Somatic cell count assessment at the quarter or cow milking level

Mollenhorst, H.; Tol, P.P.J. van der; Hogeveen, H.

doi:10.3168/jds.2009-2842

Cited by 31 publications

(39 citation statements)

References 14 publications

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“…The cows were housed in free-stalls, fed a total mixed ration, and milked three times daily. Twelve healthy cows considered free of mastitis (somatic cell count less than 500 000 cells/ml) as measured by Fossomatic 5000 (Foss Electric, Hilerød, Denmark) on day 14 and 21 after calving and 12 cows with subclinical mastitis (somatic cell counts > 500 000 cells/ml) (Liu et al, 2010;Mollenhorst et al, 2010) were selected for proteomic analysis. Cows with any other diseases or disorders according to veterinarian records, such as retained placenta and metritis, were excluded from analysis in agreement with this record.…”

Section: Sample Collection and Preparationmentioning

confidence: 99%

Comparative proteomics analysis of plasma proteins during the transition period in dairy cows with or without subclinical mastitis after calving

Yang¹,

Wang²,

Bu³

et al. 2012

CZECH J ANIM SCI

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ABSTRACT:The transition period is the most critical time of the cow's lactation cycle that is associated with the onset of mastitis. In this study, changes of plasma proteins in cows (n = 12) with or without subclinical mastitis after calving were determined by two-dimensional electrophoresis (2-DE), which detected 18 spots with variations in protein spots abundance. These spots were identified by liquid chromatography coupled with tandem mass spectrometry. The changes in protein profile from day 21 before calving to day 1 after calving were similar in cows with or without subclinical mastitis. Abundance of α1 acid glycoprotein (AGP) and haptoglobin was dramatically increased at parturition, while transthyretin was down-regulated at parturition, and apolipoprotein E and immunoglobulin gamma 1 were up-regulated at postpartum compared with prepartum in periparturient dairy cows. In cows infected with subclinical mastitis, AGP, haptoglobin, and serum amyloid A were dramatically increased and continued to be elevated in plasma from day 1 to day 21 after calving compared with cows free of mastitis. Changes of protein in plasma at parturition may serve as an immune system response to parturition and lactation process at the protein level and suggest that these altered proteins would not serve as a potential marker for predicting if the periparturient dairy cows are susceptible to subclinical mastitis.

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Section: Sample Collection and Preparationmentioning

confidence: 99%

Comparative proteomics analysis of plasma proteins during the transition period in dairy cows with or without subclinical mastitis after calving

Yang¹,

Wang²,

Bu³

et al. 2012

CZECH J ANIM SCI

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“…This is a low number in comparison with the 573 CM cases used by Milne et al (2003) and the 3833 CM cases used by Steeneveld et al (2009), but it is a high number of CM cases in the field of CM detection using sensor data. Studies in the field of automated CM detection often use a lot less CM cases for analyses (e.g., 19 CM cases used by Mollenhorst et al, 2010, and 36 CM cases used by Song et al, 2010). It is likely that the inclusion of more CM cases with sensor information and bacteriological results will improve the robustness of decision trees predicting the Gram-status or the CM causal pathogens itself.…”

Section: Discussionmentioning

confidence: 99%

Sensor measurements revealed: Predicting the Gram-status of clinical mastitis causal pathogens

Kamphuis

Mollenhorst

Hogeveen

2011

Computers and Electronics in Agriculture

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a b s t r a c tAutomatic milking systems produce mastitis alert lists that report cows likely to have clinical mastitis (CM). A farmer has to check these listed cows to confirm a CM case and to start an antimicrobial treatment if necessary. In order to make a more informed decision, it would be beneficial to have information about the CM causal pathogen at the same time a cow is listed on the mastitis alert list. Therefore, this study explored whether decision-tree induction was able to predict the Gram-status of CM causal pathogens using in-line sensor measurements from automatic milking systems. Data were collected at nine Dutch dairy farms milking with automatic milking systems and included 140 bacteriological cultured CM cases with sensor measurements of electrical conductivity, colors red, green, and blue and milk yield for analyses. In total, 110 CM cases were classified as Gram-positive CM cases and 30 as Gram-negative. Stratified randomization was used to divide the data in a training set (n = 96) for model development, and a test set (n = 44) for validation. The decision tree used three variables to predict the Gram-status of the CM causal pathogen; two variables were based on electrical conductivity measurements, and one on measurements of the color blue. This decision tree had an accuracy of 90.6% and a kappa value of 0.76 based on data in the training set. When only those CM cases were considered with extreme high probability estimates for their Gram-status (either positive or negative), 74% of all records in the training set could be classified with a stratified accuracy of 97.1%. When validated, the decision tree performed poorly; accuracy dropped to 54.5% and the kappa value to À0.20. The stratified accuracy calculated for 75% of all records in the test set was 66.7%. Predicting the CM causal pathogen showed a similar poor result; the decision tree had an accuracy of 27.9% and a kappa of 0.12, based on data in the test set. Based on these results, it is concluded that decision-tree induction in conjunction with sensor information from the electrical conductivity, color, and milk yield provide insufficient discriminative power to predict the Gram-status or the CM causal pathogen itself.

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“…However, such comparisons are hampered by the fact that designs and analyses of diagnostic studies vary . Moreover, many of these studies used multiple tests in combination or employed algorithms to increase test performance (e.g., Chagunda et al, 2006;Mollenhorst et al, 2010), complicating a direct comparison with the test performance of PA measurement. Comparison to EC and SCC within the current study would have allowed for comparing the test performance of PA measurement with EC and SCC, but this could not be performed because the clinical milk samples had been frozen, which affects SCC test results (Barkema et al, 1997) and likely also EC.…”

Section: Short Communicationmentioning

confidence: 99%

Short communication: Protease activity measurement in milk as a diagnostic test for clinical mastitis in dairy cows

Koop

Werven

Roffel

et al. 2015

Journal of Dairy Science

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Due to the increasing use of automated milking systems, automated detection of clinical mastitis is becoming more important. Various in- or on-line diagnostic tests are in use, but generally suffer from false mastitis alerts. In this study, we explored a new diagnostic approach based on measurement of protease activity using fluorogenic protease substrates, which can be performed on site, at high speed, and at low costs. Samples from cows with clinical mastitis submitted for bacteriological culture at the University Farm Animal Practice were collected during several months and kept at -20°C until protease activity measurement. A reference set of milk samples from clinically healthy cows were collected on 9 different farms and were tested for protease activity directly and after freezing at -20°C to allow for comparison with the samples from clinical cases. The protease activity in mastitic milk samples was significantly higher than in samples from healthy animals. Based on 71 clinical mastitis samples and 180 milk samples from clinically healthy quarters, the area under the receiver-operating characteristic curve was estimated to be between 0.88 and 0.90, and at a threshold of 38 fluorescence per minute the test had a specificity of 0.99 at a sensitivity of 0.58. Protease activity measured in fresh milk from clinically healthy cows was significantly associated with somatic cell count and parity, but not with electrical conductivity, whereas protease activity in milk that had been frozen was statistically significantly associated with all 3 parameters. This study indicates that protease activity measurement as a stand-alone test can be used for detecting mastitis samples, using milk samples that have been frozen. Because protease activity acts in part on a different biological mechanism than somatic cell count or electrical conductivity, this test may increase the accuracy of mastitis diagnosis in combination with currently available in- or on-line tests in automated milking systems.

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Somatic cell count assessment at the quarter or cow milking level

Cited by 31 publications

References 14 publications

Comparative proteomics analysis of plasma proteins during the transition period in dairy cows with or without subclinical mastitis after calving

Comparative proteomics analysis of plasma proteins during the transition period in dairy cows with or without subclinical mastitis after calving

Sensor measurements revealed: Predicting the Gram-status of clinical mastitis causal pathogens

Short communication: Protease activity measurement in milk as a diagnostic test for clinical mastitis in dairy cows

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