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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.

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.

Objective We aimed to estimate the optimal cut-off values of liver stiffness measurement (LSM) for diagnosing and staging fibrosis in non-obese and obese patients with nonalcoholic fatty liver disease (NAFLD). Methods NAFLD patients diagnosed by liver biopsy according to the Nonalcoholic Steatohepatitis Clinical Research Network scoring system were enrolled in this study. Non-obesity was defined as a body mass index (BMI) less than 25 kg/m2 . LSM was performed by experienced physicians within 2 weeks before or after liver biopsy. Results A total of 158 patients were included. Average BMI of the non-obese (n = 68) and obese (n = 90) groups was 23.2 ± 1.6 and 27.9 ± 2.5 kg/m2 , respectively. After adjusted for age, fibrosis stage, steatosis grade and type 2 diabetes mellitus, the obese group had a LSM of 3.522 kPa higher than the non-obese patients (P = 0.003). LSM values of the non-obese patients had a lower trend when stratified by fibrosis stage, especially in cirrhosis (F4; P = 0.021). Applying separate cut-off values for patients with NAFLD in individual fibrosis stage, 5.8 vs 7.5 kPa (≥ F1), 7.6 vs 8.5 kPa (≥ F2), 9.1 vs 11.2 kPa (≥ F3), and 12.5 vs 14.3 kPa (F4), improved their diagnostic odds ratios compared with overall cut-off values. In the non-obese NAFLD group, using a separate cut-off avoided underestimating 9.1% of patients with cirrhosis. Conclusions Non-obese NAFLD group had lower LSM than the obese group. Different cut-off values should be used to measure liver fibrosis stage in non-obese and obese NAFLD patients.

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 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.