RESUMO
The main objective of this experiment was to evaluate the effects of two milking intervals (8 and 16 h) on milk constituents (fat, protein, lactose, dry matter, and log10 SCC) of nineteen Istrian × Awassi × East-Friesian crossbred ewes in different milk fractions (0-25, 25-50, 50-75 and 75-100%) during the course of milking and in machine stripping (MS) milk. Furthermore, we sought to determine the effect of the two milking intervals on milking characteristics (average milk flow rate, peak milk flow rate, machine-on time, total milk yield, and milk production rate) and whether each milk constituent within each milking interval is best described by a linear, quadratic, or cubic function. Average milk flow rate and milk yield per milking decreased in the 8 h milking interval compared to the 16 h milking interval (P < 0.05). Peak milk flow rate, machine-on time, and milk production rate were not different between the two milking intervals. Overall, milk fat content, dry matter content, and log10 SCC increased in the 8 h milking interval compared to the 16 h milking interval (P < 0.05). Milk protein content did not change through the main milk fractions at either milking interval. Milk lactose content did not change through the milk fractions at the 8 h milking interval, whereas it decreased in the 75-100% and stripping milk fractions at the 16 h milking interval (P < 0.05). The 0-25% and stripping milk fractions contained the highest log10 SCC compared to all other milk fractions (P < 0.05). Changes of milk fat and dry matter content throughout milking were best described by quadratic functions, whereas milk protein content, milk lactose content, and log10 SCC were best described by different functions depending on the milking interval. These results demonstrate that milking interval influenced all milk constituents in various milk fractions during the course of ewe milking. Moreover, milking characteristics such as average milk flow and total milk yield, and the appropriate mathematical function to characterize milk constituents throughout a milking, were affected by milking interval.
RESUMO
Background and Objectives: Hyperinsulinemia and insulin resistance are not synonymous; if the risk of developing insulin resistance in adolescents is monitored, they do not necessarily have hyperinsulinemia. It is considered a condition of pre-diabetes and represents a condition of increased risk of developing DM (diabetes mellitus); it can exist for many years without people having the appropriate symptoms. This study aims to determine the risk of developing hyperinsulinemia at an early age in adolescents by examining which factors are crucial for its occurrence. Materials and Methods: The cross-sectional study lasting from 2019 to 2021 (2 years) was realized at the school children's department in the Valjevo Health Center, which included a total of 822 respondents (392 male and 430 female) children and adolescents aged 12 to 17. All respondents underwent a regular, systematic examination scheduled for school children. BMI is a criterion according to which respondents are divided into three groups. Results: After summary analyzes of OGTT test respondents and calculated values of HOMA-IR (homeostatic model assessment for insulin resistance), the study showed that a large percentage of respondents, a total of 12.7%, are at risk for hyperinsulinemia. The research described in this paper aimed to use the most popular AI (artificial intelligence) model, ANN (artificial neural network), to show that 13.1% of adolescents are at risk, i.e., the risk is higher by 0.4%, which was shown by statistical tests as a significant difference. Conclusions: It is estimated that a model using three different ANN architectures, based on Taguchi's orthogonal vector plans, gives more precise and accurate results with much less error. In addition to monitoring changes in each individual's risk, the risk assessment of the entire monitored group is updated without having to analyze all data.
