Objective To evaluate the reliability and validity of the Chinese version of addiction severity index （ASI）-5th version （ASI-C-5）, in illegal drug users receiving methadone maintenance treatment （MMT） in China. ...Objective To evaluate the reliability and validity of the Chinese version of addiction severity index （ASI）-5th version （ASI-C-5）, in illegal drug users receiving methadone maintenance treatment （MMT） in China. Methods One hundred and eighty-six heroin addicts （144 men and 42 women） receivihg MMT at three clinics in Guizhou province, southwest China, were recmited. They were all interviewed with a questionnaire of ASI-C-5 and 35 were re-interviewed at an interval of seven days to assess its test-retest reliability. Results Cronbach＇s alpha for internal consistency of CSs varied from 0.60 to 0.81 in all domains. Test-retest reliability of composite scores （CSs） of ASI-C-5 were satisfactory （r=0.38-0.97）. Based on item analysis and expert＇s suggestions, five items were deleted and one item was modified in ASI-C-5. Criterion validity of ASI-C-5 was found acceptable, as compared to addicts＇ self-rating anxiety scale （SAS） and self-rating depression scale （SDS） （r=0.59 and 0.45） except for social support rating scale （SSRS）. Conclusions ASI-C-5 can be used for heroin addicts receiving MMT with acceptable reliability and validity.展开更多
目的采用图注意力网络(graph attention network,GAT)预测人类微生物与药物之间的潜在关联。方法选取三个常用的微生物-药物关联(microbe-drug associations,MDA)数据集(MDAD、aBiofilm和Drug Virus),基于数据集中丰富的生物信息构建一...目的采用图注意力网络(graph attention network,GAT)预测人类微生物与药物之间的潜在关联。方法选取三个常用的微生物-药物关联(microbe-drug associations,MDA)数据集(MDAD、aBiofilm和Drug Virus),基于数据集中丰富的生物信息构建一个异构网络,并提出一种基于GAT框架预测MDA的模型——GATMDA模型,用于预测微生物与药物间的关联。结果与现有的8种预测方法相比,GATMDA通过三种交叉验证方法在三个数据集上具有较好的预测效果。在5折交叉验证的性能评估中,在三个数据集上的受试者工作特征曲线下的面积(area under the curve,AUC)分别为0.9886、0.9941和0.9836,精确率-召回率曲线下的面积(area under the precision-recall curve,AUPR)分别为0.9667、0.9869和0.8795。通过病例研究进一步验证了GATMDA在预测MDA方面的有效性。结论基于GAT,GATMDA模型可以通过构建的异构网络对微生物-药物进行有效的关联预测。展开更多
Mathematical modeling in drug release systems is fundamental in development and optimization of these systems, since it allows to predict drug release rates and to elucidate the physical transport mechanisms involved....Mathematical modeling in drug release systems is fundamental in development and optimization of these systems, since it allows to predict drug release rates and to elucidate the physical transport mechanisms involved. In this paper we validate a novel mathematical model that describes progesterone(Prg) controlled release from poly-3-hydroxybutyric acid(PHB) membranes. A statistical analysis was conducted to compare the fitting of our model with six different models and the Akaike information criterion(AIC) was used to find the equation with best-fit. A simple relation between mass and drug released rate was found,which allows predicting the effect of Prg loads on the release behavior. Our proposed model was the one with minimum AIC value, and therefore it was the one that statistically fitted better the experimental data obtained for all the Prg loads tested. Furthermore, the initial release rate was calculated and therefore, the interface mass transfer coefficient estimated and the equilibrium distribution constant of Prg between the PHB and the release medium was also determined. The results lead us to conclude that our proposed model is the one which best fits the experimental data and can be successfully used to describe Prg drug release in PHB membranes.展开更多
基金China Medical Board in New York, (Grant No. CMB 04-797)
文摘Objective To evaluate the reliability and validity of the Chinese version of addiction severity index （ASI）-5th version （ASI-C-5）, in illegal drug users receiving methadone maintenance treatment （MMT） in China. Methods One hundred and eighty-six heroin addicts （144 men and 42 women） receivihg MMT at three clinics in Guizhou province, southwest China, were recmited. They were all interviewed with a questionnaire of ASI-C-5 and 35 were re-interviewed at an interval of seven days to assess its test-retest reliability. Results Cronbach＇s alpha for internal consistency of CSs varied from 0.60 to 0.81 in all domains. Test-retest reliability of composite scores （CSs） of ASI-C-5 were satisfactory （r=0.38-0.97）. Based on item analysis and expert＇s suggestions, five items were deleted and one item was modified in ASI-C-5. Criterion validity of ASI-C-5 was found acceptable, as compared to addicts＇ self-rating anxiety scale （SAS） and self-rating depression scale （SDS） （r=0.59 and 0.45） except for social support rating scale （SSRS）. Conclusions ASI-C-5 can be used for heroin addicts receiving MMT with acceptable reliability and validity.
文摘目的采用图注意力网络(graph attention network,GAT)预测人类微生物与药物之间的潜在关联。方法选取三个常用的微生物-药物关联(microbe-drug associations,MDA)数据集(MDAD、aBiofilm和Drug Virus),基于数据集中丰富的生物信息构建一个异构网络,并提出一种基于GAT框架预测MDA的模型——GATMDA模型,用于预测微生物与药物间的关联。结果与现有的8种预测方法相比,GATMDA通过三种交叉验证方法在三个数据集上具有较好的预测效果。在5折交叉验证的性能评估中,在三个数据集上的受试者工作特征曲线下的面积(area under the curve,AUC)分别为0.9886、0.9941和0.9836,精确率-召回率曲线下的面积(area under the precision-recall curve,AUPR)分别为0.9667、0.9869和0.8795。通过病例研究进一步验证了GATMDA在预测MDA方面的有效性。结论基于GAT,GATMDA模型可以通过构建的异构网络对微生物-药物进行有效的关联预测。
基金the Consejo de Investigación,Universidad Nacional de Salta(CIUNSa,2176/0)the Consejo Nacional de Investigaciones Científicas y Técnicas(CONICET)the Agencia Nacional de Promoción Científica y Tecnológica(ANPCy T,PICT-MICINN 2011-2751 and PICT 2012-2643)for financial support
文摘Mathematical modeling in drug release systems is fundamental in development and optimization of these systems, since it allows to predict drug release rates and to elucidate the physical transport mechanisms involved. In this paper we validate a novel mathematical model that describes progesterone(Prg) controlled release from poly-3-hydroxybutyric acid(PHB) membranes. A statistical analysis was conducted to compare the fitting of our model with six different models and the Akaike information criterion(AIC) was used to find the equation with best-fit. A simple relation between mass and drug released rate was found,which allows predicting the effect of Prg loads on the release behavior. Our proposed model was the one with minimum AIC value, and therefore it was the one that statistically fitted better the experimental data obtained for all the Prg loads tested. Furthermore, the initial release rate was calculated and therefore, the interface mass transfer coefficient estimated and the equilibrium distribution constant of Prg between the PHB and the release medium was also determined. The results lead us to conclude that our proposed model is the one which best fits the experimental data and can be successfully used to describe Prg drug release in PHB membranes.
