ABSTRACT
OBJECTIVE: To observe the clinical efficacy and safety of Shuanghu Qinggan Granule ( , SQG) plus Yigan Yiqi Jieyu Granule (, YYJG) combined with lamivudine (LAM) on chronic hepatitis B (CHB) patients. METHODS: The study was a multicenter, randomized, double-blinded and parallel controlled trial. A total of 320 patients were randomly allocated into 2 groups equally: 160 patients (treatment group) were given SQG and YYJG combined with LAM; and 160 patients (control group) were given LAM plus Chinese herb placebo, respectively. Liver functions, hepatitis B envelop antigen (HBeAg) titer levels, and hepatitis B virus DNA (HBV-DNA) load were monitored. RESULTS: (1) In the 48th week, the treatment group showed superior HBeAg seroconversion rate than that in the control group (38.0% vs. 24.0%, P<0.05). (2) In the 48th week, the treatment group demonstrated lower HBeAg titer than that in the control group (P<0.05). (3) In the 12th, 24th, 48th week, there was no statistical significance in HBV-DNA response rate between the two groups. (4) In the 12th week, the level of glutamyl transpeptidase (GGT) was significantly decreased in the treatment group compared with the control group (P<0.05); in the 36th week, the levels of alanine aminotransferase and aspartate transaminase were significantly lower in the treatment group than those in the control group (P<0.05). CONCLUSION: The protocol of SQG and YYJG combined with LAM to treat CHB showed superior efficacy than LAM monotherapy.
ABSTRACT
OBJECTIVE: To establish and evaluate a new method for measurement of dental plaque by using cellular neural network-based image segmentation. METHODS: A total of 195 subjects were selected from community population. After dental plaque staining, oral digital picture of anterior teeth area was taken by an Olympus digital camera (C-7070 Wide Zoom). At the same time, the Turesky dental plaque indices of anterior teeth were evaluated. The image analysis was conducted by cellular neural network-based image segmentation. RESULTS: The image cutting errors between two operators were very small. The Kappa value is 0.935. Pearson's correlation coefficient is 0.988 (P < 0.001). There was high correlative consistency between traditional dental plaque index and plaque percentage obtained by using image analysis. Pearson's correlation coefficient was 0.853 (P < 0.001). CONCLUSIONS: Cellular neural network-based image segmentation is a new method feasible for evaluating dental plaque.
