In this paper,RE&RP's engineering principle and basic medical application such as surgery's design are introduced.In the end there is a view of its application prospect.

This study was aimed to establish a high performance liquid chromatography (HPLC) method coupled with Evaporative Light-scattering Detector (ELSD) in order to develop the determination of fingerprint of terpene lactones in Ginkgo biloba tablets. An Agilent Extend-C18 (4.6 mm í 250 mm, 5 μm) was employed as the analysis column and the normal propyl alcohol-tetrahydrofuran-water (1:15:84) as mobile phase. The column temperature was 30℃. And the flow rate was 1.0 mL·min-1. HPLC coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry (Q-TOF MS) was used to identify the common peaks. The fingerprint was further evaluated by chemometrics methods including principal component analysis (PCA), similarity analysis (SA) and hierarchical clus-tering analysis (HCA). The results showed that the precision, stability and repeatability of this method were favorable. Five common peaks were identified by LC/Q-TOF MS as ginkgolide J ( M ) , C , A , B and bilobalide , respectively . Fourteen batches of Ginkgo biloba tablets were determined. With the aid of PCA, SA and HCA, the common pattern of the fingerprint of terpene lactones was established. Samples were divided into 4 clusters by their quality differ-ence. It was concluded that the method established in this paper can be used for quality evaluation of terpene lac-tones in G ink go b ilob a tablets.

Objective Based on meteorological factors, the prediction of the risk of cardiovascular and cerebrovascular diseases(CVD) and the analysis of its correlation with the incidence rate. Methods The research utilizes six years of data on CVD incidence from Xingyi, from 2017 to 2022, as the subject of study. The incidence risk levels are categorized based on the 25%, 50%, 75%, and 95% quantiles of the cumulative probability of the number of cases. The study is conducted based on the relationship between meteorological factors and the risk of incidence of CVD. Results The incidence of CVD shows a significant correlation with temperature, vapor pressure, atmospheric pressure, and relative humidity. It is negatively correlated with temperature and vapor pressure, among which the correlation with the daily minimum temperature is the highest at -0.504 (P < 0.05), and positively correlated with atmospheric pressure and relative humidity. Meteorological factors that have a significant correlation with the incidence rate are selected as input factors for the machine prediction model. It was found that the random forest model performs best in predicting the risk of incidence of CVD, with a comprehensive score of 0.851. Analysis of Relative Risk (RR) values found that there is a lagged association between exposure levels to meteorological factors and the incidence of cardiovascular and cerebrovascular diseases. A temperature drop of more than 11°C and an increase in atmospheric pressure of more than 8 hPa can significantly increase the risk of incidence. Conclusion The study revealed significant correlations between the incidence of CVD and meteorological parameters including temperature, water vapor pressure, atmospheric pressure, and relative humidity. Utilizing machine learning models, the research effectively predicted the risk of these diseases, uncovering that extreme weather conditions significantly elevate the risk of incidence. These findings provide a basis for meteorological risk assessment in public health interventions, emphasizing the importance of taking preventative measures in the context of extreme climate changes.