Blood pressure lowering effects of Lactobacillus helveticus fermented milk containing bioactive peptides Ile-Pro-Pro and Val-Pro-Pro; mechanistic, kinetic and clinical studies

The purpose of the present study was to evaluate the effects of Lactobacillus helveticus fermented milk (peptide milk) containing the casein-derived tripeptides Isoleucyl-prolyl-proline (Ile-Pro-Pro) and Valyl-prolyl-proline (Val-Pro-Pro) on blood pressure and vascular function in hypertensive subjects. The peptide milk lowered systolic and diastolic blood pressure in long-term use in hypertensive subjects when blood pressure was measured by using 24-hour ambulatory blood pressure measurement (ABPM). The blood pressure lowering effect was seen with the dose of 50 mg of tripeptides, and a tendency for lowering blood pressure was also observed when the dose was 5 mg. No adverse effects compared to the placebo group were reported or detected in laboratory analysis. The effect of the peptide milk on arterial stiffness was shown using two different methods, the ambulatory arterial stiffness index (AASI) and pulse wave analysis (PWA). According to the AASI, arterial stiffness was significantly reduced in the peptide milk group compared to the baseline level, but the difference was not significant compared to the placebo group. PWA showed that the peptide milk reduced arterial stiffness significantly compared to the placebo group. Endothelium-independent relaxation (nitroglycerin) and endothelium-dependent relaxation (salbutamol) did not differ between the groups. The blood pressure lowering mechanisms of the tripeptides and the kinetics of Ile-Pro-Pro were investigated using spontaneously hypertensive rats (SHR) and Sprague-Dawley rats. Previous studies have suggested that the blood pressure lowering effect of the tripeptides Ile-Pro-Pro and Val-Pro-Pro is based on angiotensin-converting enzyme inhibition, but the present findings did not agree with these previous studies. It was shown in SHR that calcium, potassium and magnesium may also have an important role in attenuating the development of hypertension as part of the peptide milk effect. In addition, the present study suggests indirectly that improved endothelial nitric oxide release capacity is not the mechanism by which peptide milk mediates its favourable circulatory effects. The kinetics of Ile-Pro-Pro were studied using adult Sprague-Dawley rats. The results showed that orally administered Ile-Pro-Pro is absorbed at least partly intact from the gastrointestinal tract. Radiolabelled Ile-Pro-Pro was distributed in different tissues and considerable radioactivity levels were found in tissues related to the renin-angiotensin system (RAS), adrenals, aorta and kidneys. Ile-Pro-Pro does not bind to plasma proteins, and therefore it is possible that its blood pressure lowering effect is mediated by free Ile-Pro-Pro. In conclusion, consumption of the peptide milk lowers blood pressure and reduces arterial stiffness in hypertensive subjects. Ile-Pro-Pro can be absorbed partly intact from the gastrointestinal tract and might accumulate in tissues related to the RAS. The precise blood pressure lowering mechanism of peptide milk remains to be studied.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Characterisation of antibiotic-resistant psychrotrophic bacteria in raw milk

Dynamics of raw milk associated bacteria during cold storage of raw milk and their antibiotic resistance was reviewed, with focus on psychrotrophic bacteria. This study aimed to investigate the significance of cold storage of raw milk on antibiotic-resistant bacterial population and analyse the antibiotic resistance of the Gram-negative antibiotic-resistant psychrotrophic bacteria isolated from the cold-stored raw milk samples. Twenty-four raw milk samples, six at a time, were obtained from lorries that collected milk from Finnish farms and were stored at 4°C/4 d, 6°C/3 d and 6°C/4 d. Antibiotics representing four classes of antibiotics (gentamicin, ceftazidime, levofloxacin and trimethoprim-sulfamethoxazole) were used to determine the antibiotic resistance of mesophilic and psychrotrophic bacteria during the storage period. A representative number of antibiotic-resistant Gram-negative isolates retrieved from the cold-stored raw milk samples were identified by the phenotypic API 20 NE system and a few isolates by the 16S rDNA gene sequencing. Some of the isolates were further evaluated for their antibiotic resistance by the ATB PSE 5 and HiComb system. The initial average mesophilic counts were found below 105 CFU/mL, suggesting that the raw milk samples were of good quality. However, the mesophilic and psychrotrophic population increased when stored at 4°C/4 d, 6°C/3 d and 6°C/4 d. Gentamicin- and levofloxacin-resistant bacteria increased moderately (P < 0.05) while there was a considerable rise (P < 0.05) of ceftazidime- and trimethoprim-sulfamethoxazole-resistant population during the cold storage. Of the 50.9 % (28) of resistant isolates (total 55) identified by API 20 NE, the majority were Sphingomonas paucimobilis (8), Pseudomonas putida (5), Sphingobacterium spiritivorum (3) and Acinetobacter baumanii (2). The analysis by ATB PSE 5 system suggested that 57.1% of the isolates (total 49) were multiresistant. This study showed that the dairy environment harbours multidrug-resistant Gramnegative psychrotrophic bacteria and the cold chain of raw milk storage amplifies the antibioticresistant psychrotrophic bacterial population.