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Physiological imbalance is an abnormal physiological condition that cannot be directly observed but is assumed to precede subclinical and clinical diseases in the beginning of lactation. Alert systems to detect the physiological imbalance in a cow using Fourier transform mid-infrared spectroscopy in milk have been developed. The objective of this study was to estimate the value of information provided from such system with different indicator accuracies, herd prevalence and prices. A decision tree was created to model the probabilities of detection and associated costs of test outcome, intervention and occurrence of disease. We assumed that the negative effect of physiological imbalance was the development of subclinical ketosis and that this negative effect was prevented by drenching the cows with propylene glycol for 5 days. We simulated the economic impact of subclinical ketosis mediated through physiological imbalance to be $194 per case. The results showed that if the alert system was highly accurate (Se = 0.99/Sp = 0.99), and the prevalence of physiological imbalance was 30 %, the value of information provided from the system is $19 per cow-year. In case the prevalence is 5 % or 50 %, the value of information is $3 and $13, respectively. These estimates for the value do not cover the capital costs and operational costs of the alert system. This study furthermore clearly demonstrated that in order to estimate the value of information correctly, it is important to consider that drenching all cows and not drenching any of the cows are the two relevant alternative options in the absence of the alert system. In conclusion, the decision tree and sensitivity analysis developed in this study show that final economic results are highly variable to the prevalence of physiological imbalance and highest at an intermediate prevalence. Other relevant factors are the costs associated with drenching and the cost associated with treating false positives and not treating false negatives. In addition, this study highlights the benefits of simulation to pinpoint where additional information is needed to further quantify the economic value and required accuracy of an indication-based intervention system.

Objective: Numerous different interventions can be used to manage and control contagious mastitis within dairy herds. The effect of such interventions is often estimated by assessing the change in incidence of clinical cases or somatic cell counts, but this is prone to inclusion bias and lacks specificity for the pathogen. A more direct approach is to estimate the transmission rates before and after intervention using a Hidden Markov Model (HMM). We assessed the effect of two different hygiene procedures on the transmission of contagious mastitis pathogens in two herds using automated milking systems each with an intervention group and a control group. Materials and methods: A total of 611 cows were sampled between 1 and 8 times over a 4 month period, yielding a total of more than 2,300 cow-level qPCR observations on milk. A discrete time HMM model was implemented within a Bayesian framework to estimate transmission parameters based on qPCR observations of imperfectly detected infection status for Staph. aureus and Strep. agalactia. This model assumes that the true state of the individual at a given time point is latent but can be observed indirectly via observations with imperfect sensitivity and specificity. The cyclic threshold (ct) value chosen for Strep. agalactia and Staph. aureus were Materials and methods: We used a mechanistic, dynamic, and stochastic dairy herd model that simulates the production and state changes of dairy cows (SimHerd) to estimate the negative effects associated with the diseases that were mediated through Physiological Imbalance and developed an effect chart to quantify the cost and benefits associated with the outcome of the FT-MIR results and based on those a deterministic cost-benefit analysis. We assumed that the negative effects of Physiological Imbalance were prevented by supplementing the cows with propylene glycol for 5 days. We simulated the negative effects of disease and production mediated through Physiological Imbalance to be € 121 per case.Results: The results showed that if the FT-MIR based indicator was accurate (Se=0.9/Sp=0.95) and the prevalence of physiological imbalance was 50% a total of € 42/cow/yr could be expected to be gained for paying for the indicator-based system.Conclusion: In conclusion the results shows that final economic results to be highly variable to the prevalence of Physiological Imbalance, the costs associated with treatment and the cost associated with treating false positives and not treating false negatives. In addition, this study highlights the benefits of simulation to pinpoint where additional information is needed to further quantify the economic value and required accuracy of an indicator-based intervention system.

Objective: Physiological Imbalance is a pre-clinical disease state in dairy cows that cannot be directly observed but is assumed to precede subclinical and clinical diseases in the beginning of lactation in dairy cows. It is possible to detect the state of physiological imbalance using Fourier transform mid-infrared spectroscopy (FT-MIR) in milk but there are issues related to the accuracy of this prediction. The objective of this study was to estimate the costs and benefits associated with an indicator-based intervention against Physiological Imbalance with different indicator accuracies, herd prevalences and prices.Materials and methods: We used a mechanistic, dynamic, and stochastic dairy herd model that simulates the production and state changes of dairy cows (SimHerd) to estimate the negative effects associated with the diseases that were mediated through Physiological Imbalance and developed an effect chart to quantify the cost and benefits associated with the outcome of the FT-MIR results and based on those a deterministic cost-benefit analysis. We assumed that the negative effects of Physiological Imbalance were prevented by supplementing the cows with propylene glycol for 5 days. We simulated the negative effects of disease and production mediated through Physiological Imbalance to be € 121 per case.Results: The results showed that if the FT-MIR based indicator was accurate (Se=0.9/Sp=0.95) and the prevalence of physiological imbalance was 50% a total of € 42/cow/yr could be expected to be gained for paying for the indicator-based system.Conclusion: In conclusion the results shows that final economic results to be highly variable to the prevalence of Physiological Imbalance, the costs associated with treatment and the cost associated with treating false positives and not treating false negatives. In addition, this study highlights the benefits of simulation to pinpoint where additional information is needed to further quantify the economic value and required accuracy of an indicator-based intervention system.

The aim of this study was to describe metabolism of early-lactation dairy cows by clustering cows based on glucose, insulin-like growth factor I (IGF-I), free fatty acid, and beta-hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using 3 sets of milk biomarkers (milk metabolites and enzymes, glycans on the immuno-gamma globulin fraction of milk, and Fourier-transform mid-infrared spectra of milk). Metabolic clusters are used to identify dairy cows with a balanced or imbalanced metabolic profile. Around 14 and 35 d in milk, serum or plasma concentrations of BHB, free fatty acids, glucose, and IGF-I were determined. Cows with a favorable metabolic profile were grouped together in what was referred to as the "balanced" group (n = 43) and were compared with cows in what was referred to as the "other balanced" group (n = 64). Cows with an unfavorable metabolic profile were grouped in what was referred to as the "imbalanced" group (n = 19) and compared with cows in what was referred to as the "other imbalanced" group (n = 88). Glucose and IGF-I were higher in balanced compared with other balanced cows. Free fatty acids and BHB were lower in balanced compared with other balanced cows. Glucose and IGF-I were lower in imbalanced compared with other imbalanced cows. Free fatty acids arid BHB were higher in imbalanced cows. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat- and protein-corrected milk yield in balanced cows, whereas that of imbalanced cows was higher. Dry matter intake and the daily increase in dry matter intake were higher in balanced cows and lower in imbalanced cows. Energy balance was continuously higher in balanced cows and lower in imbalanced cows. Weekly or twice-weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6-phosphate, BHB, lactate dehydrogenase, N-acetyl-beta-D-glucosaminidase, isocitrate), immunogamma globulin glycans (19 peaks), and Fourier-transform mid-infrared spectra (1,060 wavelengths reduced to 15 principal components) were determined. Milk biomarkers with or without additional cow information (days in milk, parity, milk yield featurs) were used to create predictive models for the metabolic clusters. Accuracy for prediction of balanced (80%) and imbalanced (88%) cows was highest using milk metabolites and enzymes combined with days in milk and parity. The results and models of the present study are part of the GplusE project and identify novel milk-based phenotypes that may be used as predictors for metabolic and performance traits in early-lactation dairy cows.

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

The new modernization world have been faced various kinds of problems especially business men Facing large level of problems through their work environment, around the world each and every day changes new technologies, new trend of life, new information etc. these factors creates heavy competitions among the business men, especially affects the new entrepreneurs, the new entrepreneurs having lots of dreams and expectations for their business field, however to day business field creates high level of depression and stress , due to various unanticipated problems issues and challenges. Dr. V. Saravanan "A Study on Impact of Work Stress on Entrepreneurs - "With Special Reference to Cottage Industries"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: https://www.ijtsrd.com/papers/ijtsrd12786.pdf

Physiological imbalance (PI) is a situation in which physiological parameters deviate from the normal and cows consequently have an increased risk of developing production diseases and reduced production or reproduction. The objectives of this work were (1) to generate an index for PI based on several plasma metabolites and (2) to compare the use of this index with calculated energy balance (EBAL) and individual plasma metabolites in relation to risk of disease during early lactation. We used a total of 634 lactations from 317 cows consisting of 3 breeds ranging from a parity of 1 to 4. Weekly blood samples were analyzed for selected metabolites; that is, urea nitrogen, albumin, cholesterol, nonesterified fatty acids (NEFA), glucose, and β-hydroxybutyrate (BHBA). Energy intake and EBAL were calculated; veterinary treatment records and daily composite milk somatic cell counts were used to determine incidence of disease. Data were adjusted for numerous fixed effects (e.g., parity, breed, and week around calving) before further statistical analysis. The time of disease (TOD) was recorded as the day in which the signs of disease were observed (TOD = 0). The week before and after TOD was ± n wk relative to TOD = 0. Each week, all plasma metabolites were individually adjusted to an overall mean (=0) and variance (=1). The normalized variables were included in regression analyses by week of lactation to identify metabolites that explain the variation in calculated EBAL, as a reflection of degree of PI. Nonesterified fatty acids, BHBA, and glucose were weighted within each week based on regression coefficients (i.e., x1–x3 below) generated from a model to predict EBAL. Data from wk −1 relative to TOD were analyzed using a mixed linear model to relate degree of PI and metabolites in blood to risk of disease. The weekly PI index was defined as PI = (x1 × [NEFA]) + x2 × [BHBA] − x3 × [glucose])/3. For diseases that developed ≥2 wk after calving, no variables were associated with risk of disease. Prepartal PI and plasma NEFA were better predictors of disease (i.e., metritis, retained placenta, and milk fever) at wk 1 than EBAL and plasma BHBA and glucose. Examining the relationship between PI and milk constituents is needed for the development of an automated in-line and real-time surveillance system for early detection of risk animals on-farm.

Identification of biomarkers for degree of physiological imbalance (PI), a situation in which physiological parameters deviate from normal, is needed to reduce disease risk and improve production and reproduction performance of cows. The objective was to describe the liver proteome in early and mid lactation for cows with different degrees of PI with a special focus on biomarkers and pathways involved in periparturient disease complexes. Twenty-nine cows in early [49 ± 22 d in milk (DIM); n = 14] and mid (159 ± 39 DIM; n = 15) lactation were nutrient restricted for 4 d to increase PI by supplementing the ration with 60% wheat straw. Liver biopsies were collected −1 and 3 d relative to restriction. Before restriction, an index for PI was calculated based on plasma nonesterified fatty acids, β-hydroxybutyrate, and glucose concentrations. Within E and M cows, a subsets of 6 cow was classified as having either the greatest (PI) or least (normal; N) degree of PI and were used for isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative profiling in liver using liquid chromatography-tandem mass spectrometry. We identified pyruvate carboxylase and isocitrate dehydrogenase as potential hepatic biomarkers for PI for cows during early lactation and alcohol dehydrogenase-4 and methylmalonate-semialdehyde dehydrogenase for cows in mid lactation. This preliminary study identified new biomarkers in liver for PI and provided a better understanding of the differences in coping strategies used for cows in PI. Despite the small sample size (n = 3/group), the results lay a foundation for future research focused on the usefulness of the hepatic biomarkers for predicting PI and thereby cows at risk for disease during lactation.

The large increase in milk yield and the structural changes in the dairy industry have caused major changes in the housing, feeding and management of the dairy cow. However, while large improvements have occurred in production and efficiency, the disease incidence, based on veterinary records, does not seem to be improved. Earlier reviews have covered critical periods such as the transition period in the cow and its influence on health and immune function, the interplay between the endocrine system and the immune system and nutrition and immune function. Knowledge on these topics is crucial for our understanding of disease risk and our effort to develop health and welfare improving strategies, including proactive management for preventing diseases and reducing the severity of diseases. To build onto this the main purpose of this review will therefore be on the effect of physiological imbalance (PI) on immune function, and to give perspectives for prevention of diseases in the dairy cow through nutrition. To a large extent, the health problems during the periparturient period relate to cows having difficulty in adapting to the nutrient needs for lactation. This may result in PI, a situation where the regulatory mechanisms are insufficient for the animals to function optimally leading to a high risk of a complex of digestive, metabolic and infectious problems. The risk of infectious diseases will be increased if the immune competence is reduced. Nutrition plays a pivotal role in the immune response and the effect of nutrition may be directly through nutrients or indirectly by metabolites, for example, in situations with PI. This review discusses the complex relationships between metabolic status and immune function and how these complex interactions increase the risk of disease during early lactation. A special focus will be placed on the major energetic fuels currently known to be used by immune cells (i.e. glucose, non-esterified fatty acids, beta-hydroxybutyrate and glutamine) and how certain metabolic states, such as degree of negative energy balance and risk of PI, contribute to immunosuppression during the periparturient period. Finally, we will address some issues on disease prevention through nutrition.

Physiological imbalance (PI) is a situation in which physiological parameters deviate from the normal, and cows consequently have an increased risk of developing production diseases and reduced production or reproduction. Our objectives were to (1) determine the effect of stage of lactation and milk yield on metabolic and production responses of cows during a nutrient restriction period to experimentally increase PI; (2) identify major metabolites that relate to degree of PI; and (3) identify potential biomarkers in milk for on-farm detection of PI throughout lactation. Forty-seven Holstein cows in early [n=14; 49±22 d in milk (DIM); parity=1.6±0.5], mid (n=15; 159±39 DIM; parity=1.5±0.5), and late (n=18; 273±3 DIM; parity=1.3±0.5) lactation were used. Prior to restriction, all cows were fed the same total mixed ration ad libitum. All cows were then nutrient restricted for 4 d by supplementing the ration with 60% wheat straw to induce PI. After restriction, cows returned to full feed. Daily milk yield was recorded and composite milk samples were analyzed for fat, protein, lactose, citrate, somatic cells, uric acid, alkaline phosphatase, β-hydroxybutyrate (BHBA), and milk urea nitrogen. Blood was collected daily and analyzed for metabolites: nonesterified fatty acids (NEFA), BHBA, glucose, plasma urea nitrogen, and insulin. The revised quantitative insulin sensitivity check index (RQUICKI) was calculated for each cow. Liver biopsies collected before and during restriction were analyzed for triglycerides, glycogen, phospholipids, glucose, and total lipid content. A generalized linear mixed model was used to determine the effect of stage of lactation on responses during restriction. Regression analyses were used to examine the effect of pre-restriction levels on changes during restriction. Similar decreases in milk yield among groups indicate that the capacity of individual responses is dependent on milk yield but the coping strategies used are dependent on stage of lactation. Milk yield was a better predictor of feed intake than DIM. Plasma glucose decreased for all cows, and cows in early lactation had increased plasma BHBA, whereas cows in later lactation had increased NEFA during restriction. Milk citrate had the greatest increase (58%) during restriction for all cows. Results reported here identified metabolites (i.e., glucose, NEFA, BHBA, cholesterol) as predictors of PI and identified milk citrate as a promising biomarker for PI on farm. Physiological imbalance (PI) is a situation in which physiological parameters deviate from the normal, and cows consequently have an increased risk of developing production diseases and reduced production or reproduction. Our objectives were to (1) determine the effect of stage of lactation and milk yield on metabolic and production responses of cows during a nutrient restriction period to experimentally increase PI; (2) identify major metabolites that relate to degree of PI; and (3) identify potential biomarkers in milk for on-farm detection of PI throughout lactation. Forty-seven Holstein cows in early [n=14; 49±22 d in milk (DIM); parity=1.6±0.5], mid (n=15; 159±39 DIM; parity=1.5±0.5), and late (n=18; 273±3 DIM; parity=1.3±0.5) lactation were used. Prior to restriction, all cows were fed the same total mixed ration ad libitum. All cows were then nutrient restricted for 4 d by supplementing the ration with 60% wheat straw to induce PI. After restriction, cows returned to full feed. Daily milk yield was recorded and composite milk samples were analyzed for fat, protein, lactose, citrate, somatic cells, uric acid, alkaline phosphatase, β-hydroxybutyrate (BHBA), and milk urea nitrogen. Blood was collected daily and analyzed for metabolites: nonesterified fatty acids (NEFA), BHBA, glucose, plasma urea nitrogen, and insulin. The revised quantitative insulin sensitivity check index (RQUICKI) was calculated for each cow. Liver biopsies collected before and during restriction were analyzed for triglycerides, glycogen, phospholipids, glucose, and total lipid content. A generalized linear mixed model was used to determine the effect of stage of lactation on responses during restriction. Regression analyses were used to examine the effect of pre-restriction levels on changes during restriction. Similar decreases in milk yield among groups indicate that the capacity of individual responses is dependent on milk yield but the coping strategies used are dependent on stage of lactation. Milk yield was a better predictor of feed intake than DIM. Plasma glucose decreased for all cows, and cows in early lactation had increased plasma BHBA, whereas cows in later lactation had increased NEFA during restriction. Milk citrate had the greatest increase (58%) during restriction for all cows. Results reported here identified metabolites (i.e., glucose, NEFA, BHBA, cholesterol) as predictors of PI and identified milk citrate as a promising biomarker for PI on farm.

Udgivelsesdato: September Thirty Danish Holstein cows were used to determine how cows in early lactation adapt to changes in protein to starch supply in order to manipulate metabolism to combat physiological imbalance. During weeks 4 through 6 of lactation, 10 cows were fed either a high protein to starch ratio (high) diet, 10 cows were fed a low protein to starch ratio (low) diet while 10 others continued on the control diet. During weeks 7 through 9 of lactation, all cows returned to the control diet. The diets were formulated to consist of 15.0%, 18.6% and 22.2% crude protein and 13.3%, 7.5% and 1.7% of starch for the low, control and high diets, respectively. Besides milk urea nitrogen, no other production or metabolic parameters were affected by treatment. In conclusion, manipulation of dietary protein to starch is not a potential strategy to combat physiological imbalance during early lactation