scholarly journals Multiparametric analysis of blood parameters and hyperketonemia in cows

2021 ◽  
Vol 26 (52) ◽  
pp. 137-143
Author(s):  
Marko Cincović ◽  
Biljana Delić-Vujanović ◽  
Radojica Đoković ◽  
Branislava Belić ◽  
Bojan Blond ◽  
...  
Keyword(s):  
Total Bilirubin ◽  
Blood Parameters ◽  
Healthy Individuals ◽  
Metabolic Adaptations ◽  
Esterified Fatty Acids ◽  
Holstein Friesian ◽  
Beta Hydroxybutyrate ◽  
Large Clusters ◽  
Lower Expression ◽  
Friesian Cows

The aim of this study is to examine the interrelationships and importance of biochemical and endocrine blood parameters in the assessment of beta-hydroxybutyrate (BHB) values in healthy and ketotic cows using multiparameter statistics. The experiment included 45 Holstein Friesian cows (22 healthy and 23 with ketosis). The criterion used for detecting ketosis was the value of BHB ˃1.2 mmol/L. Based on laboratory indicators, cows were precisely classified into two large clusters: a cluster of healthy cows and a cluster of cows suffering from ketosis with minimal mixing of individual cows between clusters. Metabolic parameters were divided into two large clusters: parameters whose values increased in ketosis and decreased in healthy animals and parameters whose values decreased in ketosis and increased in healthy individuals. In ketotic cows there was a higher expression of non-esterified fatty acids, total bilirubin, aspartate aminotransferase, insulin and growth hormone, and a lower expression of glucose, albumin, triglycerides, cholesterol and total lipids compared to the healthy group. In 3 cows with ketosis, greater metabolic similarity with healthy cows was found, because of the absence of pronounced changes in the concentration of hormones and glucose. Thyroxine and triiodothyronine showed either increased or decreased expression in ketotic cows. In cows with lower values of these hormones, there were more pronounced metabolic changes characteristic of ketosis. For the development of metabolic adaptations to ketosis, in addition to hyperketonemia, there must be endocrine changes and changes in glycemia.

scholarly journals Predicting ketosis during the transition period in Holstein Friesian cows using hematological and serum biochemical parameters on the calving date

2021 ◽  
Author(s):  
Seungmin Ha ◽  
Seogjin Kang ◽  
Manhye Han ◽  
Jihwan Lee ◽  
Hakjae Chung ◽  
...  
Keyword(s):  
Biochemical Parameters ◽  
Total Bilirubin ◽  
Transition Period ◽  
Mean Corpuscular Hemoglobin ◽  
Serum Biochemical Parameters ◽  
Distribution Width ◽  
Esterified Fatty Acids ◽  
Holstein Friesian ◽  
Serum Biochemical ◽  
Friesian Cows

Abstract Ketosis often occurs during the transition period in dairy cows, which leads to economic and welfare problems. Ketosis was reported to be associated with hematological and serum biochemical parameters. However, the association between the parameters on the calving date and ketosis during the postpartum transition period remains unclear. This study aimed to investigate the association. Blood samples were collected from the jugular vein of Holstein cows on calving date and β-hydroxybutyrate was tested once every three days (8 times in 21 days). The cows were divided into three groups: non-ketosis, subclinical ketosis, and clinical ketosis. The clinical ketosis group significantly had the highest values of mean corpuscular volume, mean corpuscular hemoglobin, β-hydroxybutyrate, non-esterified fatty acids, and total bilirubin, but the lowest values of red cell distribution width, the counts of white blood cell, monocyte, and eosinophil, albumin, alanine transaminase, lactate dehydrogenase, and amylase. Non-ketosis group showed the opposite results (p < 0.05). The parameters are associated with the development and severity of ketosis. The findings suggest that these parameters on calving date may be useful indicators to identify dairy Holstein cow susceptible to ketosis during the transition period.

scholarly journals Relationships among some serum enzymes, negative energy balance parameters, parity and postparturient clinical (endo)metritis in Holstein Friesian cows – Short communication

2019 ◽  
Vol 67 (2) ◽  
pp. 241-245
Author(s):  
Baukje G. Andela ◽  
Frank J. C. M. Van Eerdenburg ◽  
Ali Choukeir ◽  
Dávid Buják ◽  
Zoltán Szelényi ◽  
...  
Keyword(s):  
Logistic Regression Model ◽  
Short Communication ◽  
Predictive Ability ◽  
Negative Energy ◽  
Serum Enzymes ◽  
Fetal Membranes ◽  
Esterified Fatty Acids ◽  
Holstein Friesian ◽  
Beta Hydroxybutyrate ◽  
Friesian Cows

Activities of alkaline phosphatase, aspartate aminotransferase and alanine aminotransferase, and concentrations of serum metabolites [beta-hydroxybutyrate (BHB) and non-esterified fatty acids (NEFA)] of primiparous (n = 83) and multiparous (n = 213) Holstein cows were studied as possible predictors of retained fetal membranes (RFM), grade 2 clinical metritis (CM) and clinical endometritis (CEM). A logistic regression model was used to calculate odds ratios (OR) for the prevalence of CM diagnosed between 0–5, 6–10 and 11–20 days in milk (DIM) and for the prevalence of CEM diagnosed between 22–28 and 42–49 DIM. The activities of the examined serum enzymes did not show significant associations either with CM or with CEM. For NEFA sampled on days 0 and 5, an OR of 2.38 for CM 0–20 DIM and an OR of 2.58 for CM 11–20 DIM was found. For BHB sampled on days 0 and 5, an OR of 8.20 for CEM 22–28 and 42–49 DIM and an OR of 1.98 for CM 6–10 DIM were found. The prevalence of RFM was higher in ≥ 4 parity cows compared to primiparous cows (46.3% vs. 26.5%). BHB and NEFA levels measured between 0 and 5 DIM could have a predictive ability for postpartum uterine disorders such as RFM, CM and CEM.

The effects of levels of concentrate offered and inclusion of maize gluten or fish meal in the concentrate on reproductive performance and blood parameters of dairy cows

1990 ◽  
Vol 50 (1) ◽  
pp. 1-10 ◽  
Author(s):  
J. D. Armstrong ◽  
E. A. Goodall ◽  
F. J. Gordon ◽  
D. A. Rice ◽  
W. J. McCaughey
Keyword(s):  
Reproductive Performance ◽  
Fish Meal ◽  
Blood Parameters ◽  
Winter Period ◽  
Plasma Urea ◽  
Grass Silage ◽  
Production Factors ◽  
Beta Hydroxybutyrate ◽  
Different Levels ◽  
Friesian Cows

ABSTRACTFive randomized-block experiments were carried out over 2 years using British Friesian cows managed as three separate herds. The effects of offering cows different levels of concentrates, ranging from 0·8 to 7·2 kg/day, in addition to grass silagead libitumduring the winter period on reproductive performance was investigated. The effects of substituting 4 kg/day maize gluten, or 0·8 kg/day fish meal, for part or all of the standard concentrate were also examined.Neither level of concentrates nor the inclusion of maize-gluten meal significantly affected reproductive performance even where milk production and quality was considerably influenced. The inclusion of fish meal improved conception rates to all services (0·64v.0·44;P< 0·05) and reduced the number of services required per conception (1·62v.2·31;P< 0·01).Plasma urea levels were raised consistently by the feeding of fish meal and, with the exception of weeks 2 and 6 of lactation, by the feeding of higher levels of concentrates. From the 6th week of lactation levels of beta-hydroxybutyrate (BHB) in the blood were significantly higher in the group of cows receiving the highest level of concentrates. The BHB level at week 6 was correlated with the number of services required per conception.A number of relationships between production factors and fertility are also presented.

Plasma progesterone, metabolic hormones and beta-hydroxybutyrate in Holstein-Friesian cows after superovulation

2011 ◽  
Vol 59 (4) ◽  
pp. 485-495 ◽  
Author(s):  
Balázs Bényei ◽  
István Komlósi ◽  
Anna Pécsi ◽  
Margit Kulcsár ◽  
László Huzsvai ◽  
...  
Keyword(s):  
Body Condition Score ◽  
Corpora Lutea ◽  
Optimum Level ◽  
Serum Concentrations ◽  
Metabolic Hormones ◽  
Lactation Stage ◽  
Holstein Friesian ◽  
Beta Hydroxybutyrate ◽  
Semi Arid ◽  
Friesian Cows

Metabolic hormones [insulin, leptin, insulin-like growth factor-I (IGF-I), thyroxine (T4) and triiodothyronine (T3)], progesterone (P4) and beta-hydroxybutyrate (BHB) serum concentrations were evaluated and their effect on the superovulation results of donor cows was investigated in a semi-arid environment. Body weight, body condition score (BCS) and lactation stage were also included in the analysis. Twenty-three Holstein-Friesian cows were superovulated with 600 IU FSHp following the routine procedure and flushed on day 7 in a Multiple Ovulation and Embryo Transfer Centre in the semi-arid area of Brazil. The corpora lutea (CL) were counted and blood samples were collected for assays. All of the hormones investigated and BHB serum concentrations were within the physiological ranges. There was a positive correlation between hormones, except between BHB and all the others. The leptin level was influenced by feeding status, as indicated by the BCS. Insulin, T4, T3 and BHB levels were affected by milking status. Dry cows had higher levels of all hormones except BHB. An optimum level of leptin resulted in the highest number of CL, while the linear increase of P4, T4 and IGF significantly increased the number of CL.

scholarly journals Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows

Animals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 271 ◽  
Author(s):  
Anna Benedet ◽  
Marco Franzoi ◽  
Carmen L. Manuelian ◽  
Mauro Penasa ◽  
Massimo De Marchi
Keyword(s):  
Infrared Spectra ◽  
Prediction Models ◽  
Early Summer ◽  
Blood Metabolites ◽  
Mid Infrared ◽  
Factors Affecting ◽  
Brown Swiss ◽  
Esterified Fatty Acids ◽  
Holstein Friesian ◽  
Friesian Cows

Serum metabolic profile is a common method to monitor health and nutritional status of dairy cows, but blood sampling and analysis are invasive, time-consuming, and expensive. Milk mid-infrared spectra have recently been used to develop prediction models for blood metabolites. The current study aimed to investigate factors affecting blood β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), and urea nitrogen (BUN) predicted from a large milk mid-infrared spectra database. Data consisted of the first test-day record of early-lactation cows in multi-breed herds. Holstein-Friesian cows had the greatest concentration of blood BHB and NEFA, followed by Simmental and Brown Swiss. The greatest and the lowest concentrations of BUN were detected for Brown Swiss and Holstein-Friesian, respectively. The greatest BHB concentration was observed in the first two weeks of lactation for Brown Swiss and Holstein-Friesian. Across the first month of lactation, NEFA decreased and BUN increased for all considered breeds. The greatest concentrations of blood BHB and NEFA were recorded in spring and early summer, whereas BUN peaked in December. Environmental effects identified in the present study can be included as adjusting factors in within-breed estimation of genetic parameters for major blood metabolites.

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

scholarly journals Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Body Parts ◽  
Nearest Neighbour ◽  
Data Set ◽  
3D Data ◽  
Holstein Friesian ◽  
Knn Classification ◽  
Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

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