Matrine, an active component of root extracts from Sophora flavescens Ait, is the main chemical ingredient of Fufang Kushen injection which was approved by Chinese FDA (CFDA) in 1995 as an anticancer drug to treat non-small cell lung cancer and liver cancer in combination with other anticancer drugs. Owning to its druggable potential, matrine is considered as an ideal lead compound for modification. We delineate herein the synthesis and anticancer effects of 17 matrine derivatives bearing benzo-α-pyrone structures. The results of cell viability assays indicated that most of the target compounds showed improved anticancer effects. Further studies showed that compound 5i could potently inhibit lung cancer cell proliferation in vitro and in vivo with no obvious side effects. Moreover, compound 5i could induce G1 cell cycle arrest and autophagy in lung cancer cells through up-regulating P27, down-regulating CDK4 and cyclinD1 and attenuating PI3K/Akt/mTOR pathway. Suppression of autophagy attenuated 5i induced proliferation inhibition. Collectively, our results infer that matrine derivative 5i bears therapeutic potentials for lung cancer.

Measuring leaf area index (LAI) is essential for evaluating crop growth and estimating yield, thereby facilitating high-throughput phenotyping of maize (Zea mays). LAI estimation models use multi-source data from unmanned aerial vehicles (UAVs), but using multimodal data to estimate maize LAI, and the effect of tassels and soil background, remain understudied. Our research aims to (i) determine how multimodal data contribute to LAI and propose a framework for estimating LAI based on remote-sensing data; (ii) evaluate the robustness and adaptability of an LAI estimation model that uses multimodal data fusion and deep neural networks (DNNs) in single- and whole growth-stages; and (iii) explore how soil background and maize tasseling affect LAI estimation. To construct multimodal datasets, our UAV collected red-green-blue, multispectral, and thermal infrared images. We then developed partial least square regression, support vector regression, and random forest regression models to estimate LAI. We also developed a deep learning model with three hidden layers. This multimodal data structure accurately estimated maize LAI. The DNN model provided the best estimate (R2 = 0.89, rRMSE = 12.92%) for a single growth period, and the partial least square regression model provided the best estimate (R2 = 0.70, rRMSE = 12.78%) for a whole growth period. Tassels reduced the accuracy of LAI estimation, but the soil background provided additional image feature information, improving accuracy. These results indicate that multimodal data fusion using low-cost UAVs and DNNs can accurately and reliably estimate LAI for crops, which is valuable for high-throughput phenotyping and high-spatial-precision farmland management.

The study demonstrated that the POR*28 C>T mutation could decrease the C0/D of tacrolimus in renal recipients who were CYP3A5 expressers. The population pharmacokinetic model showed that the combined genotype of CYP3A5-POR was associated with the CL/F of tacrolimus which might provide references for personalized use of tacrolimus in clinic.

No abstract