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Radix astragali is a herbal remedy used in China to treat patients with diabetes exposed to repeated episodes of hypoglycemia. The physiological basis or validity of this approach is not clear. In the present study, we examine the effect of pre-treatment with Radix astragali on hormonal counterregulatory responses to hypoglycemia in normal male Sprague-Dawley rats. Four groups of rodents were studied. In two of these groups, rodents were pre-treated for 3 days with either intravenous Radix astragali or control solution and, subsequently, while awake and unrestrained, underwent an in vivo hyperinsulinemic hypoglycemic (50 mg/dl) clamp study. The rodents in other two groups were pre-treated for 7 days with either intravenous Radix astragali or control solution. In addition, for the last 3-days of their treatment, the rats were subjected to a once-daily episode of insulin-induced hypoglycemia. Upon completion of this protocol, each rat underwent a controlled in vivo hyperinsulinemic hypoglycemic (50 mg/dl) clamp study. Radix astragali was shown to amplify the glucose counterregulatory response to hypoglycemia in both untreated and recurrently hypoglycemic rats. Immunocytochemistry studies suggested this might reflect increased neural activation in two key central glucose-sensing regions, the paraventricular hypothalamus and the nucleus tractus solitarius. Based on these rodent studies, we conclude that Radix astragali pre-treatment can amplify the counterregulatory response to hypoglycemia through a mechanism that may involve the central glucose-sensing regions. Future studies to examine the potential therapeutic benefit of Radix astragali in rodent models of type 1 diabetes are warranted.

Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection.