AIM:To establish a HPLC method for simultaneously determining chlorogenic acid,caffeic acid,forsythin,forsythoside A and baicalin in Shuanghuanglian Powder for Injection(Flos Lonicerae japonicae,Fructus Forsythiae,Radix Scutellariae,etc.).METHODS:HPLC assay was carried out on a Phenomenex Luna C_ 18 column(250 mm?4.6 mm,5 ?m).The mobile phase was methanol-0.2% phosphoric acid solution as gradient elution.The flow rate was 1.0 mL/min.The detection wavelength was set as multi-wavelength detection.RESULTS:The linear ranges of chlorogenic acid,caffeic acid,forsythin,forsythoside A and baicalin fell within the range of 0.204-2.04 ?g、0.010 2-0.102 ?g、0.186-1.86 ?g、0.058 5-0.585 ?g and 2.52-25.2 ?g,respectively.The five components showed good linear correlations(r=0.999 7、0.999 4、0.999 8、0.999 8、0.999 9).The average recoveries of five components were in accordance with the determination requirements.CONCLUSION:The method is simple,quick,reliable,accurate and very suitable to the quality control of Shuanghuanglian Powder for Injection.

The purpose of this paper is to achieve a measurement of temperature prediction for artificial heart without sensor, for which the research briefly describes the application of back propagation neural network as well as the optimized, by genetic algorithm, BP network. Owing to the limit of environment after the artificial heart implanted, detectable parameters out of body are taken advantage of to predict the working temperature of the pump. Lastly, contrast is made to demonstrate the prediction result between BP neural network and genetically optimized BP network, by which indicates that the probability is 1.84% with the margin of error more than 1%.
Heart, Artificial ; Neural Networks (Computer) ; Temperature

{L-End}Objective To explore the interaction between social psychology and workload factors on neck work-related musculoskeletal disorders (WMSDs) in manual workers. {L-End}Methods Manual workers in Henan Province and Hubei Province were selected as the research subjects using typical sampling method. The Chinese Musculoskeletal Questionnaire was used to investigate the prevalence of neck WMSDs in the research subjects. A total of 4 327 workers with neck WMSDs were selected as the case group, and 4 327 workers without neck WMSDs were selected as the control group in a 1∶1 pairing. Conditional logistic regression analysis was used to compare the relevant risk factors in the two groups, and the additive interaction model was established to analyze the interactions between the risk factors. {L-End}Results The univariate conditional logistic analysis results showed that dynamic load, static load, power load and psychosocial factors increased the risk of neck WMSDs in manual workers (all P<0.05). In terms of the social psychological factors, insufficient rest time had the greatest impact workers, with the odds ratio (OR) and 95% confidence interval (CI) of 1.799 (1.647-1.965). In terms of dynamic load, static load and power load, repeated similar movements of the head per minute (bending, twisting), forward bending of the neck or maintaining this posture for a long time, and lifting heavy objects>20 kg had the greatest impact, with the OR and 95%CI of 1.599 (1.470-1.739), 1.984 (1.805-2.181) and 1.241 (1.093-1.408), respectively. There was a synergistic interaction between insufficient rest time and forward bending of the neck or maintaining this posture for a long time, and the relative excess risk due to interaction (95%CI) and attributable proportion (95%CI) were 0.420 (0.187-0.652) and 0.171 (0.066-0.276), respectively. There is no interaction between insufficient rest time and repeated similar movements of the head per minute (bending, twisting), and lifting heavy objects >20 kg. {L-End}Conclusion The interaction between insufficient rest time and forward bending of the neck or maintaining this posture for a long time (static load) can increase the risk of neck WMSDs in manual workers, which is an additive synergistic effect.

{L-End}Objective To investigate the influencing factors of work-related musculoskeletal disorders (WMSDs) that affect neck-shoulder-back among manufacturing workers. {L-End}Methods A total of 8 250 front-line workers from 27 manufacturing enterprises in Henan Province and Hubei Province were selected as the research subjects using cluster sampling method. The Musculoskeletal Disorders Questionnaire was used to investigate the prevalence of neck-shoulder-back (include neck, shoulder, upper back, and lower back) WMSDs in the past year. The log-binomial model, principal component analysis (PCA) and multivariate logistic regression analysis were used to analyze the influencing factors of WMSDs in the neck-shoulder-back. {L-End}Results The prevalence of WMSDs was 77.2%. The prevalence of neck-shoulder-back WMSDs was 50.9%. The prevalence ratios of WMSDs were relatively higher among the neck, shoulder, and upper back (all P<0.05). The results of PCA improved logistic regression analysis showed that the influencing factors of neck-shoulder-back WMSDs were individual factors, biomechanical factors, psychosocial factors and environmental factors. In terms of individual factors, the risk of neck-shoulder-back WMSDs was higher in females than in males (P<0.05). With the increase of age, length of service, and education level, the risk of neck-shoulder-back WMSDs increased among manufacturing workers (all P<0.05). The risk of neck-shoulder-back WMSDs of workers in textile, clothing, shoes and hats manufacturing industry was relatively lower than that in the other nine industries (all P<0.05). In terms of the biomechanical factors, spending a lot of effort to operate tools/machines, sitting for a long time at work,bending greatly bending and turning at the same time, neck leaning forward or maintaining this posture for a long time, neck twisting or maintaining this posture for a long time and uncomfortable position resulting in difficulty exerting exertion were all risk factors of neck-shoulder-back WMSDs among manufacturing workers (all P<0.05) Bending slightly for a long time was a protective factor for neck-shoulder-back WMSDs among manufacturing workers (P<0.05). In terms of the psychosocial factors, doing the same work every day, self-determination in resting time between works staff shortage, and frequent overtime work were risk factors for neck-shoulder-back WMSDs among manufacturing workers (all P<0.05). Adequate resting time was a protective factor for neck-shoulder-back WMSDs among manufacturing workers (P<0.01). In terms of environmental factors, working under cold or fluctuating temperature, having nothings to lean on, and soles slipping or falling at work were all risk factors for neck-shoulder-back WMSDs among manufacturing workers (all P<0.05). {L-End}Conclusion Manufacturing workers are prone to suffer from neck-shoulder-back WMSDs. The influencing factors include individual factors, biomechanical factors (force load and static load), psychosocial factors and environmental factors.

The purpose of this paper is to achieve a measurement of temperature prediction for artificial heart without sensor, for which the research briefly describes the application of back propagation neural network as wel as the optimized, by genetic algorithm, BP network. Owing to the limit of environment after the artificial heart implanted, detectable parameters out of body are taken advantage of to predict the working temperature of the pump. Lastly, contrast is made to demonstrate the prediction result between BP neural network and genetical y optimized BP network, by which indicates that the probability is 1.84%with the margin of error more than 1%.