[Experimental study on prevention and treatment of osteoporosis by phytoestrogen].

OBJECTIVE: To observe the effect of phytoestrogen (PE) in preventing and treating osteoporosis in ovariectomized rats. METHODS: Forty-five Wistar female rats, 3 months old, were randomly divided into the sham-operated group, the model group, low dose and high dose PE groups, and the positive control group, 9 in each group. All rats, except that those in the sham operated group were simutaneously operated, were ovariectomized. The medication started from the 31st days after operation by daily given saline per os to the sham-operated group and the model group, PE per os to the PE groups, and estradiol by intramuscular injection to the positive control group. The animals were sacrificed 90 days later, their sera were collected for determination of biochemical parameters, their right femoral bone was taken to detect the bone mineral density (BMD), left femoral bone to examine pathology of bone trabecula, and whole uterus was weighed to calculate the uterine index. RESULTS: PE could increase the BMD level, serum estradiol and inorganic phosphorus content, and uterine index, lower serum BGP, tartrate-resistant acid phosphatase (TRAP), alkaline phosphatuse (AKP) and interleukin-6 levels. Pathological examination showed that in the model group, the cortex of bone thinned, bone trabecula thinned also, with poor integrity, distorted, broken and decreased in size, while in the PE groups, the above-mentioned changes were significantly alleviated. CONCLUSION: PE is effective in preventing and treating osteoporosis in ovariectomized rats.

Prediction of prokaryotic promoters based on prediction of transcriptional units.

Identification of promoters is very important in understanding gene regulating relationships in an organism, and computational identification of promoters has been a long standing problem in computational biology. A new method was presented to predict promoter regions in prokaryotic organism. The method predicted transcription unit (TU) first and the TU was divided into singlet that contains only one single gene in a TU, and operon that contains more than one gene. Based on these predicted TUs, promoter was predicted for each TU using hidden Markov model including explicit state duration density. Both predicted TUs and promoters were satisfying.