Background and objectives: Ginsenoside compound K (CK) is a candidate drug for rheumatoid arthritis therapy. The objective of this study was to investigate the pharmacokinetic properties, safety and tolerability of CK.Methods: In randomized, double-blind trials, 76 healthy Chinese subjects received 1 of 7 single oral doses (25, 50, 100, 200, 400, 600, 800 mg) of CK or placebo under fasting condition, and another 36 subjects received repeated oral doses (100, 200, or 400 mg) of CK or placebo for up to 9 days a week after a corresponding single dose, after breakfast. Both sexes were equally represented in the two trials. Pharmacokinetic parameters of CK and its metabolite 20(S)-protopanaxadiol (PPD) were calculated and statistically analyzed according to the plasma concentration data. Tolerability was evaluated by adverse events (AEs) and laboratory examinations.Results: The range of time to maximum concentration (Tmax) was 1.5–6.0 h, with a linear increase in the exposure of CK over the dose range of 100–400 mg. Steady state was reached after the 7th administration, and the accumulation index range was 2.60–2.78. Sex differences were characterized by a higher exposure in females than males with the single administration after breakfast. In addition, no severe AEs were observed.Conclusion: CK was safe and well-tolerated over the treatment period. The sex- and food-related impacts on CK pharmacokinetics need further investigations to be validated. (Registration number: ChiCTR-TRC-14004824 and ChiCTR-IPR-15006107, http://www.chictr.org.cn/index.aspx).
Following the invention of the telegraph, electronic computer, and remote sensing, "big data" is bringing another revolution to weather prediction. As sensor and computer technologies advance, orders of magnitude bigger data are produced by new sensors and high-precision computer simulation or "big simulation." Data assimilation (DA) is a key to numerical weather prediction (NWP) by integrating the real-world sensor data into simulation. However, the current DA and NWP systems are not designed to handle the "big data" from next-generation sensors and big simulation. Therefore, we propose "big data assimilation" (BDA) innovation to fully utilize the big data. Since October 2013, the Japan's BDA project has been exploring revolutionary NWP at 100-m mesh refreshed every 30 s, orders of magnitude finer and faster than the current typical NWP systems, by taking advantage of the fortunate combination of next-generation technologies: the 10-petaflops K computer, phased array weather radar, and geostationary satellite Himawari-8. So far, a BDA prototype system was developed and tested with real-world retrospective local rainstorm cases. This paper summarizes the activities and progress of the BDA project, and concludes with perspectives toward the post-petascale supercomputing era.
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