In vitro α-amylase inhibitory activity and in vivo hypoglycemic effect of methanol extract of Citrus macroptera Montr. fruit

Objective: To investigate the therapeutic effects of methanol extract of Citrus macroptera Montr. fruit in α-amylase inhibitory activity (in vitro) and hypoglycemic activity in normal and glucose induced hyperglycemic rats (in vivo). Methods: Fruits of Citrus macroptera without rind was extracted with pure methanol following cold extraction and tested for presence of phytochemical constituents, α-amylase inhibitory activity, and hypoglycemic effect in normal rats and glucose induced hyperglycemic rats.Results:showed that fruit extract had moderate α-amylase inhibitory activity [IC50 value=(3.638±0.190) mg/mL] as compared to acarbose. Moreover at 500 mg/kg and 1 000 mg/kg doses fruit extract significantly (P<0.05 and P<0.01 respectively) reduced fasting blood glucose level in normal rats as compared to glibenclamide (5 mg/kg). In oral glucose tolerance test, 500 mg/kg dose significantly reduced blood glucose level (P<0.05) at 2 h but 1000 mg/kg dose significantly reduced blood glucose level at 2 h and 3 h (P<0.05 and P<0.01 respectively) whereas glibenclamide (5 mg/kg) significantly reduced glucose level at every hour after administration. Overall time effect is also considered extremely significant with F value=23.83 and P value=0.0001 in oral glucose tolerance test.Conclusion:These findings suggest that the plant may be a potential source for the development Presence of saponin, steroid and terpenoid were identified in the extract. The results of new oral hypoglycemic agent.

Cluster detection diagnostics for small area health data: with reference to evaluation of local likelihood models.

The focus of this paper is the development of a range of cluster detection diagnostics that can be used to assess the degree to which a clustering method recovers the true clustering behaviour of small area data. The diagnostics proposed range from individual region specific diagnostics to neighbourhood diagnostics, and assume either individual region risk as focus, or concern areas of maps defined to be clustered and the recovery ability of methods. A simulation-based comparison is made between a small set of count data models: local likelihood, BYM and Lawson and Clark. It is found that local likelihood has good performance across a range of criteria when a CAR prior is assumed for the lasso parameter.