Colloid centrifugation reduces bacterial load in chilled dog semen.

Conventional semen extenders contain antibiotics to prevent bacterial growth. Finding alternatives would be beneficial to minimize the development of bacterial resistance mechanisms. The aim of this study was to determine the effect of Single Layer Centrifugation (SLC) with Canicoll of dog semen on microbial load and sperm quality during cooled storage. Twenty-four ejaculates were obtained from healthy dogs by digital manipulation. Samples were diluted in Tris-citrate-fructose extender without antibiotics and divided into two treatment groups: SLC-selected samples and unselected samples. Sperm motility (CASA), viability and acrosome integrity (PI/FITC-PNA) as well as bacterial load of each microorganism species (colony-forming units/mL) were assessed at 0 and 48 h of storage at 4 °C. Results indicate SLC-selected dog spermatozoa have greater percentages of motility, viability and acrosome integrity (P < 0.05). Bacterial growth in SLC sperm samples was less (P < 0.05) than unselected samples. Removal of individual bacterial species varied from 91 % to 98 % for Escherichia coli (91.62 %), Streptococcus spp. (98.18 %), Staphylococcus spp.(95.33 %) and Pseudomonas spp. (92.50 %). In conclusion, the use of SLC with Canicoll has the potential to decrease bacterial load in chilled dog semen.

Commentary on "Performance of a glucose meter with a built-in automated bolus calculator versus manual bolus calculation in insulin-using subjects".

Since the early 2000s, there has been an exponentially increasing development of new diabetes-applied technology, such as continuous glucose monitoring, bolus calculators, and "smart" pumps, with the expectation of partially overcoming clinical inertia and low patient compliance. However, its long-term efficacy in glucose control has not been unequivocally proven. In this issue of Journal of Diabetes Science and Technology, Sussman and colleagues evaluated a tool for the calculation of the prandial insulin dose. A total of 205 insulin-treated patients were asked to compute a bolus dose in two simulated conditions either manually or with the bolus calculator built into the FreeStyle InsuLinx meter, revealing the high frequency of wrong calculations when performed manually. Although the clinical impact of this study is limited, it highlights the potential implications of low diabetesrelated numeracy in poor glycemic control. Educational programs aiming to increase patients' empowerment and caregivers' knowledge are needed in order to get full benefit of the technology.

Evaluation of a novel continuous glucose monitoring-based method for mealtime insulin dosing--the iBolus--in subjects with type 1 diabetes using continuous subcutaneous insulin infusion therapy: a randomized controlled trial.

OBJECTIVE: Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy. SUBJECTS AND METHODS: An individual patient's model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t(0)) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG(0-5h)) and variability as the individual coefficient of variation (CV) of AUC-PG(0-5h). The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t(0) (PG(t0)), and insulin dose to AUC-PG(0-5h) and its CV was also investigated. RESULTS: AUC-PG(0-5h) was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5±127.5 vs. 689.2±180.7 mg/dL·h) or 100-g (752.1±237.7 vs. 760.0±263.2 mg/dL·h) CHO meals. A multiple regression analysis revealed a significant model only for the tBolus, with PG(t0) being the best predictor of AUC-PG(0-5h) explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7±15.3% vs. 10.1±12.5%) but independent of the factors studied. CONCLUSIONS: A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identified and analyzed for further optimization of postprandial glycemic control.

Combining basal-bolus insulin infusion for tight postprandial glucose control: an in silico evaluation in adults, children, and adolescents.

BACKGROUND: Achieving good postprandial glycemic control, without triggering hypoglycemia events, is a challenge of treatment strategies for type 1 diabetes subjects. Continuous subcutaneous insulin infusion, the gold standard of therapy, is based on heuristic adjustments of both basal and prandial insulin. Some tools, such as bolus calculators, are available to aid patients in selecting a meal-related insulin dose. However, they are still based on empiric parameters such as the insulin-to-carbohydrate ratio and on the physicians' and patients' ability to fit bolus mode to meal composition. METHODS: In this article, a nonheuristic method for assessment of prandial insulin administration is presented and evaluated. An algorithm based on set inversion via interval analysis is used to coordinate basal and bolus insulin infusions to deal with postprandial glucose excursions. The evaluation is carried out through an in silico study using the 30 virtual patients available in the educational version of the Food and Drug Administration-accepted University of Virginia simulator. Results obtained using the standard bolus strategy and different coordinated basal-bolus solutions provided by the algorithm are compared. RESULTS: Coordinated basal-bolus solutions improve postprandial glucose performance in most cases, mainly in terms of reducing hypoglycemia risk, but also increasing the percentage of time in normoglycemia. Moreover, glycemic variability is reduced considerably by using these innovative solutions. CONCLUSIONS: The algorithm presented here is a robust nonheuristic alternative to deal with postprandial glycemic control. It is shown as a powerful tool that could be integrated in future smart insulin pumps.