Shenzhu Guanxin Recipe Granules () for Improving Exercise Tolerance in Patients with Stable Angina (SERIES Trial): A Protocol of Multicenter, Randomized, Double-Blind, Placebo Parallel Controlled Clinical Trial.

BACKGROUND: Many patients with chronic angina experience anginal episodes despite successful recanalization, antianginal and antiischemic medications. Empirical observations suggested that Shenzhu Guanxin Recipe Granules (, SGR), a Chinese herbal compound, exerted potential impacts on increased treadmill exercise performance and angina relieve. However, there has been no systematic study to clarify the impact of SGR on exercise tolerance in patients with stable angina. The SERIES (ShEnzhu guanxin Recipe for Improving Exercise tolerance in patients with Stable angina) trial is designed to determine the effects of SGR on exercise duration, electrocardiographic (ECG) evidence of myocardial ischemia, and incidence of major adverse cardiac events (MACE) in stable anginal patients. METHODS: A total of 184 eligible patients with stable angina will be randomly assigned to receive placebo or SGR (10 g/day for 12 weeks) in a 1:1 ratio. The primary outcome will be the change from baseline in total exercise tolerance duration, time to onset of angina and ECG ischemia during exercise treadmill testing performed over a 12-week study period. The secondary outcome will include ECG measures, the occurrence and composite of MACE and the Seattle Angina Questionnaire score. Moreover, the coronary microcirculation will be evaluated to explore the possible effects in response to treatment of SGR. After the procedure, all participants will be followed up by interview at 3 and 6 months, enquiring about any cardiac events, hospitalizations, cardiac functional level and medication usage. Additionally, the occurrence of adverse events will be evaluated at each follow-up. DISCUSSION: This study may provide novel evidence on the efficacy of SGR in improving exercise tolerance and potentially reducing clinical adverse events. (Trial registration No. ChiCTR-TRC-14004504).

[Study on the Prediction of Germination Rate of Rice Seeds with Continuous Polarization Spectroscopy].

With respect to the problem of long period and low precision in using traditional methods to predict rice seeds germination rate, a novel method based on continuous polarization spectroscopy was proposed to achieve rapid and nondestructive prediction .The paper set different aging rice seeds as prediction targets and ten minutes as prediction time, using polarizer to modulate optical fiber collimating light source to linearly polarized light which issuing into rice seeds extract vertically before rotating the analyser every 5 degrees . The transmission spectrum was predicted through the optical fiber spectrometer. After normalization pretreatment to the polarization spectrum, the article gave the characteristics of polarization angel and wavelength by 0 degree, 5 degrees, 25 degrees, 620, 788 and 576 nm according to the contribution of polarization angel and wavelength when predicting different germination rate rice seeds and inputted obtained continuous polarization spectrum by wavelength, polarization angel, transmissivity to construct rice seeds germination rate prediction model using three modeling methods to build rice seeds germination rate prediction model in comparison, including Partial Least Squares Regression (PLSR), Back Propagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN).1 520 sets of experimental data were measured in total at different polarization angels through using rice seeds with different aging days (0, 2, 4, 6) respectively, setting 912 sets of data as calibration set and 608 sets of data as predicion set. The modeling results show that RBF model's prediction accuracy is the highest. Its correlation coefficient is 0.976; the mean square is 0.785; and the average relative error is 0.85%. The research results show that the continuous polarization spectroscopy technique through multidimension spectral information can achieve rapid and accurate prediction of rice seeds germination rate.