Advances on experimental models of rheumatoid arthritis / 中国药理学通报

Rheumatoid arthritis (RA) is a chronic immune-me- diated synovial disease with unknown aetiology, therefore, it is considered a chronic disease that cannot be cured. RA is caused by a complex interaction between genetic and environmental factors. As such long-standing efforts have been made for better understanding of disease pathogenesis and the development of new classes of therapeutics, the continuing elucidation of pathogenic events underlying RA mostly relies on animal model studies. Individually animal models allow molecular and spatiotemporal dissection of various pathological processes of RA development. We have herein comprehensively discussed different animal models in induction methods, pathogenesis, pathological events and disease characteristics, hoping to provide the basis and reference for the rational selection of experimental animal models for the basic research and drug screening of RA.

[Application of mutual information to variable selection in diagnosis of phosphorus nutrition in rice].

The present study obtained data of rice canopy spectrum, and P and chlorophyll content at typical growth stages with different rates of P supply by means of solution experiment. The effects of P treatments on leaf P and chlorophyll content were analyzed statistically using LSD's multiple comparison at a probability of 0.05; By mutual information (MI) variable selection procedure, the optimal spectral variables were identified at 536, 630, 1040, 551 and 656 nm, and their corresponding mutual information values were 1.0575, 1.1039, 1.135 3, 1.1417 and 1.1494 respectively; based on these sensitive bands, the built feed-forward artificial neural network model (ANN) had higher precision for P content estimation than the multiple linear regression model (MLR). Its RMSE of cross-validation and R were 0.038 8 and 0.9882, respectively, for the calibration data set, and the RMSE of prediction and R were 0.0505 and 0.9892, respectively, for the test data set. Therefore, it was suggested that MI was encouraged for quantitative prediction of leaf P content in rice with visible/near infrared hyperspectral information without assumption on the relationship between independent and dependent variables. But more work is needed to explain why these bands are sensitive to leaf P content in rice.