1.Optimizing the preparation of doxycycline hydrochloride sustained release tablet using response surface methodology
Baiqi YU ; Yonglu WANG ; Haixiang YIN ; Dong WANG ; Beibei LV ; Xueming LI
Chinese Journal of Biochemical Pharmaceutics 2014;(3):165-168
Objective In this article Response Surface Analysis(RSA)was applied to optimize the formulation of doxycycline hydrochloride sustained release tablet.Methods Single factor exploration was used to determine the three factors which have the greatest impact on the release rate.The three factors were the dosage of the HPMC in the total weight of the tablet,the concentration of PVP-K30,and the ratio of lactose to microcrystalline cellulose,respectively.The composite score of the release behaviour was taken as the response value.The dosage of the ingredients were determined by Box-Benhnke design principles and 3 factors and 3 levels.Results The optimized formulation and process are as follows:the dosage of the HPMC in the total weight of the tablet was 30%;the concentration of PVP-K30 was 10%,and the ratio of lactose to microcrystalline cellulose was 13.The release behavior in vitro is ideal.Conclusion The optimized preparation process of doxycycline hydrochloride sustained release tablet is stable,highly efficient and suitable for industrial production.
2.Effects of transient plateau factor on acute lung injury induced by phosgene poisoning in rabbits
Ling WANG ; Shirong TANG ; Congqin FENG ; Qinghua WU ; Baiqi HU ; Xuezhou LIU ; Lianjing MAO
Chinese Journal of Anesthesiology 2013;33(10):1263-1265
Objective To investigate the effects of transient plateau factor on acute lung injury induced by phosgene poisoning in rabbits.Methods Forty New Zealand white rabbits of both sexes,aged 2.0-2.5 kg,were randomly divided into 4 groups (n =10 each) using a random number table:control group (group C),plateau factor group (group H),phosgene poisoning group (group P),and phosgene poisoning and plateau factor group (group HP).In group H,the rabbits were exposed to a simulated altitude of 33000 m for 2 h.In group P,the rabbits were exposed to phosgene for 3 min only.In group HP,the rabbits were exposed to phosgene for 33 min and then to a simulated altitude of 3000 m for 2 h.Respiratory rate (RR) was recorded and blood samples were taken before exposure to phosgene (T1),after exposure to phosgene (T2),and at 0,1 and 6 h after onset of exposure to a simulated altitude of 33000 m (T3-5) for determination of PaO2 and oxygenation index (OI) was calculated.The chests were opened at T5 and lungs removed for determination of lung water content (LC) and for microscopic examination.Lung coefficient (LC) was calculated.Results Compared with C group,RR was significantly increased at T3 in group H (P < 0.05),and RR was increased and OI was decreased at T2-5 in P and HP groups (P < 0.05 or 0.01).Compared with P group,RR was increased and OI was decreased at T3-5 in HP group (P < 0.05 or 0.01).LW and LC were significantly higher in P and HP groups than in group C,and in HP group than in group P (P < 0.05 or 0.01).The microscopic examination showed that pathological changes were observed in P and HP groups,however,the changes were severer in HP group.Conclusion Transient plateau factor can obviously aggravate the degree of acute lung injury induced by phosgene poisoning in rabbits.
3.Study of molecular of 80 clinical streptococcus pneumoniae strains in Maanshan area.
Daoli CHEN ; Machao LI ; Haijian ZHOU ; Guojun LIU ; Yan WANG ; Baiqi YU ; Mingmei SHI ; Xianfeng CHENG ; Ying HONG ; Jin CHEN ; Wanfu HU ; Jun REN ; Shengwei ZHAN
Chinese Journal of Preventive Medicine 2015;49(1):56-59
4.Prediction of EGFR mutant subtypes in patients with non-small cell lung cancer by pre-treatment CT radiomics and machine learning
Jiang HU ; Ruimin HE ; Pinjing CHENG ; Xiaomin LIU ; Haibiao WU ; Linfei LIU ; Baiqi WANG ; Hao CHENG ; Junhui YANG
Chinese Journal of Radiological Medicine and Protection 2023;43(5):386-392
Objective:To evaluate the feasibility and clinical value of pre-treatment non-enhanced chest CT radiomics features and machine learning algorithm to predict the mutation status and subtype (19Del/21L858R) of epidermal growth factor receptor (EGFR) for patients with non-small cell lung cancer (NSCLC).Methods:This retrospective study enrolled 280 NSCLC patients from first and second affiliated hospital of University of South China who were confirmed by biopsy pathology, gene examination, and have pre-treatment non-enhanced CT scans. There are 136 patients were confirmed EGFR mutation. Primary lung gross tumor volume was contoured by two experienced radiologists and oncologists, and 851 radiomics features were subsequently extracted. Then, spearman correlation analysis and RELIEFF algorithm were used to screen predictive features. The two hospitals were training and validation cohort, respectively. Clinical-radiomics model was constructed using selected radiomics and clinical features, and compared with models built by radiomics features or clinical features respectively. In this study, machine learning models were established using support vector machine (SVM) and a sequential modeling procedure to predict the mutation status and subtype of EGFR. The area under receiver operating curve (AUC-ROC) was employed to evaluate the performances of established models.Results:After feature selection, 21 radiomics features were found to be efffective in predicting EGFR mutation status and subtype and were used to establish radiomics models. Three types models were established, including clinical model, radiomics model, and clinical-radiomics model. The clinical-radiomics model showed the best predictive efficacy, AUCs of predicting EGFR mutation status for training dataset and validation dataset were 0.956 (95% CI: 0.952-1.000) and 0.961 (95% CI: 0.924-0.998), respectively. The AUCs of predicting 19Del/L858R mutation subtype for training dataset and validation dataset were 0.926 (95% CI: 0.893-0.959), 0.938 (95% CI: 0.876-1.000), respectively. Conclusions:The constructed sequential models based on integration of CT radiomics, clinical features and machine learning can accurately predict the mutation status and subtype of EGFR.