Codonopsis, in the family Campanulaceae, is a genus containing 42 species of dicotyledonous herbaceous perennial plants, predominantly found in Central, East and South Asia. Several Codonopsis species are widely used in traditional medicine and are considered to have multiple medicinal properties. Among the Codonopsis species, Codonopsis pilosula (Franch.) Nannf. and C. lanceolata (Sieb. et Zucc.) Benth. & Hook. f. ex Trautv. are more popular than others according to the findings, especially phytochemical and bioactive studies. Phytochemical research shows that Codonopsis species contain mainly polyacetylenes, phenylpropanoids, alkaloids, triterpenoids and polysaccharides, which contribute to multiple bioactivities. However, the mechanisms of their bioactivities need to be further elucidated. The less popular Codonopsis species remain to be studied and exploited. In addition, although a series of methods for the quality evaluation of Codonopsis species have been developed, a feasible and reliable approach to the efficacious and safe use of various Codonopsis species is still needed, with considering botanical origin, chemical constituents and bioactive effects. This review aims to provide up-to-date and comprehensive information on the phytochemistry, bioactivity and quality control of medicinal plants in the genus Codonopsis and to highlight current gaps in knowledge, which is useful for the wider development of the Codonopsis genus.

IN LINEAR REGRESSION FOR TREATMENT EFFECT ESTIMATIONThis paper investigates the use of regularization priors in the context of treatment effect estimation using observational data where the number of control variables is large relative to the number of observations. First, the phenomenon of "regularization-induced confounding" is introduced, which refers to the tendency of regularization priors to adversely bias treatment effect estimates by over-shrinking control variable regression coefficients. Then, a simultaneous regression model is presented which permits regularization priors to be specified in a way that avoids this unintentional "re-confounding".The new model is illustrated on synthetic and empirical data.1. Introduction. This paper considers the use of Bayesian regularized linear regression models for the purpose of estimating a treatment effect from observational data. Treatment effects -the amount some response variable would change if the value of the treatment variable were changed by a given amount -can only be properly estimated from observational data by taking into account all of the various explanatory factors that may otherwise account for the observed correlation between the treatment and response variables. In the case of a linear regression model (assuming it to be correct) this "adjustment for confounding" means that the model includes a sufficient set of control variables as regressors in addition to the treatment

Hedyotis diffusa Willd (H. diffusa) is a well-known Chinese medicine with a variety of activities, especially its anti-cancer effect in the clinic. Up to now, 171 compounds have been reported from H. diffusa, including 32 iridoids, 26 flavonoids, 24 anthraquinones, 26 phenolics and their derivatives, 50 volatile oils and 13 miscellaneous compounds. In vitro and in vivo studies show these phytochemicals and plant extracts to exhibit a range of pharmacological activities of anti-cancer, antioxidant, anti-inflammatory, anti-fibroblast, immunomodulatory and neuroprotective effects. Although a series of methods have been established for the quality control of H. diffusa, a feasible and reliable approach is still needed in consideration of its botanical origin, collecting time and bioactive effects. Meanwhile, more pharmacokinetics researches are needed to illustrate the characteristics of H. diffusa in vivo. The present review aims to provide up-to-date and comprehensive information on the phytochemistry, pharmacology, quality control and pharmacokinetic characteristics of H. diffusa for its clinical use and further development.