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Background Although many studies have shown that herbs containing aristolochic acids can treat various human diseases, AAΙ in particular has been implicated as a nephrotoxic agent. Methods and results Here, we detail the nephrotoxic effect of AAΙ via an approach that integrated 1H NMR-based metabonomics and network pharmacology. Our findings revealed renal injury in mice after the administration of AAΙ. Metabolomic data confirmed significant differences among the renal metabolic profiles of control and model groups, with significant reductions in 12 differential metabolites relevant to 23 metabolic pathways. Among them, there were seven important metabolic pathways: arginine and proline metabolism; glycine, serine, and threonine metabolism; taurine and hypotaurine metabolism; ascorbate and aldehyde glycolate metabolism; pentose and glucosinolate interconversion; alanine, aspartate, and glutamate metabolism; and glyoxylate and dicarboxylic acid metabolism. Relevant genes, namely, nitric oxide synthase 1 (NOS1), pyrroline-5-carboxylate reductase 1 (PYCR1), nitric oxide synthase 3 (NOS3) and glutamic oxaloacetic transaminase 2 (GOT2), were highlighted via network pharmacology and molecular docking techniques. Quantitative real-time PCR findings revealed that AAI administration significantly downregulated GOT2 and NOS3 and significantly upregulated NOS1 and PYCR1 expression and thus influenced the metabolism of arginine and proline. Conclusion This work provides a meaningful insight for the mechanism of AAΙ renal injury.

Nonalcoholic steatohepatitis (NASH) is increasingly recognized as a serious disease that can lead to cirrhosis, hepatocellular carcinoma (HCC), and death. However, there is no effective drug to thwart the progression of the disease. Development of new drugs for NASH is an urgent clinical need. Liver biopsy plays a key role in the development of new NASH drugs. Histological findings based on liver biopsy are currently used as the main inclusion criteria and the primary therapeutic endpoint in NASH clinical trials. However, there are inherent challenges in the use of liver biopsy in clinical trials, such as evaluation reliability, sampling error, and invasive nature of the procedure. In this article, we review the advantages and value of liver histopathology based on liver biopsy in clinical trials of new NASH drugs. We also discuss the challenges and limitations of liver biopsy and identify future drug development directions.

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders of pregnancy and can cause short- and long-term adverse effects in both pregnant women and their offspring. However, the etiology and pathogenesis of GDM are still unclear. As a metabolic disease, GDM is well suited to metabolomics study, which can monitor the changes in small molecular metabolites induced by maternal stimuli or perturbations in real time. The application of metabolomics in GDM can be used to discover diagnostic biomarkers, evaluate the prognosis of the disease, guide the application of diet or drugs, evaluate the curative effect, and explore the mechanism. This review provides comprehensive documentation of metabolomics research methods and techniques as well as the current progress in GDM research. We anticipate that the review will contribute to identifying gaps in the current knowledge or metabolomics technology, provide evidence-based information, and inform future research directions in GDM.

The aim of this study was to evaluate the relationship among inflammatory cytokines including hypersensitive C-reactive protein (hs-CRP) and interleukin-6 (IL-6), glycated hemoglobin A1c (HbA1c), and platelet distribution width (PDW) in women with gestational diabetes mellitus (GDM).Data on 191 pregnant women (96 women with GDM; 95 healthy controls) were extracted from routine prenatal examination records in Nanjing, China. Fasting concentrations of hs-CRP, IL-6, HbA1c, blood cell indices, and glucose at 24-28th gestational weeks were determined.The levels of hs-CRP, IL-6, FPG, PG1h, PG2h, HbA1c, RBC, and PDW significantly were increased (P 0.05) in GDM group. hs-CRP had a positive correlation with HbA1c and PLT (P 0.05). The odds ratios of HbA1c and PDW were 7.817 (95% CI 1.921-31.816, P = 0.004) and 1.523 (95% CI 1.158-2.002, P = 0.003), respectively. Furthermore, AUC of the combined diagnosis of GDM including HbA1c, FPG, and PDW reached 0.754, with specificity of 80.0% and sensitivity of 60.4%.Our findings support that elevated levels of hs-CRP, IL-6, HbA1c, and PDW at 24-28th gestational weeks even within the conventional normal range, may be implicated in the pathogenesis of GDM and their evaluation should be part of prenatal care routine.

Objectives Stroke is the third most common cause of death and also causes seizures and disability. Biomarkers are abnormal signal indicators at the biological level that are present before the organism is seriously affected and are more sensitive to early diagnosis than are traditional imaging methods. Early diagnosis of stroke can prevent the progression of the disease. However, there are currently no widely accepted biomarkers for stroke that have been applied clinically. Methods A serum metabonomics method based on ultra-high-performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry (UPLC-Q-TOF/MS) was used to identify potential biomarkers and metabolic pathways of cerebral infarction. The receiver-operating characteristic (ROC) curve was used to verify the diagnostic and classification abilities of the biomarkers, and a support vector machine (SVM) model was developed for the prediction of cerebral infarction. Results Principal component analysis revealed a clear separation between the normal and cerebral infarction groups. A total of 13 potential serum biomarkers were identified, which were mainly involved in linoleic acid metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; tyrosine metabolism; arachidonic acid metabolism; and fatty acid biosynthesis. The ROC curve analysis showed that the potential biomarkers had high specificity and sensitivity for the diagnosis of cerebral infarction. The SVM model had good diagnostic ability and could accurately distinguish the control group from the cerebral infarction group. Discussion The metabonomics approach may be a useful bioanalytical method for understanding the pathophysiology of cerebral infarction and may provide an experimental basis for the development of clinical biomarkers for stroke.

This paper examines the role of land consolidation in contributing to the Chinese government’s objective of sustainable and balanced growth of the economy. Our review shows the current approach by the government suffers from major deficiencies: 1) historical legacy in land law amendment; 2) the reliance on rural encroachment for financing local fiscal income 3) deficiencies of land consolidation model.

We investigate the statistical distribution for ideal Bose gases with constant particle density in the 3D box of volume $V=L^{3}$. By changing linear size $L$ and imposing different boundary conditions on the system, we present a numerical analysis on the characteristic temperature and condensate fraction, and find that the smaller linear size is efficient to increase the characteristic temperature and condensate fraction. Moreover, there is a singularity under the antiperiodic boundary condition., 6 pages, 10 figures

Many infections and stress signals can rapidly activate the NLRP3 inflammasome to elicit robust inflammatory responses. This activation requires a priming step, which is thought to be mainly for upregulating NLRP3 transcription. However, recent studies report that the NLRP3 inflammasome can be activated independently of transcription, suggesting that the priming process has unknown essential regulatory steps. Here, we report that JNK1-mediated NLRP3 phosphorylation at S194 is a critical priming event and is essential for NLRP3 inflammasome activation. We show that NLRP3 inflammasome activation is disrupted in NLRP3-S194A knockin mice. JNK1-mediated NLRP3 S194 phosphorylation is critical for NLRP3 deubiquitination and facilitates its self-association and the subsequent inflammasome assembly. Importantly, we demonstrate that blocking S194 phosphorylation prevents NLRP3 inflammasome activation in cryopyrin-associated periodic syndromes (CAPS). Thus, our study reveals a key priming molecular event that is a prerequisite for NLRP3 inflammasome activation. Inhibiting NLRP3 phosphorylation could be an effective treatment for NLRP3-related diseases.

Hepatic metastasis is responsible for the majority of colorectal cancer (CRC)-related mortalities. Although Gankyrin (PSMD10) has been implicated in cancer metastasis, its exact role and underlying mechanisms of CRC hepatic metastasis remain largely unknown. Herein, we showed that the expression of Gankyrin was higher in primary CRC with hepatic metastasis compared with CRC without metastasis. RNAi-mediated silencing of Gankyrin expression in highly metastatic human CRC cells impaired their migratory and metastatic capacity in vivo. Genome-wide transcriptome profiling revealed activation of the interleukin (IL)-8 signaling pathway by Gankyrin. Protein levels of IL-8 and cyclin D1 (CCND1), the two important molecules in the IL-8 pathway, were positively correlated with Gankyrin expression in human CRC specimens. Furthermore, genetic deletion of cyclin D1 showed its requirement in Gankyrin-mediated cell migration. Finally, administration of recombinant IL-8 rescued the migratory defect of CRC cells where Gankyrin expression was silenced. Together, our findings highlight the importance of Gankyrin in hepatic metastasis of CRC and point out its candidature as a potential prognostic marker and therapeutic target to improve the clinical management of metastatic CRC. Cancer Res; 73(14); 4548–58. ©2013 AACR.

We use a great deal of wireless sensor nodes to detect target signal that is more accurate than the traditional single radar detection method. Each local sensor detects the target signal in the region of interests and collects relevant data, and then it sends the respective data to the data fusion center (DFC) for aggregation processing and judgment making whether the target signal exists or not. However, the current judgment fusion rules such as Counting Rule (CR) and Clustering-Counting Rule (C-CR) have the characteristics on high energy consumption and low detection precision. Consequently, this paper proposes a novel Weight-based Clustering Decision Fusion Algorithm (W-CDFA) to detect target signal in wireless sensor network. It first introduces the clustering method based on tree structure to establish the precursor-successor relationships among the clusters in the region of interests and then fuses the decision data along the direction from the precursor clusters to the successor clusters gradually, and DFC (i.e., tree root) makes final determination by overall judgment values from subclusters and ordinary nodes. Simulation experiments show that the fusion rule can obtain more satisfactory system level performance at the environment of low signal to noise compared with CR and C-CR methods.