Effect of herbal medicines on the pharmacokinetics and pharmacodynamics of Warfarin in healthy subjects

Herbal medicines are widely used in our community. A survey of Australian consumers indicated that 60% had used complementary and/or alternative medicines in the past year with the majority not informing their doctor that they were using herbal medicines. Little is known about the potentially serious consequences of interactions between herbal and conventional medicines. Warfarin has an important role in treating people with heart disease, yet it has a narrow therapeutic range, is highly bound to plasma proteins, and is metabolised by cytochrome P450. This creates the potential for life-threatening interactions with other drugs and foods leading to excessive bleeding. Hence, warfarin is one of the most frequently investigated drugs for interaction studies. Early clinical reports suggest that there exists the potential for an interaction between warfarin and four herbal medicines: St John�s wort, ginseng, ginkgo and ginger. However, these herb-drug combinations have never been conclusively studied. The two clinical studies conducted as part of this research had an identical study design. Twenty-four healthy male subjects were recruited into the two separate studies. This was an open label, three-way crossover randomised study in twelve healthy male subjects, who received a single 25 mg dose of warfarin alone or after 14 days pre-treatment with St John�s wort, or 7 days pre-treatment with ginseng. Dosing with St John�s wort or ginseng was continued for 7 days after administration of the warfarin dose in study I or who received a single 25 mg dose of warfarin alone or after 7 days pre-treatment with recommended doses of ginkgo or ginger from single ingredient products of known quality. Dosing with ginkgo or ginger was continued for 7 days after administration of the warfarin dose in study II. Platelet aggregation, international normalised ratio (INR) of prothrombin time, warfarin enantiomer protein binding, warfarin enantiomer concentrations in plasma and S-7-hydroxywarfarin concentration in urine were measured in both studies. Statistical comparisons were made using ANOVA and 95% confidence interval (CI) for mean value and 90% CI for geometric mean ratio value are reported. n study I, the mean (95% CI) apparent clearance of S-warfarin after warfarin alone or with St John�s wort or ginseng were, respectively, 198 (174 � 223) ml/h, 269 (241 � 297) ml/h and 220 (201 � 238) ml/h. The respective apparent clearances of R-warfarin were 110 (94 � 126) ml/h, 142 (123 � 161) ml/h and 119 (106 � 131) ml/h. The mean ratio of apparent clearance for S-warfarin was 1.29 (1.16-1.46) and for R-warfarin was 1.23 (1.11-1.37) when St John�s wort was co-administered. The mean ratio of AUC0-168 of INR was 0.79 (0.70 - 0.95) when St John�s wort was co-administered. The urinary excretion ratio of S-7-hydroxywarfarin after administration of warfarin alone was 0.04 (0.03 � 0.06) mg/h and there was no significant difference following treatment with either St John�s wort 0.03 (0.02 � 0.04) mg/h or ginseng 0.03 (0.02 � 0.04) mg/h. The ratio of geometric means for S-7-hydroxywarfarin UER was 0.82 (0.61-1.12) for St John�s wort, and 0.68 (0.50-0.91) for ginseng. St John�s wort and ginseng did not affect the apparent volumes of distribution or protein binding of warfarin enantiomers. In study II, the mean (95% CI) apparent clearance of S-warfarin after warfarin alone, with ginkgo or ginger were 189 (167 � 210) ml/h, 200 (173 � 227) ml/h and 201 (171 � 231) ml/h, respectively. The respective apparent clearances of R-warfarin were 127 (106 � 149) ml/h, 126 (111 � 141) ml/h and 131 (106 � 156) ml/h. The mean ratio of apparent clearance for S-warfarin was 1.05 (0.98 -1.12) and for R-warfarin was 1.00 (0.93 -1.08) when co-administered with ginkgo. The mean ratio of AUC0-168 of INR was 0.93 (0.81 -1.05) when co-administered with ginkgo. The mean ratio of apparent clearance for S-warfarin was 1.05 (0.97 -1.13) and for R-warfarin was 1.02 (0.95 -1.10) when co-administered with ginger. The mean ratio of AUC0-168 of INR was 1.01 (0.93 -1.15) when co-administered with ginger. The urinary excretion ratio (UER) of S-7-hydroxywarfarin after administration of warfarin alone was 0.04 (0.03 � 0.05) mg/h and there was no significant difference following treatment with either ginkgo 0.04 (0.03 � 0.04) mg/h or ginger 0.03 (0.02 � 0.04) mg/h. The ratio of geometric means for S-7-hydroxywarfarin UER was 1.07 (0.69-1.67) for ginkgo, and 1.00 (0.64-1.56) for ginger. Ginkgo and ginger did not affect the apparent volumes of distribution or protein binding of either S-warfarin or R-warfarin. In conclusion, St John�s wort significantly induced the apparent clearance of both S-warfarin and R-warfarin, which in turn resulted in a significant reduction in the pharmacological effect of rac-warfarin. Ginseng, ginkgo and ginger at recommended doses affect neither clotting status, nor the pharmacokinetics or pharmacodynamics of either S-warfarin or R-warfarin in healthy subjects

Robust Quadratic Stabilizability and H

This paper mainly discusses the robust quadratic stability and stabilization of linear discrete-time stochastic systems with state delay and uncertain parameters. By means of the linear matrix inequality (LMI) method, a sufficient condition is, respectively, obtained for the stability and stabilizability of the considered system. Moreover, we design the robust H∞ state feedback controllers such that the system with admissible uncertainties is not only quadratically internally stable but also robust H∞ controllable. A sufficient condition for the existence of the desired robust H∞ controller is obtained. Finally, an example with simulations is given to verify the effectiveness of our theoretical results

An integrated decision making model for dynamic pricing and inventory control of substitutable products based on demand learning

Purpose: This paper focuses on the PC industry, analyzing a PC supply chain system composed of onelarge retailer and two manufacturers. The retailer informs the suppliers of the total order quantity, namelyQ, based on demand forecast ahead of the selling season. The suppliers manufacture products accordingto the predicted quantity. When the actual demand has been observed, the retailer conducts demandlearning and determines the actual order quantity. Under the assumption that the products of the twosuppliers are one-way substitutable, an integrated decision-making model for dynamic pricing andinventory control is established.Design/methodology/approach: This paper proposes a mathematical model where a large domestichousehold appliance retailer decides the optimal original ordering quantity before the selling season and theoptimal actual ordering quantity, and two manufacturers decide the optimal wholesale price.Findings:By applying this model to a large domestic household appliance retail terminal, the authors canconclude that the model is quite feasible and effective. Meanwhile, the results of simulation analysis showthat when the product prices of two manufacturers both reduce gradually, one manufacturer will often waittill the other manufacturer reduces their price to a crucial inflection point, then their profit will show aqualitative change instead of a real-time profit-price change.Practical implications: This model can be adopted to a supply chain system composed of one largeretailer and two manufacturers, helping manufacturers better make a pricing and inventory controldecision.Originality/value: Previous research focuses on the ordering quantity directly be decided. Limited workhas considered the actual ordering quantity based on demand learning. However, this paper considers boththe optimal original ordering quantity before the selling season and the optimal actual ordering quantityfrom the perspective of the retailerPeer Reviewe

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics - A Review

In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration, and other harsh operation conditions. The drilling activities generate massive field data, namely field reliability big data (FRBD), which includes downhole operation, environment, failure, degradation, and dynamic data. Field reliability big data has large size, high variety, and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and maintenance planning and optimization. Furthermore, the authors highlight the future research about how to better apply the downhole vibration factor in reliability big data analytics to further improve tool reliability and optimize maintenance planning

Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling

Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or span-level labeling models based on metric learning. However, these methods are only trained at a single granularity (i.e., either token level or span level) and have some weaknesses of the corresponding granularity. In this paper, we first unify token and span level supervisions and propose a Consistent Dual Adaptive Prototypical (CDAP) network for few-shot sequence labeling. CDAP contains the token-level and span-level networks, jointly trained at different granularities. To align the outputs of two networks, we further propose a consistent loss to enable them to learn from each other. During the inference phase, we propose a consistent greedy inference algorithm that first adjusts the predicted probability and then greedily selects non-overlapping spans with maximum probability. Extensive experiments show that our model achieves new state-of-the-art results on three benchmark datasets.Comment: Accepted by ACM Transactions on Information System

Diesel degradation capability and environmental robustness of strain Pseudomonas aeruginosa WS02

Petroleum hydrocarbon (PHC) degrading bacteria have been frequently discovered. However, in practical application, a single species of PHC degrading bacterium with weak competitiveness may face environmental pressure and competitive exclusion due to the interspecific competition between petroleum-degrading bacteria as well as indigenous microbiota in soil, leading to a reduced efficacy or even malfunction. In this study, the diesel degradation ability and environmental robustness of an endophytic strain Pseudomonas aeruginosa WS02, were investigated. The results show that the cell membrane surface of WS02 was highly hydrophobic, and the strain secreted glycolipid surfactants. Genetic analysis results revealed that WS02 contained multiple metabolic systems and PHC degradation-related genes, indicating that this strain theoretically possesses the capability of oxidizing both alkanes and aromatic hydrocarbons. Gene annotation also showed many targets which coded for heavy metal resistant and metal transporter proteins. The gene annotation-based inference was confirmed by the experimental results: GC-MS analysis revealed that short chain PHCs (C10–C14) were completely degraded, and the degradation of PHCs ranging from C15–C22 were above 90% after 14 d in diesel-exposed culture; Heavy metal (Mn2+, Pb2+ and Zn2+) exposure was found to affect the growth of WS02 to some extent, but not its ability to degrade diesel, and the degradation efficiency was still maintained at 39–59%. WS02 also showed a environmental robustness along with PHC-degradation performance in the co-culture system with other bacterial strains as well as in the co-cultured system with the indigenous microbiota in soil fluid extracted from a PHC-contaminated site. It can be concluded that the broad-spectrum diesel degradation efficacy and great environmental robustness give P. aeruginosa WS02 great potential for application in the remediation of PHC-contaminated soil.<br/

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

The metaverse is regarded as a new wave of technological transformation that provides a virtual space for people to interact through digital avatars. To achieve immersive user experiences in the metaverse, real-time rendering is the key technology. However, computing-intensive tasks of real-time rendering from metaverse service providers cannot be processed efficiently on a single resource-limited mobile device. Alternatively, such mobile devices can offload the metaverse rendering tasks to other mobile devices by adopting the collaborative computing paradigm based on Coded Distributed Computing (CDC). Therefore, this paper introduces a hierarchical game-theoretic CDC framework for the metaverse services, especially for the vehicular metaverse. In the framework, idle resources from vehicles, acting as CDC workers, are aggregated to handle intensive computation tasks in the vehicular metaverse. Specifically, in the upper layer, a miner coalition formation game is formulated based on a reputation metric to select reliable workers. To guarantee the reliable management of reputation values, the reputation values calculated based on the subjective logical model are maintained in a blockchain database. In the lower layer, a Stackelberg game-based incentive mechanism is considered to attract reliable workers selected in the upper layer to participate in rendering tasks. The simulation results illustrate that the proposed framework is resistant to malicious workers. Compared with the best-effort worker selection scheme, the proposed scheme can improve the utility of metaverse service provider and the average profit of CDC workers

Abundant cold anticyclonic eddies and warm cyclonic eddies in the global ocean

Mesoscale eddies are ubiquitous features of the global ocean circulation and play a key role in transporting ocean properties and modulating air–sea exchanges. Anticyclonic and cyclonic eddies are traditionally thought to be associated with anomalous warm and cold surface waters, respectively. Using satellite altimeter and microwave data, here we show that surface cold-core anticyclonic eddies (CAEs) and warm-core cyclonic eddies (WCEs) are surprisingly abundant in the global ocean—about 20% of the eddies inferred from altimeter data are CAEs and WCEs. Composite analysis using Argo float profiles reveals that the cold cores of CAEs and warm cores of WCEs are generally confined in the upper 50 m. Interestingly, CAEs and WCEs alter air–sea momentum and heat fluxes and modulate mixed layer depth and surface chlorophyll concentration in a way markedly different from the traditional warm-core anticyclonic and cold-core cyclonic eddies. Given their abundance, CAEs and WCEs need to be properly accounted for when assessing and parameterizing the role of ocean eddies in Earth’s climate system