May 2022 DOI 10.14302/issn.2379-7835.ijn-22-4170
Garden egg nutritional values are numerous. However, few nutritional and epidemiological data exist on the impacts of garden egg consumption on diabetes control. In this experimentally-control designed nutritional study, the effects of scarlet garden egg species (Solanum aethiopicum L.) on lipoglycemic profile, weight control and, pancreas histoarchitecture in diabetic male Wistar rats were examined. Twenty-One adult male Wistar rats inducted with diabetes were randomly categorized into three groups (n = 7, each): Diabetic control (DC); Diabetic treated with aqueous extract of garden egg (DEE) and Diabetic fed with garden egg-supplemented diet (DSE). Animals were fed for six weeks according to the experimental design. Glycemic status and body weights were assessed twice weekly while lipid analysis was conducted at the entry and 6th week of the study. Oral glucose tolerance (OGT) test was conducted. Gross analysis and tissue histology of the pancreas were assessed by Hematoxylin and Eosin (H&E) staining technique. Statistical analysis was done using analysis of variance, and the results were expressed as mean ± S.E.M. at P < 0.05. Garden egg reduced mean body weight gain (DSE: 14.53%; DEE: 10.58%; P value = 0.04) and decreased blood glucose concentrations (DEE: 37.33%; DSE: 18.68%; P = 0.03) with corresponding improved lipid profile, glycemic tolerance and control (DEE > DSE) and, preserved pancreas histoarchitecture in diabetic Wistar rats. Solanum aethiopicum (garden egg) consumption (as fresh fruit or supplemented diet) preserves pancreatic tissue histoarchitecture and improves lipoglycemic profile and weight control in diabetic Wistar rats.
May 2014 DOI 10.14302/issn.2374-9431.jbd-13-283
Continuous-time glucose monitoring (CGM) effectively improves glucose control, as oppose to infrequent glucose measurements (i.e. using Lancet Meters), by providing frequent blood glucose concentration (BGC) to better associate this variation with changes in behavior. Currently, the most widely used CGM devices rely on a sensor that is inserted invasively under the skin. Because of the invasive nature and also the replacement cost of sensors, the primary users of current CGM devices are insulin dependent people (type 1 and some type 2 diabetics). Most non-insulin dependent diabetics use only lancet glucose measurements. The ultimate goal of this research is the development of CGM technology that overcomes these limitations (i.e. invasive sensors and their cost) in an effort to increase CGM applications among non-insulin dependent people. To meet this objective, this preliminary work has developed a methodology to mathematically infer BGC from measurements of non-invasive input variables which can be thought of as a “virtual” or “soft” sensor approach. In this work virtual sensors are developed and evaluated on 20 subjects using four BGC measurements per day and eight input variables representing meals, activity, stress, and clock time. Up to four weeks of data are collected for each subject. One evaluation consists of 3 days of training and up to 25 days of testing data. The second one consists of one week of training, one week of validation, and 2 weeks of testing data. The third one consists two weeks of training, one week of validation and one week of testing data. Model acceptability is determined on an individual basis based on the fitted correlation to CGM testing data. For 3 day, 1 week, and 2 weeks training studies, 35%, 55% and 65% of the subjects, respectively, met the Acceptability Criteria that we established based on the concept of usefulness.