Preclinical Absorption, Distribution, Metabolism, and Excretion of Sodium Danshensu, One of the Main Water-Soluble Ingredients in Salvia miltiorrhiza, in Rats

In this study, the absorption, distribution, metabolism and excretion (ADME) of sodium danshensu (Sodium DL-β-(3, 4-dihydroxyphenyl)lactate), one of the main water-soluble active constituents in Salvia miltiorrhiza, were evaluated in rats. Pharmacokinetic study was evaluated in doses of 15, 30, and 60 mg/kg after intravenous administration of sodium danshensu. Bioavailability study was evaluated by comparing between 30 mg/kg (I.V.) and 180 mg/kg (P.O.) of sodium danshensu. Tissue distribution, metabolism, and excretion were evaluated at 30 mg/kg (I.V.) of sodium danshensu. Following intravenous administration, sodium danshensu exhibited linear pharmacokinetics in the dose range of 15–60 mg/kg. Sodium danshensu appeared to be poorly absorbed after oral administration, with an absolute bioavailability of 13.72%. The primary distribution tissue was kidney, but it was also distributed to lung, stomach, muscle, uterus, heart, etc. Within 96 h after intravenous administration, 46.99% was excreted via urine and 1.16% was excreted via feces as the parent drug. Biliary excretion of sodium danshensu was about 0.83% for 24 h. Metabolites in urine were identified as methylation, sulfation, both methylation and sulfation, and acetylation of danshensu. Sodium danshensu can be developed as an injection because of its poor oral bioavailability. In conclusion, sodium danshensu is widely distributed, mainly phase II metabolized and excreted primarily in urine as an unchanged drug in rats

Group-level personality detection based on text generated networks

Personality analysis has been widely used in various social services such as mental healthcare, recommendation systems and so on because its natural explainability for AI applications in Web intelligence. With the penetration of Web2.0, traditional social researches have gradually turned to online social networks. However, for a long time, personality detection from online social texts has sunk into an embarrassing situation for the lack of large labeled datasets. Limited by supervised learning frameworks and small labeled datasets, prior works mainly detect one’s personality in the individual perspective, which may not well meet the challenges of massive un-labeled data in the near future. In this paper, we present a first look into group-level personality detection and we use an unsupervised feature learning method instead of supervised methods used in most related works. We propose AdaWalk, a new and novel model of group-level personality detection by learning the influence from text generated networks. The model uses different kernels to evaluate how much a given node should decide its walk path locally or globally. The advantage of AdaWalk is three-folded: a) the model is an unsupervised feature learning method, which means it relies less on annotations. b) by traversing the network, we can capture the influence in the group level, thus the analysis of one’s personality is not only based on individual records but also the information in groups. Therefore, AdaWalk can leverage small datasets more comprehensively. c) AdaWalk is scalable and can be easily transformed as distributed algorithms, which means it has more potential, compared with existing personality detection methods, to meet the massive data without annotations. We use AdaWalk to predict users’ Big Five personality scores in FIVE heterogeneous personality datasets. Compared with more than TEN famous related methods, AdaWalk outperforms the others, meanwhile verifying the significance of the group perspective and unsupervised feature learning methods in the application of personality analysis. To make our experiment repeatable, AdaWalk and related datasets are available at https://xiangguosun.strikingly.com

Production Forecasting Based on Attribute-Augmented Spatiotemporal Graph Convolutional Network for a Typical Carbonate Reservoir in the Middle East

Production forecasting plays an important role in development plans during the entire period of petroleum exploration and development. Artificial intelligence has been extensively investigated in recent years because of its capacity to extensively analyze and interpret complex data. With the emergence of spatiotemporal models that can integrate graph convolutional networks (GCN) and recurrent neural networks (RNN), it is now possible to achieve multi-well production prediction by considering the impact of interactions between producers and historical production data simultaneously. Moreover, an accurate prediction not only depends on historical production data but also on the influence of neighboring injectors’ historical gas injection rate (GIR). Therefore, based on the assumption that introducing GIR can enhance prediction accuracy, this paper proposes a deep learning-based hybrid production forecasting model that is aimed at considering both the spatiotemporal characteristics of producers and the GIR of neighboring injectors. Specifically, we integrated spatiotemporal characteristics and GIR into an attribute-augmented spatiotemporal graph convolutional network (AST-GCN) and gated recurrent units (GRU) neural network to extract intricate temporal correlations from historical data. The method proposed in this paper has been successfully applied in a well pattern (including five producers and seven gas injectors) in a low-permeability carbonate reservoir in the Middle East. In single well production forecasting, the error of AST-GCN is 63.2%, 37.3%, and 16.1% lower in MedAE, MAE, and RMSE compared with GRU and 62.9%, 44.6%, and 28.9% lower compared with RNS. Similarly, the accuracy of AST-GCN is 15.9% and 35.8% higher than GRU and RNS in single well prediction. In well-pattern production forecasting, the error of AST-GCN is 41.2%, 64.2%, and 75.2% lower in RMSE, MAE, and MedAE compared with RNS, while the accuracy of AST-GCN is 29.3% higher. After different degrees of Gaussian noise are added to the actual data, the average change in AST-GCN is 3.3%, 0.4%, and 1.2% in MedAE, MAE, and RMSE, which indicates the robustness of the proposed model. The results show that the proposed model can consider the production data, gas injection data, and spatial correlation at the same time, which performs well in oil production forecasts

PIVKA-II level is correlated to development of portal vein tumor thrombus in patients with HBV-related hepatocellular carcinoma

Abstract Aim To evaluate the correlation of serum PIVKA-II levels and development of portal vein tumor thrombus (PVTT) in hepatocellular carcinoma (HCC) patients. Methods One hundred and twenty-three patients with newly diagnosed HCC were included in this study between March 2016 and October 2018. Thirty-five of these patients were detected with PVTT and all subjects were randomly divided to analysis group (N = 73) and validation (N = 50) group. Serum levels of PIVKA-II, laboratory tests including serum aspartate aminotransferase, total bilirubin, platelet count, albumin levels were demonstrated in all the patients. T-test, chi-squared test and logistic regression was used for analyzing data. Diagnostic efficiency and cut-off value of PIVKA-II in PVTT development of HCC patients were calculated using receiver operator curve (ROC) analysis. Results Serum level of PIVKA-II in HCC patients with PVTT was significantly higher than that in HCC patients without PVTT (995.8 mAU/ml vs 94.87 mAU/ml; P = 0.003), as well as D-dimer levels (2.12 mg/L vs 0.56 mg/L P = 0.001). Univariate analysis showed that high serum D-dimer level was an independent risk factor for development of PVTT (OR = 1.22, 95%CI 1.02–1.45). ROC curve showed that among analysis group, the area under ROC curve (AUROC) of PIVKA-II was 0.73 (95%CI 0.59–0.86). For the detection of PVTT in HCC, PIVKA-II had a sensitivity of 83.7% and a specificity of 69.2% at a cutoff of 221.26 mAU/ml, which had a sensitivity of 85.71% and a specificity of 55.56% in validation group, respectively. Conclusion Serum PIVKA-II level is a potential marker for diagnosis of PVTT in HCC patients, which may guide therapeutic strategy and assessment of tumor prognosis of HCC