A further functional characterization revealed that GmSULTR3;1a-overexpressing soybean hairy roots had higher SO42−, GSH, and methionine (Met) contents compared with the wild-type (WT) plant. These results demonstrate that the overexpression of GmSULTR3;1a may promote the sulfur assimilation metabolism and increase the content of sulfur-containing amino acids in plants.

Abstract Turbines are generally oversimplified in conventional algorithms for steady‐state analysis of hydropower plant (HPP) networks. Moreover, conventional algorithms are hardly suitable for serial HPPs. In this paper, a modified global gradient algorithm (MGGA) is developed by extending turbine models and optimizing turbine interpolation. First, the nonlinear model of turbines is established with the proposed variable resistance factor and added to the global matrix. Then turbine unit quantity interpolation is proposed to compute the turbine operating parameters. Finally, turbine links and pipes can be iterated synchronously, and the global matrix is solved using the Newton–Raphson method. Characteristics of MGGA are evaluated by employing two test HPPs, including rapidity of convergence, computational stability of turbine, and applicability for serial HPPs. The results show that: (a) The iterative numbers of the MGGA are 0.72 and 4.98 times on average less than that of CA1 and CA2 for the accuracy of 10−6 in 400 operating conditions, even though MGGA has an obvious hysteretic nature; (b) Only MGGA can successfully calculate the extreme cases that large head losses in HPP networks; (c) Although fixed head nodes are not present between turbine links, such as serial HPPs, MGGA still works.

Surface defect detection is a critical task in the manufacturing industry to ensure product quality and machining efficiency. Image-based precise defect detection faces significant challenges due to defects lacking fixed shapes and the detection being heavily influenced by lighting conditions. Addressing the efficiency demands of defect detection algorithms, often deployed on embedded devices, and the highly imbalanced pixel ratio between foreground and background images, this paper introduces a multi-scale fusion staged U-shaped convolutional neural network (DEU-Net). The network provides segmentation results for defect anomalies while indicating the probability of defect presence. It enables the model to train with fewer parameters, a crucial requirement for practical applications. The proposed model achieves an MIoU of 66.94 and an F1 score of 74.89 with lower Params (36.675) and Flops (19.714). Comparative analysis with FCN, U-Net, Deeplab v3+, U-Net++, Attention U-Net, and Trans U-Net demonstrates the superiority of the proposed approach in surface defect detection.

Underwater detection faces uncomfortable illumination conditions, and traditional optical images sensitive to intensity often cannot work well in these conditions. Polarization imaging is a good solution for underwater detection under adverse lighting conditions. However, the process of obtaining polarization information causes it to be more sensitive to noise; serious noise reduces the quality of polarized images and subsequent performance in advanced visual tasks. Unfortunately, the flourishing low-light image enhancement methods applied to intensity images have not demonstrated satisfactory performance when transferred to polarized images. In this paper, we propose a low-light image enhancement paradigm based on the antagonistic properties of polarization parameters. Furthermore, we develop a dual-branch network that relies on a gradient residual dense feature extraction module (GRD) designed for polarized image characteristics and polarization loss, effectively avoiding noise introduced during the direct amplification of brightness, and capable of restoring target contour details. To facilitate a data-driven learning method, we propose a simulation method for underwater low-light polarized images. Extensive experimental results on real-world datasets demonstrate the effectiveness of our proposed approach and its superiority against other state-of-the-art methods.

Abstract Binding of therapeutics to proteins in blood plasma is important in influencing their distribution as it is their free (unbound) form that is able to cross cellular membranes to enter tissues and exert their actions. The concentration and composition of plasma proteins vary during pregnancy and development, resulting in potential changes to drug protein binding. Here, we describe an ultrafiltration method to investigate the extent of protein binding of six drugs (digoxin, paracetamol, olanzapine, ivacaftor, valproate and lamotrigine) and two water soluble inert markers (sucrose and glycerol) to plasma proteins from pregnant and developing rats. Results showed that the free fraction of most drugs was lower in the non-pregnant adult plasma where protein concentration is the highest. However, plasma of equivalent protein concentration to younger pups obtained by diluting adult plasma did not always exhibit the same extent of drug binding, reinforcing the likelihood that both concentration and composition of proteins in plasma influence drug binding. Comparison between protein binding and brain drug accumulation in vivo revealed a correlation for some drugs, but not others. Results suggests that plasma protein concentration should be considered when using medications in pregnant and paediatric patients to minimise potential for fetal and neonatal drug exposure.

Abstract Background Apparent permeability of the blood brain barrier to hydrophilic markers has been shown to be higher in the developing brain. Apart from synthesis in situ, any substance detected in the brain parenchyma can originate from two sources: directly through blood vessels of brain vasculature and/or indirectly by entry from the cerebrospinal fluid (CSF) after transfer across the choroid plexuses. The relative quantitative contribution of these two routes to the overall brain entry remains unclear. Methods In rats at embryonic day 16, 19 and postnatal day 4 and young adults, a small (sucrose, mw. 342 Da) or a large (dextran, mw. 70 kDa) radiolabelled hydrophilic marker was injected intravenously for very short periods of time (30 s to 5 min) before collection of plasma, cerebrospinal fluid (CSF) and brain samples. Results are presented as concentration ratios between radioactivity measured in CSF or brain and that in plasma (%). Results The dextran brain/plasma ratio five minutes post injection was similar (2–4%) from E16 to adulthood whereas the sucrose brain/plasma ratio was significantly higher in fetal brains, but was comparable to dextran values in the adult. Sucrose CSF/plasma ratios were also significantly higher in fetal animals and decreased with age. In very short experiments involving fetal animals, entry of sucrose into the CSF after only 30 s was similar to that of dextran and both markers showed similar brain/plasma ratios. Conclusions In the developing brain the apparent higher brain entry of a small hydrophilic marker such as sucrose can be attributed to its higher entry into the CSF and subsequent diffusion into the brain. By contrast, movement of a larger marker like 70 kDa dextran is restricted firstly by choroid plexus epithelial tight junctions and secondly by specialised junctions in the neuroependymal interface between the CSF and brain. Brain/plasma ratios of 70 kDa dextran were similar in fetal and adult rats. Therefore 70 kDa dextran should be considered an appropriate marker if brain residual vascular space is to be measured, especially in younger animals.

Abstract Herein, we design and synthesize a series of oligomers [Ir(ppy)2(dabpy)‐ODPA]n (D1‐n) by copolymerization of [Ir(ppy)2(dabpy)][PF6] (D2) with 4,4′‐Oxydiphthalic anhydride (ODPA), to resolve the problem of simultaneous improvement of stability and activity of classical iridium complex for photocatalytic water‐splitting. Fourier‐transform infrared spectroscopy, X‐ray photoelectron spectroscopy, solid‐state nuclear magnetic resonance, and gel permeation chromatography results indicate that the degree of polymerization (n) of D1‐n could be tuned by the synthesis method. The best photocatalytic performance is reached by D1‐n with n at of 2 and/or 3 (D1‐2/3), which exhibits a photocatalytic lifetime up to 676 h and a photocatalytic hydrogen evolution of 162055.1 μmol·g‐1. Compared with classical iridium complex D2, the photocatalytic lifetime of D1‐2/3 is about 38 times longer and the photocatalytic activity is 1.3 times higher. Further increase of n leads to a decrease in both photocatalytic lifetime and activity. According to the spectroscopic characterizations, photoelectrochemical experiments, and density functional theory calculation, the significantly enhanced photocatalytic performance of D1‐2/3 originates from the oligomeric structure. The oligomer chain of D1‐2/3 with suitable length acts as a large steric hindrance to reduce the undesired photoinduced decomposition and prolong its lifetime. The possible coupling of adjacent Ir complexes in D1‐2/3 lowers the energy gap and increases the utilization of visible light, which overcomes the adverse effect of large steric hindrance and finally improve the activity. This work first provides a simple strategy for constructing oligomeric Ir photosensitizers to simultaneously achieve long lifetime and high activity, it will lay the foundation for the design of highly efficient photosensitizers in the future.

Concrete made from rice husk ash (RHA) is stronger and more durable than normal concrete. It can also help reduce greenhouse gas emissions. A feasible prediction model to rapidly determine the compressive strength of RHA concrete can save resources and time, and the properties and characteristics of RHA concrete can be more accurately determined. In this study, the compressive strength of RHA concrete is predicted by using support vector regression (SVR) in conjunction with three optimization algorithms, namely firefly algorithm (FA), particle swarm optimization (PSO), and grey wolf optimization (GWO). The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), a10-index and adjusted R2 are used to assess the predictive accuracy of models. The results show that all the three optimization algorithms can significantly improve the prediction performance of support vector regression. The prediction accuracy is also substantially higher than that of the classical machine learning models like the random forest (RF) model and the back propagation neural network (BPNN) model. The FA-SVR model with R2 values of 0.9544 and 0.9614, adjusted R2 values of 0.9530 and 0.9560, RMSE values of 3.7506 and 3.6571, MAE values of 2.2880 and 3.0732, and a10-index values of 0.8584 and 0.8793 corresponding to the training and test sets, respectively. The FA-SVR model outperforms the other two hybrid models in terms of prediction performance. The sensitivity analysis reveals that the fine aggregate content is the key factor in predicting the compressive strength of RHA concrete.

Spinal cord ischemia-reperfusion (IR) injury (SCIRI) is a significant secondary injury that causes damage to spinal cord neurons, leading to the impairment of spinal cord sensory and motor functions. Excessive reactive oxygen species (ROS) production is considered one critical mechanism of neuron damage in SCIRI. Nonetheless, the molecular mechanisms underlying the resistance of neurons to ROS remain elusive. Our study revealed that the deletion of Git1 in mice led to poor recovery of spinal cord motor function after SCIRI. Furthermore, we discovered that Git1 has a beneficial effect on neuron resistance to ROS production. Mechanistically, Git1 interacted with PGK1, regulated PGK1 phosphorylation at S203, and affected the intermediate products of glycolysis in neurons. The influence of Git1 on glycolysis regulates the dimerization of Keap1, which leads to changes in Nrf2 ubiquitination and plays a role in resisting ROS. Collectively, we show that Git1 regulates the Keap1/Nrf2 axis to resist ROS in a PGK1-dependent manner and thus is a potential therapeutic target for SCIRI.

Abstract Developing cost–benefit and high‐performance non‐noble metal oxygen reduction reaction (ORR) electrocatalysts is highly imperative for wide applications of renewable energy conversion devices. Herein, a one stone two birds phosphorization strategy has been proposed to synthesize hollow structured Cu/Cu3P heterogeneous nanoparticles supported on N, P co‐doped carbon (Cu/Cu3P@NP‐Cs). The optimized Cu/Cu3P@NP‐C‐900 features high ORR performance under both alkaline and acidic conditions. Moreover, the Cu/Cu3P@NP‐C‐900‐drivened Zn‐air battery exhibits a substantially higher power density output (148.2 mW cm−2) and stronger charge–discharge stability (300 h, 1805 cycles) than those of Pt/C‐equipped counterpart. The cross‐interface electron transfer from Cu3P to Cu effectively regulates the d‐band center of Cu/Cu3P, thereby leading to the balanced adsorption/desorption energy of oxygen species. Meanwhile, the hollow structure maximizes the exposure of accessible active centers, resulting in much accelerated ORR kinetics. This work proposes an innovative insight for developing hollow hetero‐structured catalysts to improve ORR performance.

As an essential micronutrient, manganese plays an important role in the physiological process and immune process. In recent decades, cGAS-STING pathway, which can congenitally recognize exogenous and endogenous DNA for activation, has been widely reported to play critical roles in the innate immunity against some important diseases, such as infections and tumor. Manganese ion (Mn2+) has been recently proved to specifically bind with cGAS and activate cGAS-STING pathway as a potential cGAS agonist, however, is significantly restricted by the low stability of Mn2+ for further medical application. As one of the most stable forms of manganese, manganese dioxide (MnO2) nanomaterials have been reported to show multiple promising functions, such as drug delivery, anti-tumor and anti-infection activities. More importantly, MnO2 nanomaterials are also found to be a potential candidate as cGAS agonist by transforming into Mn2+, which indicates their potential for cGAS-STING regulations in different diseased conditions. In this review, we introduced the methods for the preparation of MnO2 nanomaterials as well as their biological activities. Moreover, we emphatically introduced the cGAS-STING pathway and discussed the detailed mechanisms of MnO2 nanomaterials for cGAS activation by converting into Mn2+. And we also discussed the application of MnO2 nanomaterials for disease treatment by regulating cGAS-STING pathway, which might benefit the future development of novel cGAS-STING targeted treatments based on MnO2 nanoplatforms.

IntroductionBipolaris sorokiniana is the popular pathogenic fungi fungus which lead to common root rot and leaf spot on wheat. Generally, chemical fungicides are used to control diseases. However, the environmental pollution resulting from fungicides should not be ignored. It is important to study the mode of antagonistic action between biocontrol microbes and plant pathogens to design efficient biocontrol strategies.ResultsAn antagonistic bacterium DB2 was isolated and identified as Bacillus amyloliquefaciens. The inhibition rate of cell-free culture filtrate (CF, 20%, v/v) of DB2 against B. sorokiniana reached 92.67%. Light microscopy and scanning electron microscopy (SEM) showed that the CF significantly altered the mycelial morphology of B. sorokiniana and disrupted cellular integrity. Fluorescence microscopy showed that culture filtrate destroyed mycelial cell membrane integrity, decreased the mitochondrial transmembrane potential, induced reactive oxygen species (ROS) accumulation, and nuclear damage which caused cell death in B. sorokiniana. Moreover, the strain exhibited considerable production of protease and amylase, and showed a significant siderophore and indole-3-acetic acid (IAA) production. In the detached leaves and potted plants control assay, B. amyloliquefacien DB2 had remarkable inhibition activity against B. sorokiniana and the pot control efficacy was 75.22%. Furthermore, DB2 suspension had a significant promotion for wheat seedlings growth.ConclusionB. amyloliquefaciens DB2 can be taken as a potential biocontrol agent to inhibit B. sorokiniana on wheat and promote wheat growth.

Disruption of the blood-spinal cord barrier (BSCB) leads to inflammatory cell infiltration and neural cell death, thus, contributing to poor functional recovery after spinal cord injury (SCI). Previous studies have suggested that Sirtuin 1 (SIRT1), an NAD+-dependent class III histone deacetylase, is abundantly expressed in endothelial cells and promotes endothelial homeostasis. However, the role of SIRT1 in BSCB function after SCI remains poorly defined. Here, we report that SIRT1 is highly expressed in spinal cord endothelial cells, and its expression significantly decreases after SCI. Using endothelial cell-specific SIRT1 knockout mice, we observed that endothelial cell-specific knockout of SIRT1 aggravated BSCB disruption, thus, resulting in widespread inflammation, neural cell death and poor functional recovery after SCI. In contrast, activation of SIRT1 by the agonist SRT1720 had beneficial effects. In vitro, knockdown of SIRT1 exacerbated IL-1β-induced endothelial barrier disruption in bEnd.3 cells, whereas overexpression of SIRT1 was protective. Using RNA-seq and IP/MS analysis, we identified p66Shc, a redox protein, as the potential target of SIRT1. Further studies demonstrated that SIRT1 interacts with and deacetylates p66Shc, thereby attenuating oxidative stress and protecting endothelial barrier function. Overall, our results indicate that SIRT1 decreases endothelial ROS production and attenuates BSCB disruption by deacetylating p66Shc after SCI, and suggest that SIRT1 activation has potential as a therapeutic approach to promote functional recovery against BSCB disruption following SCI.

Students’ college satisfaction is an important part of measuring the quality of college teaching. The admission of college entrance exam is the first step for college students to enter colleges and corresponding majors. Whether they affect students’ college satisfaction after enrollment is related to the formulation and adjustment of college admission strategies and training methods. This paper is based on data from students in colleges in Beijing enrolled in the fall of 2006 and 2008 and adopts propensity score matching to analyze the influence of the admitted characteristics of college entrance exams, such as whether they were accepted by their first choice. We also further explored the heterogeneity. The empirical results show that whether the student was admitted to the first-choice college has a significant positive impact on overall satisfaction and academic and nonacademic satisfaction, while whether the student was admitted to the first-choice major has no significant impact on nonacademic satisfaction. In addition, making an independent major choice has a positive effect on the improvement of overall satisfaction and academic satisfaction, and the impact on overall satisfaction is even greater than that of being admitted to the first-choice major. The impact of the admission characteristics of college entrance examinations on the satisfaction of students in liberal arts and science and students of different types of colleges and universities presents different characteristics.

Large-scale integration of electric vehicles (EVs) into the city system for charging will affect the operation of both traffic and distribution networks. An electric vehicle navigation and cluster dispatch model is proposed for improving the overall charging efficiency of EVs on the transportation network and increasing the voltage level of the distribution network. First, a simplified model of vehicles and traffic road network is established, and a cell transmission model (CTM) is used to simulate the real traffic network. The traffic system takes into account charging EVs, discharging EVs, and other vehicles, and traffic congestion is considered. Then, a coupled model of the traffic–power system is built for the orderly charging of electric vehicles upon arrival at a charging station. The model considers the coupling of the two systems on a time scale, and the charging/discharging power at each charging station node is controlled. The validity of the model is verified in a coupled system of 357 cell traffic network and modified IEEE33 nodes. The results show that the proposed model can ensure good guarantee of the distribution network voltage reliability and reveal the scheduling process of the traffic network. The proposed model also provides a reference for planning of charging stations in the distribution network.

Highly active and cost-efficient electrocatalysts for oxygen reduction reaction (ORR) are significant for developing renewable energy conversion devices. Herein, a nanocomposite Fe/ZnS-SNC electrocatalyst with an FeS and ZnS heterojunction on N,S-codoped carbon has been fabricated via a facile one-step sulfonating of the pre-designed Zn- and Fe-organic frameworks. Benefitting from the electron transfer from FeS to adjacent ZnS at the heterointerfaces, the optimized Fe/ZnS-SNC900 catalyst exhibits excellent ORR performances, featuring the half-wave potentials of 0.94 V and 0.81 V in alkaline and acidic media, respectively, which is competitive with the commercial 20 wt.% Pt/C (0.87 and 0.76 V). The flexible Zn-air battery equipping Fe/ZnS-SNC900 affords a higher open-circuit voltage (1.45 V) and power density of 30.2 mW cm−2. Fuel cells assembled with Fe/ZnS-SNC900 as cathodic catalysts deliver a higher power output of 388.3 and 242.8 mW cm−2 in H2-O2 and -air conditions. This work proposes advanced heterostructured ORR electrocatalysts that effectively promote renewable energy conversions.

Reinforcement-learning (RL)-based brain-machine interfaces (BMIs) interpret dynamic neural activity into movement intention without patients’ real limb movements, which is promising for clinical applications. A movement task generally requires the subjects to reach the target within one step and rewards the subjects instantaneously. However, a real BMI scenario involves tasks that require multiple steps, during which sensory feedback is provided to indicate the status of the prosthesis, and the reward is only given at the end of the trial. Actually, subjects internally evaluate the sensory feedback to adjust motor activity. Existing RL-BMI tasks have not fully utilized the internal evaluation from the brain upon the sensory feedback to guide the decoder training, and there lacks an effective tool to assign credit for the multi-step decoding task. We propose first to extract intermediate guidance from the medial prefrontal cortex (mPFC) to assist the learning of multi-step decoding in an RL framework. To effectively explore the neural-action mapping in a large state-action space, a temporal difference (TD) method is incorporated into quantized attention-gated kernel reinforcement learning (QAGKRL) to assign the credit over the temporal sequence of movement, but also discriminate spatially in the Reproducing Kernel Hilbert Space (RKHS). We test our approach on the data collected from the primary motor cortex (M1) and the mPFC of rats when they brain control the cursor to reach the target within multiple steps. Compared with the models which only utilize the final reward, the intermediate evaluation interpreted from the mPFC can help improve the prediction accuracy by 10.9% on average across subjects, with faster convergence and more stability. Moreover, our proposed algorithm further increases 18.2% decoding accuracy compared with existing TD-RL methods. The results reveal the possibility of achieving better multi-step decoding performance for more complicated BMI tasks.

Impedance/admittance models (IM/AMs) have been widely used to analyze the small-signal stability of grid-tied power electronic devices, such as the voltage source converter (VSC). They can be either derived from theoretical analysis under white-box conditions, where all parameters and control structures are fully known, or measured based on experiments under black-box conditions, where the topology and parameters of the controllers are completely unknown. As the IM/AMs depend on specific operating conditions, it is highly desirable to develop fast algorithms for IM/AM prediction (or estimation) under the black-box and variable-operating-point conditions. This article extends the nearly-decoupled AM method for sequence AMs proposed recently by Liu et al to fit any unknown control structure, including not only grid-following VSC, but also grid-forming VSC. It is, therefore, referred to as the fully-decoupled IM (FDIM) method. Furthermore, a curve fitting method for the transfer function is proposed to expedite the algorithm, based on the information of a few disturbance frequencies only. Finally, the algorithm is verified by wide simulations and experiments under different situations, including the direct-drive wind turbine generator. The whole approach is expected to be broadly applicable to the stability analysis of power-electronic-based power systems under variable operating conditions.

Abstract Subtalar osteoarthritis (STOA) is often secondary to chronic ankle sprains, which seriously affects the quality of life of patients. Due to its etiology and pathogenesis was not studied equivocally yet, there is currently a lack of effective conservative treatments. Although they have been used for tissue repair, platelet-rich plasma-derived exosomes (PRP-Exo) have the disadvantage of low retention and short-lived therapeutic effects. This study aimed to determine whether incorporation of PRP-Exo in thermosensitive hydrogel (Gel) increased their retention in the joint and thereby playing a therapeutic role on STOA due to chronic mechanical instability established by transecting lateral ligaments (anterior talofibular ligament (ATFL)/calcaneal fibular ligament (CFL)). PRP-Exo incorporated Gel (Exo-Gel) system, composed of Poloxamer-407 and 188 mixture-based thermoresponsive hydrogel matrix in an optimal ratio, was determined by its release ability of Exo and rheology of Gel response to different temperature. The biological activity of Exo-Gel was evaluated in vitro, and the therapeutic effect of Exo-Gel on STOA was evaluated in vivo. Exo released from Exo-Gel continuously for 28 days could promote the proliferation and migration of mouse bone mesenchymal stem cells (mBMSCs) and chondrocytes, at the same time enhance the chondrogenic differentiation of mBMSCs, and inhibit inflammation-induced chondrocyte degeneration. In vivo experiments confirmed that Exo-Gel increased the local retention of Exo, inhibited the apoptosis and hypertrophy of chondrocytes, enhanced their proliferation, and potentially played the role in stem cell recruitment to delay the development of STOA. Thus, Delivery of PRP-Exo incorporated in thermosensitive Gel provides a novel approach of cell-free therapy and has therapeutic effect on STOA. Graphical Abstract

Abstract Electrical explosion, characterized by ultrafast atomization and quenching rate (dT/dt ≈ 1010–1012 K s–1) of the sample, is a unique approach for “one‐step” synthesis of nanomaterials. Experiments are carried out with layered graphite and Bi2Se3 under the action of electrical explosion in a confined reaction tube. High‐speed photography and electrophysical diagnostics are applied to characterize dynamic processes. SEM and EDS are used to characterize surface micro‐morphology of reaction products. The layered materials are first exfoliated to thin nanosheets/nanocrystals by shock waves and turbulent flow of the explosion. As the ionized explosion products (>10 000 K) contacts the sample, intense heat transfer happens, simultaneously atomizing the sample and quenching the plasmas. As a result, nanoparticles grow on the surface of thin sheets, forming “dot‐sheet” structure. The size distribution of the nanoparticles typically ranges from 10 to 100 nm, following Log‐normal distribution. The dotted graphite nanosheets gather together and form a stacked/cabbage‐like structure. By contrast, Bi2Se3 case accompanies with chemical reactions, causing surface corrosion and showing more possibilities: nanocrystals and nanotubes growth on different areas of the sample.

Manganese (Mn), a nutrient inorganic trace element, is necessary for a variety of physiological processes of animal body due to their important roles in oxidative regulation effects and other aspects of activities. Moreover, manganese ion (Mn2+) has widely reported to be crucial for the regulations of different immunological responses, thus showing promising application as potential adjuvants and immunotherapeutics. Taking the advantages of Mn-based biological and immunological activities, Manganese dioxide nanoparticles (MnO2 NPs) are a new type of inorganic nanomaterials with numerous advantages, including simple preparation, low cost, environmental friendliness, low toxicity, biodegradable metabolism and high bioavailability. MnO2 NPs, as a kind of drug carrier, have also shown the ability to catalyze hydrogen peroxide (H2O2) to produce oxygen (O2) under acidic conditions, which can enhance the efficacy of radiotherapy, chemotherapy and other therapeutics for tumor treatment by remodeling the tumor microenvironment. More importantly, MnO2 NPs also play important roles in immune regulations both in innate and adaptive immunity. In this review, we summarize the biological activities of Manganese, followed by the introduction for the biological and medical functions and mechanisms of MnO2 NPs. What’s more, we emphatically discussed the immunological regulation effects and mechanisms of MnO2 NPs, as well as their potentials to serve as adjuvants and immunomodulators, which might benefit the development of novel vaccines and immunotherapies for more effective disease control.

Background: Olanzapine is used to treat schizophrenia and bipolar disorder in women of childbearing age. Continuation of psychotropic medications throughout pregnancy and lactation is often required as cessation could be dangerous for both mother and child. However, there is a lack of information on the transfer of these drugs into the developing brain. Methods: Sprague Dawley rats at three developmental ages: embryonic day E19, postnatal day P4 and non-pregnant adult females were administered unlabelled or radiolabelled (3H) olanzapine (0.15 mg/kg) either as monotherapy or in combination with each of seven other common medications. Similar injections were administered to pregnant E19 females to investigate placental transfer. Olanzapine in plasma, cerebrospinal fluid (CSF) and brain was measured by liquid scintillation counting after a single dose (acute) or following 5 days of treatment (prolonged). Results: Olanzapine entry into brain and CSF was not age-dependent. Prolonged olanzapine treatment reduced placental transfer from 53% to 46% (p

Autophagy, one of the major intracellular degradation systems, plays an important role in maintaining normal cellular physiological functions and protecting organisms from different diseases. Selenium (Se), an essential trace element, is involved in many metabolic regulatory signaling events and plays a key role in human health. In recent years, selenium nanoparticles (Se NPs) have attracted increasing attentions in biomedical field due to their low toxicity, high bioavailability and high bioactivity. Taking the advantage of their advanced biological activities, Se NPs can be used alone as potential therapeutic agents, or combine with other agents and served as carriers for the development of novel therapeutics. More interestingly, Se NPs have been widely reported to affect autophagy signaling, which therefor allow Se NPs to be used as potential therapeutic agents against different diseases. Here, this review suggested the relationships between Se and autophagy, followed by the trends and recent progresses of Se NPs for autophagy regulation in different diseased conditions. More importantly, this work discussed the roles and potential mechanisms of Se NPs in autophagy regulating, which might enhance our understanding about how Se NPs regulate autophagy for potential disease treatment. This work is expected to promote the potential application of Se NPs as novel autophagy regulators, which might benefit the development of novel autophagy associated therapeutics.

ObjectiveWe aimed to describe the trends and influence factors in the prevalence, awareness, treatment, and control of hypertension among US Adults from 1999 to 2018.MethodsWe utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning ten survey cycles (n = 53,496). Prevalence, awareness, treatment, and control of hypertension were calculated using survey weights. Joinpoint regression and survey-weighted generalized linear models were used to analyze trends and influence factors, respectively.ResultsThe estimated prevalence of hypertension increased significantly from 33.53% to 40.58% (AAPC = 0.896, P = 0.002) during 1999-2018 with dropping rate of newly diagnosed hypertension from 8.62% to 4.82% before 2014 (APC = -4.075, P = 0.001), and then rose to 7.51% in 2018 (APC = 12.302, P = 0.126). Despite modest improvements or stability in the awareness, treatment, and control since 1999, the latter two remained inadequate in 2018 at 59.52% and 51.71%. There was an uptrend in the use of angiotensin-converting enzyme inhibitors (from 24.02% to 45.71%) and angiotensin receptor blockers (from 20.22% to 38.38%), and downtrend in β-blocker (from 12.71% to 4.21%). Men were at higher risk of incidence, un-awareness, un-treatment, and un-control for hypertension. Lower income and education were associated with susceptibility to hypertension, while being married was favorable for treatment and control. Optimal health reduced the incidence of hypertension, and increased the awareness and treatment.ConclusionAlthough the rate of newly diagnosed hypertension has declined slightly since 2010 in the US, the prevalence of hypertension is increasing, and treatment and control rates remain inadequate. To manage hypertension effectively, we need to focus on screening and prevention for high-risk populations, while advocating for optimal health to improve the burden of hypertension.

IntroductionAs a deadly disease induced by Mycobacterium tuberculosis (Mtb), tuberculosis remains one of the top killers among infectious diseases. The low intracellular Mtb killing efficiency of current antibiotics introduced the long duration anti-TB therapy in clinic with strong side effects and increased drug-resistant mutants. Therefore, the exploration of novel anti-TB agents with potent anti-TB efficiency becomes one of the most urgent issues for TB therapies. MethodsHere, we firstly introduced a novel method for the preparation of zinc oxide-selenium nanoparticles (ZnO-Se NPs) by the hybridization of zinc oxide and selenium to combine the anti-TB activities of zinc oxide nanoparticles and selenium nanoparticles. We characterized the ZnO-Se NPs by dynamic laser light scattering and transmission electron microscopy, and then tested the inhibition effects of ZnO-Se NPs on extracellular Mtb by colony-forming units (CFU) counting, bacterial ATP analysis, bacterial membrane potential analysis and scanning electron microscopy imaging. We also analyzed the effects of ZnO-Se NPs on the ROS production, mitochondrial membrane potential, apoptosis, autophagy, polarization and PI3K/Akt/mTOR signaling pathway of Mtb infected THP-1 macrophages. At last, we also tested the effects of ZnO-Se NPs on intracellular Mtb in THP-1 cells by colony-forming units (CFU) counting. ResultsThe obtained spherical core-shell ZnO-Se NPs with average diameters of 90 nm showed strong killing effects against extracellular Mtb, including BCG and the virulent H37Rv, by disrupting the ATP production, increasing the intracellular ROS level and destroying the membrane structures. More importantly, ZnO-Se NPs could also inhibit intracellular Mtb growth by promoting M1 polarization to increase the production of antiseptic nitric oxide and also promote apoptosis and autophagy of Mtb infected macrophages by increasing the intracellular ROS, disrupting mitochondria membrane potential and inhibiting PI3K/Akt/mTOR signaling pathway. DiscussionThese ZnO-Se NPs with synergetic anti-TB efficiency by combining the Mtb killing effects and host cell immunological inhibition effects were expected to serve as novel anti-TB agents for the development of more effective anti-TB strategy.

Introduction: Radiation-induced heart disease (RIHD), characterized by cardiac dysfunction and myocardial fibrosis, is one of the most common complications after cardiothoracic radiotherapy. Dang Gui Bu Xue Tang (DBT) is a conventional Chinese herb decoction composed of Radix Astragali membranaceus (RAM) and Radix Angelicae sinensis (RAS) at a ratio of 5:1, famous for its “blood-nourishing” effect. In this study, we aimed to investigate the cardioprotective effect of DBT on RIHD.Methods: C57BL mice at 8 weeks of age were divided into five groups, namely Control, Radiation, RDBT51 (Radiation with DBT, RAM:RAS = 5:1), RDBT11 (Radiation with DBT, RAM:RAS = 1:1), and RDBT15 (Radiation with DBT, RAM:RAS = 1:5).Results: We mainly found that radiation in the cardiothoracic region led to significant left ventricular systolic dysfunction, myocardial fibrotic lesions and cardiac injury accompanied by abnormally increased myocardial HMGB1 protein levels. Administration of conventional DBT significantly ameliorated left ventricular systolic dysfunction, alleviated myocardial fibrosis, and counteracted cardiac injury, all of which supported the protective effect of DBT on RIHD, involving upregulation of myocardial Nrf2 protein levels and downregulation of HMGB1 protein levels as underlying mechanisms.Conclusions: DBT exerts a significant protective effect on RIHD, and the Nrf2/ HMGB1 pathway probably plays an important role in this protective effect.

Abstract Background Little is known about the extent of drug entry into developing brain, when administered to pregnant and lactating women. Lithium is commonly prescribed for bipolar disorder. Here we studied transfer of lithium given to dams, into blood, brain and cerebrospinal fluid (CSF) in embryonic and postnatal animals as well as adults. Methods Lithium chloride in a clinically relevant dose (3.2 mg/kg body weight) was injected intraperitoneally into pregnant (E15–18) and lactating dams (birth-P16/17) or directly into postnatal pups (P0–P16/17). Acute treatment involved a single injection; long-term treatment involved twice daily injections for the duration of the experiment. Following terminal anaesthesia blood plasma, CSF and brains were collected. Lithium levels and brain distribution were measured using Laser Ablation Inductively Coupled Plasma-Mass Spectrometry and total lithium levels were confirmed by Inductively Coupled Plasma-Mass Spectrometry. Results Lithium was detected in blood, CSF and brain of all fetal and postnatal pups following lithium treatment of dams. Its concentration in pups’ blood was consistently below that in maternal blood (30–35%) indicating significant protection by the placenta and breast tissue. However, much of the lithium that reached the fetus entered its brain. Levels of lithium in plasma fluctuated in different treatment groups but its concentration in CSF was stable at all ages, in agreement with known stable levels of endogenous ions in CSF. There was no significant increase of lithium transfer into CSF following application of Na+/K+ ATPase inhibitor (digoxin) in vivo, indicating that lithium transfer across choroid plexus epithelium is not likely to be via the Na+/K+ ATPase mechanism, at least early in development. Comparison with passive permeability markers suggested that in acute experiments lithium permeability was less than expected for diffusion but similar in long-term experiments at P2. Conclusions Information obtained on the distribution of lithium in developing brain provides a basis for studying possible deleterious effects on brain development and behaviour in offspring of mothers undergoing lithium therapy.

Background: Olanzapine is used to treat schizophrenia and bipolar disorder in women of childbearing age. Continuation of psychotropic medications throughout pregnancy and lactation is often required as cessation could be dangerous for both mother and child. However, there is a lack of information on the transfer of these drugs into the developing brain. Methods: Sprague Dawley rats at three developmental ages: embryonic day E19, postnatal day P4 and non-pregnant adult females were administered unlabelled or radiolabelled (3H) olanzapine (0.15 mg/kg) either as monotherapy or in combination with each of seven other common medications. Similar injections were administered to pregnant E19 females to investigate placental transfer. Olanzapine in plasma, cerebrospinal fluid (CSF) and brain was measured by liquid scintillation counting after a single dose (acute) or following 5 days of treatment (prolonged). Results: Olanzapine entry into brain and CSF was not age-dependent. Prolonged olanzapine treatment reduced placental transfer from 53% to 46% (p

The influenza vaccination uptake rate is low in China. In the current study, we aimed to explore the preferences of influenza vaccination among the Chinese elderly. A discrete choice experiment design was performed to assess their preferences toward five influenza vaccination attributes, including vaccine effectiveness, duration of protection, severe adverse effects, vaccination cost, and vaccination recommendation source. A total of 144 participants aged over 60 years from three cities of Shaanxi province in China were surveyed. A mixed logit model was employed to analyze the data. The elderly population were found to prefer influenza vaccination with a longer duration of protection, followed by lower severe adverse effects, higher vaccine effectiveness, and recommended by healthcare providers. The vaccination cost was the least important attribute. The most considerable marginal willingness to pay for vaccination (CNY220.90) and the highest vaccination choice probability (83.70%) occurred when the duration of protection extended from 3 to 12 months. The present study’s findings would inform decision-makers on implementing appropriate interventions for the increase of influenza vaccination coverage among the elderly in China.