Latitudinal but not elevational variation in blood glucose level is linked to life history across passerine birds

Macrophysiological research is vital to our understanding of mechanisms underpinning global life history variation and adaptation under diverse environments. Birds represent an important model taxon in this regard, yet our knowledge is limited to only a few physiological traits, mostly studied in temperate and Neotropical species. Here, we examined latitudinal and elevational variation in an emerging biomarker of physiological pace of life, blood glucose concentration, collected from 61 European temperate and 99 Afrotropical passerine species. Our data suggest that the slow physiological pace-of-life syndrome, indicated by lower baseline glucose level and stronger stress response, evolves convergently in lowland tropical birds across continents and is shaped by their low fecundity. In contrast, elevational variation in blood glucose levels implied a unique montane pace-of-life syndrome combining slow-paced life histories with fast-paced physiology. The observed patterns suggest an unequal importance of life history in shaping physiological adaptations associated with latitude and elevation.

Comparison of two glucose-monitoring systems for use in horses

OBJECTIVE To determine the accuracy of 2 interstitial glucose-monitoring systems (GMSs) for use in horses compared with a point-of-care (POC) glucometer and standard laboratory enzymatic chemistry method (CHEM). ANIMALS 8 clinically normal adult horses. PROCEDURES One of each GMS device (Dexcom G6 and Freestyle Libre 14-day) was placed on each horse, and blood glucose concentration was measured via POC and CHEM at 33 time points and compared with simultaneous GMS readings. An oral glucose absorption test (OGAT) was performed on day 2, and glucose concentrations were measured and compared. RESULTS Glucose concentrations were significantly correlated with one another between all devices on days 1 to 5. Acceptable agreement was observed between Dexcom G6 and Freestyle Libre 14-day when compared with CHEM on days 1, 3, 4, and 5 with a combined mean bias of 10.45 mg/dL and 1.53 mg/dL, respectively. During dextrose-induced hyperglycemia on day 2, mean bias values for Dexcom G6 (10.49 mg/dL) and FreeStyle Libre 14-day (0.34 mg/dL) showed good agreement with CHEM. CLINICAL RELEVANCE Serial blood glucose measurements are used to diagnose or monitor a variety of conditions in equine medicine; advances in near-continuous interstitial glucose monitoring allow for minimally invasive glucose assessment, thereby reducing stress and discomfort to patients. Data from this study support the use of the Dexcom G6 and Freestyle Libre 14-day interstitial glucose-monitoring systems to estimate blood glucose concentrations in horses.

Identification of biomarkers of chronic kidney disease among kidney-derived proteins

Background Chronic kidney disease (CKD) has few objective symptoms, and it is difficult to make an early diagnosis by using existing methods. Therefore, new biomarkers enabling diagnosis of renal dysfunction at an early stage need to be developed. Here, we searched for new biomarkers of CKD by focusing on kidney-derived proteins that could sensitively reflect that organ’s disease state. Methods To identify candidate marker proteins, we performed a proteomics analysis on renal influx and efflux blood collected from the same individual. Results Proteomics analysis revealed 662 proteins in influx blood and 809 in efflux. From these identified proteins, we selected complement C1q as a candidate; the plasma C1q level was significantly elevated in the renal efflux of donors. Moreover, the plasma concentration of C1q in a mouse model of diabetic nephropathy was significantly increased, in association with increases in blood glucose concentration and urinary protein content. Importantly, we demonstrated that the tendency of C1q to increase in the plasma of CKD patients was correlated with a decrease in their estimated glomerular filtration rate. Conclusion Overall, our results indicate that our approach of focusing on kidney-derived proteins is useful for identifying new CKD biomarkers and that C1q has potential as a biomarker of renal function.

Examining Type 1 Diabetes Mathematical Models Using Experimental Data

Type 1 diabetes requires treatment with insulin injections and monitoring glucose levels in affected individuals. We explored the utility of two mathematical models in predicting glucose concentration levels in type 1 diabetic mice and determined disease pathways. We adapted two mathematical models, one with β-cells and the other with no β-cell component to determine their capability in predicting glucose concentration and determine type 1 diabetes pathways using published glucose concentration data for four groups of experimental mice. The groups of mice were numbered Mice Group 1–4, depending on the diabetes severity of each group, with severity increasing from group 1–4. A Markov Chain Monte Carlo method based on a Bayesian framework was used to fit the model to determine the best model structure. Akaike information criteria (AIC) and Bayesian information criteria (BIC) approaches were used to assess the best model structure for type 1 diabetes. In fitting the model with no β-cells to glucose level data, we varied insulin absorption rate and insulin clearance rate. However, the model with β-cells required more parameters to match the data and we fitted the β-cell glucose tolerance factor, whole body insulin clearance rate, glucose production rate, and glucose clearance rate. Fitting the models to the blood glucose concentration level gave the least difference in AIC of 1.2, and a difference in BIC of 0.12 for Mice Group 4. The estimated AIC and BIC values were highest for Mice Group 1 than all other mice groups. The models gave substantial differences in AIC and BIC values for Mice Groups 1–3 ranging from 2.10 to 4.05. Our results suggest that the model without β-cells provides a more suitable structure for modelling type 1 diabetes and predicting blood glucose concentration for hypoglycaemic episodes.

Development of non-invasive blood glucose regression based on near-infrared spectroscopy combined with a deep learning method

Extracting micro-scale spectral features from dynamic blood glucose concentrations is extremely difficult when using non-invasive measurement methods. This work proposes a new machine-learning method based on near-infrared spectroscopy, deep belief network (DBN), and support vector machine (SVR), to improve the prediction accuracy. First, the standard oral glucose tolerance test is used to collect near-infrared spectroscopy and actual blood glucose concentration values for specific wavelengths (1200, 1300, 1350, 1450, 1600, 1610, and 1650 nm), and the blood glucose concentrations is within a clinical range of 70mg/dL~220mg/dL. Second, based on the DBN model, high-dimensional deep features of the non-invasive blood glucose spectrum are extracted. These are used to establish a support vector regression (SVR) model and to quantitatively analyze the influence of spectral sample size and corresponding feature dimensions (i.e., DBN network structure) on the prediction accuracy. Finally, based on data from six volunteers, a comparative analysis of the SVR prediction accuracy is performed both before and after using high-dimensional deep features. For volunteer 1, when the DBN-based high-dimensional deep features were used, the root mean square error (RMSE) of support vector regression (SVR) was reduced by 71.67%, the correlation coefficient (R2) and the P value of Clark grid analysis (P) were increased by 13.99% and 6.28%, respectively. Moreover, we have similar results when the proposed method was carried out on the data of other volunteers. The results show that the presented algorithm can play an important role in dynamic non-invasive blood glucose concentration prediction and can effectively improve the accuracy of the SVR model. Further, by applying the algorithm to six independent sets of data, this research also illustrates the high-precision regression and generalization capabilities of the DBN-SVR algorithm.

Effect of garlic and ginger extracts on the levels of glucose and Peptide-c in diabetic mice: تأثير مستخلصي الثوم والزنجبيل في مستوى سكر الدم والببتيد c عند الفئران المستحدث عندها داء السكري

This study aimed to investigate the effect of the alcoholic extract of garlic and ginger together on the levels of glucose, peptide -c and body weight in diabetic white mice. The study included 40 male white mice, Balb/c strain, which were divided into four experimental groups (10 mice in each group). The first group was a physiological control that was injected with physiological saline (0.9%) until the end of the experiment. As for the second group, diabetes was induced with a dose of 200 mg/kg of Alloxan hydrate weight of the mouse only, while the third group developed diabetes, and then it was treated with alcoholic extract of garlic and ginger together at a dose of 500 mg/kg of mouse weight for 10 days. While the fourth group developed diabetes and was treated with Glibenclamide. At the end of the experiment, the animals were anesthetized and blood was drawn from them by cardiocentesis. The results showed the effectiveness of garlic and ginger extracts in reducing blood glucose concentration by 35.75% and returning Peptide-c levels to their normal levels, equivalent to Glibenclamide (glyburide), which is known as an oral hypoglycemic agent.

Effects of Silymarin on Blood Glucose Concentration, Hepatic Histopathological Changes and FOXA2 and FOXA3 Gene Expression in Streptozotocin-Induced Diabetic Male Wistar Rats

Since the liver is among the primary organs susceptible to the effects of hyperglycaemia, diabetes mellitus (DM) could be a risk factor for the development and progression of liver damage. In present study, since no side-effects from the herbal medicine have been reported, the effect of silymarin on blood glucose concentration, hepatic histopathological changes and FOXA2 and FOXA3 gene expression, which are key genes in liver regeneration, was investigated. In this fundamental with experimental approach study, 40 male Wistar rats weighing 180-220 g were used. Rats were kept under the standard conditions of temperature of 20-22°C and humidity of 50% and consecutive 12-hour periods of light and darkness. Rats were randomly divided into five different groups (n=8 each), including healthy control rats, diabetic control rats, diabetic rats receiving silymarin (50, 100 and 150 mg/kg). Diabetes was induced by injecting streptozotocin (50 mg/kg B.W., i.p.). For 4 weeks silymarin groups received the drug once every three days through gavage and fasting blood glucose concentration measured once every 10 days. At the end of a month experiment, livers were harvested for hepatic histopathological and FOXA2 and FOXA3 gene expression changes analysis. In the diabetic rats treated with silymarin (50, 100 and 150 mg/kg), by comparison with the diabetic control group (p<0.05), glucose levels decreased significantly. Moreover, FOXA2 and FOXA3 expression in diabetic groups treated with silymarin significantly increased compared to diabetic control group (p<0.05). Hepatic histopathological changes were improved in the treated groups.The present study indicates that silymarin significantly decreased blood glucose concentration and increased the FOXA2 and FOXA3 gene products level. Hence, silymarin is able to improve some of the symptoms associated with diabetes and possesses hepatoprotective effects in streptozotocin-induced diabetic rats.

Effects of GYDENPHY caspules on streptozotocin-induced type 1 diabetic in Swiss mouse model

Objective: Evaluation the hypoglycemic effect of Gydenphy capsules on Streptozotocin-induced type 1 diabetic in Swiss mouse model. Methods: The type 1 diabetic model was established by intraperitoneal injections of Streptozocin 150mg/kg in Swiss mouse. Then, the Gydenphy were orally administered daily at a dose of 576 mg/kg/day or 1152 mg/kg/day in 10 days. Blood glucose concentration in the Gydenphy oral groups with that of water control group and the intraperitoneal insulin injection group was compared. Results: Blood glucose concentration in the groups using Gydenphy (dose576 mg/kg/24h and dose 1152 mg/kg/24h) significal decreased compared to the distilled water group at (p <0.05 at the time of 4 hours, 8 hours; p <0.01 at the time of 3, 10 days). The hypoglycemic effect of Gydenphy at 576mg/kg/day and 1152 mg/kg/day at 4 hours, 8 hours and 3 days were inferior to insulin 0.1 UI/kg/day for glycemic control. However, the hypoglycemic effect ofGydenphy were equivalent to insulin after 10 consecutive days on treatment. Conclusion: Gydenphy capsules have hypoglycemic effects onStreptozotocin-induced type 1 diabetes in Swiss mouse model.

Computational Modelling of Glucose Uptake by SGLT1 and Apical GLUT2 in the Enterocyte

It has been suggested that glucose absorption in the small intestine depends on both constitutively expressed SGLT1 and translocated GLUT2 in the brush border membrane, especially in the presence of high levels of luminal glucose. Here, we present a computational model of non-isotonic glucose uptake by small intestinal epithelial cells. The model incorporates apical uptake via SGLT1 and GLUT2, basolateral efflux into the blood via GLUT2, and cellular volume changes in response to non-isotonic conditions. The dependence of glucose absorption on luminal glucose, blood flow rate, and inlet blood glucose concentration is studied. Uptake via apical GLUT2 is found to be sensitive to all these factors. Under a range of conditions, the maximum apical GLUT2 flux is about half of the SGLT1 flux and is achieved at high luminal glucose (&gt; 50 mM), high blood flow rates, and low inlet blood concentrations. In contrast, SGLT1 flux is less sensitive to these factors. When luminal glucose concentration is less than 10 mM, apical GLUT2 serves as an efflux pathway for glucose to move from the blood to the lumen. The model results indicate that translocation of GLUT2 from the basolateral to the apical membrane increases glucose uptake into the cell; however, the reduction of efflux capacity results in a decrease in net absorption. Recruitment of GLUT2 from a cytosolic pool elicits a 10–20% increase in absorption for luminal glucose levels in the a 20–100 mM range. Increased SGLT1 activity also leads to a roughly 20% increase in absorption. A concomitant increase in blood supply results in a larger increase in absorption. Increases in apical glucose transporter activity help to minimise cell volume changes by reducing the osmotic gradient between the cell and the lumen.