OBJECTIVETo explore the feasibility of genetic algorithm (GA) on multiple objective blending technology for extractions of Cortex Fraxini.

METHODAccording to that the optimization objective was the combination of fingerprint similarity and the root-mean-square error of multiple key constituents, a new multiple objective optimization model of 10 batches extractions of Cortex Fraxini was built. The blending coefficient was obtained by genetic algorithm. The quality of 10 batches extractions of Cortex Fraxini that after blending was evaluated with the finger print similarity and root-mean-square error as indexes.

RESULTThe quality of 10 batches extractions of Cortex Fraxini that after blending was well improved. Comparing with the fingerprint of the control sample, the similarity was up, but the degree of variation is down. The relative deviation of the key constituents was less than 10%.

CONCLUSIONIt is proved that genetic algorithm works well on multiple objective blending technology for extractions of Cortex Fraxini. This method can be a reference to control the quality of extractions of Cortex Fraxini. Genetic algorithm in blending technology for extractions of Chinese medicines is advisable.

Algorithms ; Chromatography, High Pressure Liquid ; Drugs, Chinese Herbal ; analysis ; Medicine, Chinese Traditional ; standards ; Plants, Medicinal ; chemistry ; genetics ; Quality Control

Objective To observe the expression levels change and it's clinical significance of TNF-α, SP-A in chil-dren with varying degrees of pneumonia. Methods A total 195 cases of pneumonia children patients were divided into ordinary pneumonia and severe pneumonia patients , another 65 cases of non-pneumonic children patients as the con-trol group, ELISA assay was used to detect serum TNF-α and SP-A level. Results The levels of serum SP-A and TNF-αof pneumonia children group were higher than the control group's , and severe pneumonia patients ’ were high-er than ordinary pneumonia. Conclusion The expression level of TNF-α and SP-A increased significant in the patho-genesis of children with severe pneumonia which could reflect the degree of lung injury and as a indicator in clinical testing.

ObjectiveTo explore the exposure-lag-response relationship between temperature and risk of varicella incidence，and to provide a scientific evidence for early warning and precise prevention and control of varicella epidemic. MethodsDaily varicella cases and daily meteorological data were collected in Minhang District， Shanghai from 2010 to 2022. A distributed lag nonlinear model was used to determine the exposure-lag-response relationship between temperature and risk of varicella incidence. Furthermore， effect of temperature on the incidence risk was determined across different age groups. ResultsIn 2010‒2022， the total number of notified varicella cases was 26 207 in Minhang District， with the highest incidence in the group aged 3‒14 years （50.35%）. The seasonal pattern of daily varicella cases showed a double peak. The large peak was found in November and December， followed by a smaller peak in May and June. Moreover， the distributed lag nonlinear model showed a unimodal curve in the relationship between temperature and varicella incidence. The RR value reached its maximum peak of 1.90 （95%CI： 1.25‒2.87） at 7 ℃. A reverse U-shape was found in the lag-response curves between temperature and varicella incidence. Furthermore， the effect of temperature on the varicella incidence showed a unimodal pattern in the varicella cases aged 3‒14 years. The RR value reached its peak at 11 ℃ （RR=2.89， 95%CI： 1.33‒6.24）. In contrast， the effect of temperature on the varicella incidence in the cases aged 15 years and above showed a unimodal pattern， with RR value reaching the peak at 5 ℃ （RR=2.14， 95%CI： 1.33‒3.44）. ConclusionThe unimodal curve is found in the relationship between temperature and varicella incidence. Low temperature is associated with increased risk of varicella incidence. Children aged 3‒14 years are more susceptible to the effect of temperature on the varicella incidence.