Impaired cerebral microvascular endothelial cells integrity due to elevated dopamine in myasthenic model.

Myasthenia gravis is an autoimmune disease characterized by pathogenic antibodies that target structures of the neuromuscular junction. However, some patients also experience autonomic dysfunction, anxiety, depression, and other neurological symptoms, suggesting the complex nature of the neurological manifestations. With the aim of explaining the symptoms related to the central nervous system, we utilized a rat model to investigate the impact of dopamine signaling in the central nervous and peripheral circulation. We adopted several screening methods, including western blot, quantitative PCR, mass spectrum technique, immunohistochemistry, immunofluorescence staining, and flow cytometry. In this study, we observed increased and activated dopamine signaling in both the central nervous system and peripheral circulation of myasthenia gravis rats. Furthermore, changes in the expression of two key molecules, Claudin5 and CD31, in endothelial cells of the blood-brain barrier were also examined in these rats. We also confirmed that dopamine incubation reduced the expression of ZO1, Claudin5, and CD31 in endothelial cells by inhibiting the Wnt/ß-catenin signaling pathway. Overall, this study provides novel evidence suggesting that pathologically elevated dopamine in both the central nervous and peripheral circulation of myasthenia gravis rats impair brain-blood barrier integrity by inhibiting junction protein expression in brain microvascular endothelial cells through the Wnt/ß-catenin pathway.

LION: an integrated R package for effective prediction of ncRNA-protein interaction.

Understanding ncRNA-protein interaction is of critical importance to unveil ncRNAs' functions. Here, we propose an integrated package LION which comprises a new method for predicting ncRNA/lncRNA-protein interaction as well as a comprehensive strategy to meet the requirement of customisable prediction. Experimental results demonstrate that our method outperforms its competitors on multiple benchmark datasets. LION can also improve the performance of some widely used tools and build adaptable models for species- and tissue-specific prediction. We expect that LION will be a powerful and efficient tool for the prediction and analysis of ncRNA/lncRNA-protein interaction. The R Package LION is available on GitHub at https://github.com/HAN-Siyu/LION/.

TIGER: technical variation elimination for metabolomics data using ensemble learning architecture.

Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.

Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i.

BACKGROUND: Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. METHODS: Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. RESULTS: Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. CONCLUSION: This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Deep forest ensemble learning for classification of alignments of non-coding RNA sequences based on multi-view structure representations.

Non-coding RNAs (ncRNAs) play crucial roles in multiple biological processes. However, only a few ncRNAs' functions have been well studied. Given the significance of ncRNAs classification for understanding ncRNAs' functions, more and more computational methods have been introduced to improve the classification automatically and accurately. In this paper, based on a convolutional neural network and a deep forest algorithm, multi-grained cascade forest (GcForest), we propose a novel deep fusion learning framework, GcForest fusion method (GCFM), to classify alignments of ncRNA sequences for accurate clustering of ncRNAs. GCFM integrates a multi-view structure feature representation including sequence-structure alignment encoding, structure image representation and shape alignment encoding of structural subunits, enabling us to capture the potential specificity between ncRNAs. For the classification of pairwise alignment of two ncRNA sequences, the F-value of GCFM improves 6% than an existing alignment-based method. Furthermore, the clustering of ncRNA families is carried out based on the classification matrix generated from GCFM. Results suggest better performance (with 20% accuracy improved) than existing ncRNA clustering methods (RNAclust, Ensembleclust and CNNclust). Additionally, we apply GCFM to construct a phylogenetic tree of ncRNA and predict the probability of interactions between RNAs. Most ncRNAs are located correctly in the phylogenetic tree, and the prediction accuracy of RNA interaction is 90.63%. A web server (http://bmbl.sdstate.edu/gcfm/) is developed to maximize its availability, and the source code and related data are available at the same URL.

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts.

BACKGROUND: Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways. METHODS: We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed. RESULTS: We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism. CONCLUSION: Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

Characteristics, application and modeling of solid amine adsorbents for CO2 capture: A review.

In recent years, global warming has become an important topic of public concern. As one of the most promising carbon capture technologies, solid amine adsorbents have received a lot of attention because of their high adsorption capacity, excellent selectivity, and low energy cost, which is committed to sustainable development. The preparation methods and support materials can influence the thermal stability and adsorption capacity of solid amine adsorbents. As a supporting material, it needs to meet the requirements of high pore volume and abundant hydroxyl groups. Industrial and biomass waste are expected to be a novel and cheap raw material source, contributing both carbon dioxide capture and waste recycling. The applied range of solid amine adsorbents has been widened from flue gas to biogas and ambient air, which require different research focuses, including strengthening the selectivity of CO2 to CH4 or separating CO2 under the condition of the dilute concentration. Several kinetic or isotherm models have been adopted to describe the adsorption process of solid amine adsorbents, which select the pseudo-first order model, pseudo-second order model, and Langmuir isotherm model most commonly. Besides searching for novel materials from solid waste and widening the applicable gases, developing the dynamic adsorption and three-dimensional models can also be a promising direction to accelerate the development of this technology. The review has combed through the recent development and covered the shortages of previous review papers, expected to promote the industrial application of solid amine adsorbents.

LPInsider: a webserver for lncRNA-protein interaction extraction from the literature.

BACKGROUND: Long non-coding RNA (LncRNA) plays important roles in physiological and pathological processes. Identifying LncRNA-protein interactions (LPIs) is essential to understand the molecular mechanism and infer the functions of lncRNAs. With the overwhelming size of the biomedical literature, extracting LPIs directly from the biomedical literature is essential, promising and challenging. However, there is no webserver of LPIs relationship extraction from literature. RESULTS: LPInsider is developed as the first webserver for extracting LPIs from biomedical literature texts based on multiple text features (semantic word vectors, syntactic structure vectors, distance vectors, and part of speech vectors) and logistic regression. LPInsider allows researchers to extract LPIs by uploading PMID, PMCID, PMID List, or biomedical text. A manually filtered and highly reliable LPI corpus is integrated in LPInsider. The performance of LPInsider is optimal by comprehensive experiment on different combinations of different feature and machine learning models. CONCLUSIONS: LPInsider is an efficient analytical tool for LPIs that helps researchers to enhance their comprehension of lncRNAs from text mining, and also saving their time. In addition, LPInsider is freely accessible from http://www.csbg-jlu.info/LPInsider/ with no login requirement. The source code and LPIs corpus can be downloaded from https://github.com/qiufengdiewu/LPInsider .

Distribution characteristics of microplastics in the soil of mangrove restoration wetland and the effects of microplastics on soil characteristics.

The dense vegetation in the wetland could effectively retain microplastic particles, and the distribution of microplastics varied significantly under different planting densities. In addition, microplastics in the soil environment can affect soil properties to a certain extent, which in turn can affect soil functions and biodiversity. In this study, we investigated the distribution of soil microplastics in a mangrove restoration wetland under different planting densities and their effects on wetland soil properties. The results indicated that the average abundance of soil microplastics was 2177.5 n/500 g, of which 70.9% exhibited a diameter ranging from 0.038-0.05 mm, while the remaining soil microplastics accounted for less than 20% of all microplastics, indicating that smaller-diameter microplastics were more likely to accumulate in wetland soil. The microplastic abundance could be ranked based on the planting density as follows: 0.5 × 0.5 m > 1.0 × 0.5 m > 1.0 × 1.0 m > control area. Raman spectroscopy revealed that the predominant microplastic categories in this region included polyethylene terephthalate (PET, 52%), polyethylene (PE, 24%) and polypropylene (PP, 15%). Scanning electron microscopy (SEM) images revealed fractures and tears on the surface of microplastics. EDS energy spectra indicated a large amount of metal elements on the surface of microplastics. Due to the adsorptive features of PET, this substance could influence the soil particle size distribution and thus the soil structure. All physicochemical factors, except for the soil pH, were significantly affected by PET. In addition, the CV analysis results indicated that soils in vegetated areas are more susceptible to PET than are soils in bare ground areas, leading to greater variation in their properties.

Spatial-temporal evolution of coupling relationship between land development intensity and resources environment carrying capacity in China.

Land development poses challenges to the sustainable use of resources and environmental health in regions. This study explores the coupling relationship and its spatial-temporal evolution trend between land development intensity and resources environment carrying capacity of 31 provinces in China from 2005 to 2017. The information entropy method, coupling degree model, and coupling coordination degree model are used to calculate the index weight, coupling degree, and coupling coordination degree. The results show that: (1) Three change types of resources environment carrying capacity are presented with land development intensity increasing: first decrease and then increase; first increase and then decrease; and alternating fluctuations. (2) The proportion of construction land, GDP per land, and population density are dominate determinants of land development intensity, while the water resources per capita, energy consumption per unit of GDP, and per capita cultivated land area are that of resources environment carrying capacity. (3) From the perspective of temporal evolution, both coupling and coordination relationship were found to have continuously strengthened. (4) In terms of spatial evolution, the coupling level presented a constantly narrowing inter-regional gap, and the coordination level has changed from initial two-level differentiation to final regional gap narrowing. These findings can provide evidence in support for integrating land development with resources environmental protection to promote regional coordinated development.

Biodrying with the hot-air aeration system for kitchen food waste.

Biodrying is a promising method that produces bio-stabilized output with minimum pretreatment requirements. In this study, a hot-air supply system was added to the traditional biodrying process for kitchen waste, which showed significant reduction in moisture content in 5 days (maximum reduction of 37.45%). A series of experiments was conducted to optimize the hot-air biodrying system utilizing different aeration rates, temperatures, and mixing ratios of feedstock to bulking agents. The results showed that a 65 °C aeration temperature led to the highest water removal rate and low volatile solids consumption rate, with the biodrying index reaching 4.9 g water per gram of volatile solids. On the other hand, evaluation of the overall biodrying efficiency based on the weight loss and bio-stabilization showed that intermittent aeration temperature at 55 °C performed best, offering suitable conditions for water evaporation and bio-degradation. In combination with a flow rate of 0.8 L/kg*min and 1:1 mixing ratio, these conditions resulted in the maximum volatile solids consumption of 26.26% in 5 days. The volatile solids consumption and 34.47% water removal rate of the trial had contributed to a total of 64.13% weight loss. The weight loss was even higher than that of a conventional biodrying system which was conducted for more than 14 days.

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Infectious diseases are commonly seen in clinical practice, and pathogen diagnosis is the key link in diagnosis and treatment; however, conventional pathogen detection methods cannot meet clinical needs due to time-consuming operation and low positive rate. As a new pathogen detection method, metagenomic next-generation sequencing (mNGS) has a wide detection range and can detect bacteria, viruses, fungi, parasites, rare pathogens, and even unknown pathogens. The technique of mNGS is unbiased and can rapidly, efficiently, and accurately obtain all nucleic acid information in test samples, analyze pathogens, and guide clinical diagnosis and treatment, thereby playing an important role in complicated infectious diseases. This article reviews the diagnostic advantages and clinical value of mNGS in bacterial, fungal, viral, and parasitic infections.

Capsule-LPI: a LncRNA-protein interaction predicting tool based on a capsule network.

BACKGROUND: Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA-protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. RESULTS: We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. CONCLUSIONS: This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver ( http://csbg-jlu.site/lpc/predict ) is developed to be convenient for users.

LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property.

Discovering new long non-coding RNAs (lncRNAs) has been a fundamental step in lncRNA-related research. Nowadays, many machine learning-based tools have been developed for lncRNA identification. However, many methods predict lncRNAs using sequence-derived features alone, which tend to display unstable performances on different species. Moreover, the majority of tools cannot be re-trained or tailored by users and neither can the features be customized or integrated to meet researchers' requirements. In this study, features extracted from sequence-intrinsic composition, secondary structure and physicochemical property are comprehensively reviewed and evaluated. An integrated platform named LncFinder is also developed to enhance the performance and promote the research of lncRNA identification. LncFinder includes a novel lncRNA predictor using the heterologous features we designed. Experimental results show that our method outperforms several state-of-the-art tools on multiple species with more robust and satisfactory results. Researchers can additionally employ LncFinder to extract various classic features, build classifier with numerous machine learning algorithms and evaluate classifier performance effectively and efficiently. LncFinder can reveal the properties of lncRNA and mRNA from various perspectives and further inspire lncRNA-protein interaction prediction and lncRNA evolution analysis. It is anticipated that LncFinder can significantly facilitate lncRNA-related research, especially for the poorly explored species. LncFinder is released as R package (https://CRAN.R-project.org/package=LncFinder). A web server (http://bmbl.sdstate.edu/lncfinder/) is also developed to maximize its availability.

Clinical features and imaging manifestations of retinoblastoma with hepatic metastasis.

Clinical data of five patients with hepatic metastases of retinoblastoma were analyzed retrospectively (two had bilateral tumors three had unilateral intraocular tumors). On computed tomography, multiple and single low-density foci were observed. Four patients had tumor remission, and one showed no response after chemotherapy. Three patients who underwent enucleation were at high risk for extensive choroidal invasion. Central nervous system and bone metastases occurred in all five patients. Neuron-specific enolase and lactate dehydrogenase levels were significantly elevated in all patients. Two patients died (not from hepatic metastasis). Three patients (one with tumor progression and two with shorter courses) are continuing treatment.

Origin of high thermoelectric performance with a wide range of compositions for BixSb2-xTe3 single quintuple layers.

Composition regulation of semiconductors can engineer the band structures and hence optimize their properties for better applications. Herein, we report a BixSb2-xTe3 (BST) single QL with high ZT values (∼1.2 to ∼1.5) at 300 K across a wide range of compositions 0 < x≤ 1. The improved description of band structures by the unfold method reveals the multi-valley bands near the Fermi energy. The high power factor of a p-type BST single QL originates from the robust multi-valley character of valence bands. The wide composition range is ensured by the valence band maximum dominated by the antibonding states of Sb-Te2 bonds, which would be affected little by the disorder. The optimal composition for the BST single QL is attributed to the different contributions from Sb and Bi to the valence band edge. This work paves the way for the further combination of a large power factor and low thermal conductivity across a wide range of compositions.

RPITER: A Hierarchical Deep Learning Framework for ncRNA⁻Protein Interaction Prediction.

Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implicated in many complex human diseases. Mostly ncRNAs function by interacting with corresponding RNA-binding proteins. The research on ncRNA⁻protein interaction is the key to understanding the function of ncRNA. However, the biological experiment techniques for identifying RNA⁻protein interactions (RPIs) are currently still expensive and time-consuming. Due to the complex molecular mechanism of ncRNA⁻protein interaction and the lack of conservation for ncRNA, especially for long ncRNA (lncRNA), the prediction of ncRNA⁻protein interaction is still a challenge. Deep learning-based models have become the state-of-the-art in a range of biological sequence analysis problems due to their strong power of feature learning. In this study, we proposed a hierarchical deep learning framework RPITER to predict RNA⁻protein interaction. For sequence coding, we improved the conjoint triad feature (CTF) coding method by complementing more primary sequence information and adding sequence structure information. For model design, RPITER employed two basic neural network architectures of convolution neural network (CNN) and stacked auto-encoder (SAE). Comprehensive experiments were performed on five benchmark datasets from PDB and NPInter databases to analyze and compare the performances of different sequence coding methods and prediction models. We found that CNN and SAE deep learning architectures have powerful fitting abilities for the k-mer features of RNA and protein sequence. The improved CTF coding method showed performance gain compared with the original CTF method. Moreover, our designed RPITER performed well in predicting RNA⁻protein interaction (RPI) and could outperform most of the previous methods. On five widely used RPI datasets, RPI369, RPI488, RPI1807, RPI2241 and NPInter, RPITER obtained A U C of 0.821, 0.911, 0.990, 0.957 and 0.985, respectively. The proposed RPITER could be a complementary method for predicting RPI and constructing RPI network, which would help push forward the related biological research on ncRNAs and lncRNAs.

Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods.

Non-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA⁻protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA⁻protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA⁻protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at https://github.com/HAN-Siyu/APINet/.

The effect of vanadium on essential element uptake of Setaria viridis' seedlings.

High concentrations of vanadium, a ubiquitous element in the environment, in growing media leads to deformation of root structure and leaf chlorosis and necrosis, consequently affecting the translocations of nutrients and essential elements. However, the effects of vanadium on essential element uptake, and the interactions of essential elements in the presence of vanadium, remain incompletely understood. To elucidate the effects of different concentrations of vanadium on major and trace essential elements and plant growth, a native plant species growing in a vanadium mining area, Setaria viridis (dog tail's grass), was incubated in solutions containing 0-55.8 mg/L vanadium. The shoot accumulation of four major essential elements and five trace essential elements was detected, and the root length and stem height were measured. The results showed that vanadium in soil solution enhanced the accumulation of all major essential elements in shoot. Vanadium concentrations lower than 47.4 mg/L showed an obvious positive (p < 0.05) effect on P accumulation and translocation. In the case of trace essential elements, there were threshold values for solution vanadium stimulation of element uptake. The threshold values for Cu and Zn, Fe, and Mo uptake were 4.3, 16.3, and 40.6 mg/L, respectively. When vanadium levels surpassed these values, accumulation was suppressed and the solution vanadium concentrations attenuated the solution-to-shoot translocation of most of the essential elements. Among the trace essential elements, translocation of Fe was obviously enhanced (p < 0.05) by vanadium. Solution vanadium also enhanced plant growth at lower concentrations and inhibited it at higher levels. The threshold values for stem height and root length were 36.8 and 16.3 mg/L, respectively. Concentrations of 40 and 55.8 mg/L vanadium in soil solution caused a 50% decrease in root length and stem height, respectively, showing that root length of Setaria viridis is more susceptible to vanadium toxicity than stem growth.

Genistein inhibited ammonia induced astrocyte swelling by inhibiting NF-κB activation-mediated nitric oxide formation.

Our previous study has indicated the involvement of epidermal growth factor receptor (EGFR) transactivation in ammonia-induced astrocyte swelling, which represents a major pathogenesis of brain edema in hepatic encephalopathy. In this study, we examined the effect of genistein, a naturally occurred broad-spectrum protein tyrosine kinase (PTK) inhibitor, on ammonia-induced cell swelling. We found that genistein pretreatment significantly prevented ammonia-induced astrocyte swelling. Mechanistically, ammonia triggered EGFR/extracellular signal-regulated kinase (ERK) association and subsequent ERK phosphorylation were alleviated by genistein pretreatment. Moreover, ammonia-induced NF-κB nuclear location, iNOS expression, and consequent NO production were all prevented by AG1478 and genistein pretreatment. This study suggested that genistein could alleviate ammonia-induced astrocyte swelling, which may be, at least partly, related to its PTK-inhibiting activity and repression of NF-κB mediated iNOS-derived NO accumulation.