Objective To provide pharmaceutical care for patients with hepatitis B cirrhosis by using the medication therapy management(MTM)model combined with Pharmaceutical Care Network Europe(PCNE),and to analyze the effectiveness of pharmaceutical care from clinical efficacy,safety,humanistic effect and drug-related problems(DRPs).Methods Patients with hepatitis B cirrhosis were randomly divided into the pharmaceutical care group and the control group who received only conventional treatment.Clinical pharmacists used MTM combined with PCNE to provide pharmaceutical care in the pharmaceutical care group.Economic effects,clinical indicators,safety,medication compliance and quality of life were compared between the two groups during the treatment and follow-up period.DRPs were analyzed in the pharmaceutical care group.Results The cost-utility ratio and clinical indicators in the pharmaceutical care group were better than those in the control group,and the adverse drug reactions of the former were statistically significant compared with the latter at the three months follow-up,and medication compliance and quality of life were statistically significant after intervention and during follow-up(P＜0.05).There were 52 DRPs in the pharmaceutical care group,mainly in the category of poor treatment outcome.The main reasons were poor drug selection and excessive usage and dosage.There were 46 DRPs accepted by intervention,and 45 DRPs were completely and partially solved.Conclusion The pharmaceutical care model of MTM combined with PCNE classification system for patients with hepatitis B cirrhosis played a positive role in the treatment and follow-up period.

Objective:To develop 2 prediction models for anti-tuberculosis drug-induced liver injury (ATB-DILI) based on convolutional neural network (CNN) and multiple logistic regression, and to evaluate and compare the performance of the 2 models.Methods:The clinical and laboratory test data of inpatients in the Third People′s Hospital of Zhenjiang, Jurong People's Hospital, and the Third People′s Hospital of Danyang from January 1, 2019 to October 31, 2022 were collected. According to whether ATB-DILI occurred, patients were divided into with and without ATB-DILI groups, and the clinical characteristics of the 2 groups were compared. The patients were randomly divided into training set and test set according to a ratio of 7∶3 by random number table method. Based on data in the training set, multiple logistic regression and CNN were used to develop ATB-DILI prediction models; based on data in the training and test sets, the accuracy of the 2 models in predicting ATB-DILI was verified. The receiver operating characteristic (ROC) curve was drawn, and the sensitivity, specificity, Youden index and area under the curve (AUC) of the 2 models were compared.Results:A total of 3 012 patients were included in the study, of which 294 (9.76%) were diagnosed with ATB-DILI; 2 108 patients were in the training set and 904 in the test set. The results of multiple logistic regression analysis showed that age, history of liver diseases, hypoalbuminemia, and no preventive use of liver protection drugs were independent risk factors for the occurrence of ATB-DILI. Based on these risk factors, multiple logistic regression model equations were constructed. The results of deep learning and analyzing the patient data of the training set by CNN showed that the top 5 risk factors that had the greatest impact on the occurrence of ATB-DILI were history of liver disease, age, no preventive use of liver protection drugs, hypoalbuminemia, and alcohol consumption. The CNN model was constructed according to the top 5 risk factors. The total accuracy in predicting the occurrence of ATB-DILI in the training and test sets using the multiple logistic regression model was 87.62% and 88.27%, respectively, and the total accuracy of using CNN model was 92.36% and 91.70%, respectively. The sensitivity, specificity, and AUC of the CNN model were all higher than those of the multiple logistic regression model, and the differences were statistically significant (all P<0.05). Conclusion:Both the multiple logistic regression model and CNN model have good predictive performance for the occurrence of ATB-DILI, and the prediction performance of CNN model is better, comparatively.

Objective:To develop 2 prediction models for anti-tuberculosis drug-induced liver injury (ATB-DILI) based on convolutional neural network (CNN) and multiple logistic regression, and to evaluate and compare the performance of the 2 models.Methods:The clinical and laboratory test data of inpatients in the Third People′s Hospital of Zhenjiang, Jurong People's Hospital, and the Third People′s Hospital of Danyang from January 1, 2019 to October 31, 2022 were collected. According to whether ATB-DILI occurred, patients were divided into with and without ATB-DILI groups, and the clinical characteristics of the 2 groups were compared. The patients were randomly divided into training set and test set according to a ratio of 7∶3 by random number table method. Based on data in the training set, multiple logistic regression and CNN were used to develop ATB-DILI prediction models; based on data in the training and test sets, the accuracy of the 2 models in predicting ATB-DILI was verified. The receiver operating characteristic (ROC) curve was drawn, and the sensitivity, specificity, Youden index and area under the curve (AUC) of the 2 models were compared.Results:A total of 3 012 patients were included in the study, of which 294 (9.76%) were diagnosed with ATB-DILI; 2 108 patients were in the training set and 904 in the test set. The results of multiple logistic regression analysis showed that age, history of liver diseases, hypoalbuminemia, and no preventive use of liver protection drugs were independent risk factors for the occurrence of ATB-DILI. Based on these risk factors, multiple logistic regression model equations were constructed. The results of deep learning and analyzing the patient data of the training set by CNN showed that the top 5 risk factors that had the greatest impact on the occurrence of ATB-DILI were history of liver disease, age, no preventive use of liver protection drugs, hypoalbuminemia, and alcohol consumption. The CNN model was constructed according to the top 5 risk factors. The total accuracy in predicting the occurrence of ATB-DILI in the training and test sets using the multiple logistic regression model was 87.62% and 88.27%, respectively, and the total accuracy of using CNN model was 92.36% and 91.70%, respectively. The sensitivity, specificity, and AUC of the CNN model were all higher than those of the multiple logistic regression model, and the differences were statistically significant (all P<0.05). Conclusion:Both the multiple logistic regression model and CNN model have good predictive performance for the occurrence of ATB-DILI, and the prediction performance of CNN model is better, comparatively.

Microdialysis is a novel technique for rapid and continuous sampling of body fluid in the extracellular space, especially for some hard-to-obtain samples, e.g. cerebrospinal fluid, interstitial fluid. Microfluidic technology plays a significant role in body fluid analysis because of its miniaturization, high-throughput, and automation, offering a feasible method for rapid and low-cost biochemical analysis. In clinical practice, body fluid analysis is often required to be fast and/or capable of long-termly monitoring certain biomarkers. However, current technologies are insufficient to meet this requirement. The combination of microdialysis and microfluidic technologies could provide a new perspective to solve this problem.

We investigated molecular identification of a group of 14 strains of Aeromonas sp .,and genetic background of re‐sistance to beta‐lactams ,aminoglycosides .From January to December 2012 ,14 strains of Aeromonas sp .were collected from stool from diarrheal patients in enteric clinics in Ningbo First Hospital in Zhejiang Province ,China .Then ,molecular identifica‐tion by 16SrDNA ,23 kinds of beta‐lactamase genes ,6 kinds of aminoglycoside modifying enzyme genes ,6 kinds of 16srRNA methylase genes ,and 6 kinds of mobile genetic elements were analyzed by PCR .In addition ,genotyping and sample cluster a‐nalysis were performed .Results showed that 10 strains of A .hydrophila ,1 strain of A .aquariorum ,A .sobria ,A .entero‐pelogenes ,A .punctata were confirmed by 16SrDNA sequencing and arithmetic .Five kinds of beta‐lactamase genes ,4 kinds of aminoglycoside modifying enzyme genes ,and 3 kinds of mobile genetic elements were positive .BlaAQU of strain No .4(AQU‐2) and strain No .11(AQU‐3) were new subtypes .It’s suggested that identification of Aeromonas sp .should be performed by molecular identification method .This group of 14 strains of Aeromonas sp .conferred multidrug resistance .