Risk factors for clinical or subclinical mastitis following infusion of internal teat sealant alone at the end of lactation in cows with low somatic cell counts

2021 ◽  
pp. 1-20
Author(s):  
S McDougall ◽  
J Williamson ◽  
K Gohary ◽  
J Lacy-Hulbert
Keyword(s):  
Risk Factors ◽  
Somatic Cell ◽  
Subclinical Mastitis ◽  
Somatic Cell Counts ◽  
Cell Counts

scholarly journals Prevalence of coagulase-negative staphylococci in bovine mastitis in Zimbabwe

2002 ◽  
Vol 73 (2) ◽  
Author(s):  
T. Kudinha ◽  
C. Simango
Keyword(s):  
Somatic Cell ◽  
Enteric Bacteria ◽  
Subclinical Mastitis ◽  
Clinical Mastitis ◽  
Bacillus Species ◽  
Small Scale ◽  
Coagulase Negative Staphylococci ◽  
Somatic Cell Counts ◽  
Milk Samples ◽  
Cell Counts

This study was carried out to determine the prevalence of coagulase-negative staphylococci in clinical and subclinical mastitis in commercial and small-scale farms in Zimbabwe. Thirty five quarter milk samples from clinical mastitis cases and 371 quarter milk samples from cows with subclinical mastitis were cultured for bacterial pathogens. The most frequent pathogens isolated in clinical mastitis were the enteric bacteria (31.4 %), followed by coagulase negative staphylococci (22.9 %) and then Staphylococcus aureus (17.1 %), whereas in subclinical mastitis S. aureus (34.2 %) and coagulase-negative staphylococci were (33.2 %) the most common. Bacillus species were only isolated in milk samples from subclinical mastitis. Coagulase-negative staphylococci were observed in mixed infections with other bacteria in only 2.2 % of the 406 milk samples from clinical and subclinical mastitis where they were isolated together with Bacillus species in 6 of the 9 mixed infection cases. About 95 % of the milk samples from which 131 coagulase-negative staphylococci were isolated had correspondingly high somatic cell counts. The coagulase-negative staphylococci isolated most frequently were S. chromogenes (7.9 %), S. epidermidis (7.4 %) and S. hominis (5.9 %). They were all associated with high somatic cell counts. All the coagulase-negative staphylococci isolates were susceptible to cloxacillin and erythromycin, and more than 90 %of the isolates were susceptible to neomycin, penicillin and streptomycin. The highest resistance was to tetracycline (17.6 %), followed by lincomycin (13.7 %). About 8 % of the isolates were resistant to both penicillin and streptomycin.

Intramammary infections and risk factors for clinical mastitis in herds with low somatic cell counts in bulk milk

1989 ◽  
Vol 125 (15) ◽  
pp. 393-396 ◽  
Author(s):  
Y. Schukken ◽  
D. Van de Geer ◽  
F. Grommers ◽  
J. Smit ◽  
A. Brand
Keyword(s):  
Risk Factors ◽  
Somatic Cell ◽  
Clinical Mastitis ◽  
Intramammary Infections ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
Bulk Milk

scholarly journals Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Nazira Mammadova ◽  
İsmail Keskin
Keyword(s):  
Support Vector Machine ◽  
Dairy Cattle ◽  
Somatic Cell ◽  
Control Period ◽  
Subclinical Mastitis ◽  
Clinical Mastitis ◽  
Support Vector ◽  
Cross Sectional ◽  
Somatic Cell Counts ◽  
Cell Counts

This study presented a potentially useful alternative approach to ascertain the presence of subclinical and clinical mastitis in dairy cows using support vector machine (SVM) techniques. The proposed method detected mastitis in a cross-sectional representative sample of Holstein dairy cattle milked using an automatic milking system. The study used such suspected indicators of mastitis as lactation rank, milk yield, electrical conductivity, average milking duration, and control season as input data. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the control period. Cattle were judged to be healthy or infected based on those somatic cell counts. This study undertook a detailed scrutiny of the SVM methodology, constructing and examining a model which showed 89% sensitivity, 92% specificity, and 50% error in mastitis detection.

Investigation of risk factors for clinical mastitis in British dairy herds with bulk milk somatic cell counts less than 150,000 cells/ml

2006 ◽  
Vol 158 (19) ◽  
pp. 649-653 ◽  
Author(s):  
K. M. O'Reilly ◽  
M. J. Green ◽  
E. J. Peeler ◽  
J. L. Fitzpatrick ◽  
L. E. Green
Keyword(s):  
Risk Factors ◽  
Somatic Cell ◽  
Clinical Mastitis ◽  
Dairy Herds ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
Bulk Milk

Presence of cathelicidin-1 in milk as an indicator of the severity of mammary infection in ewes

2020 ◽  
Vol 17 ◽  
Author(s):  
Angeliki I. Katsafadou ◽  
Natalia G.C. Vasileiou ◽  
George T. Tsangaris ◽  
Katerina S. Ioannidi ◽  
Athanasios K. Anagnostopoulos ◽  
...  
Keyword(s):  
Somatic Cell ◽  
Milk Yield ◽  
Inverse Correlation ◽  
Subclinical Mastitis ◽  
Clinical Mastitis ◽  
Somatic Cell Counts ◽  
Milk Samples ◽  
Cell Counts ◽  
The Mean ◽  
Important Disease

: Aims: The importance of cathelicidin-1 as an indicator of the severity of mammary infection in ewes. Background: Mastitis is an important disease of sheep, affecting their health and welfare. Objective: The association of the presence of cathelicidin-1 in milk samples from ewes with mastitis with the severity of the infection. Methods: Ewes were intramammarily inoculated with Mannheimia haemolytica or Staphylococcus chromogenes. Conventional (clinical, bacteriological and cytological examinations; milk yield measurements) and proteomics evaluation (2-DE, MALDI-TOF MS) to record cathelicidin-1 spot optical densities in milk samples were recorded. Results: Ewes challenged with M. haemolytica developed clinical and ewes challenged with S. chromogenes subclinical mastitis (P=0.05). The challenge organism was isolated from milk samples from inoculated mammary glands; increased somatic cell counts were also recorded. Cathelicidin-1 was detected in milk samples from the inoculated side of udders of all ewes. Mean spot density of cathelicidin-1 from samples from inoculated glands of ewes challenged with M. haemolytica was higher than from ewes challenged with S. chromogenes: 2896 ± 973 versus 1312 ± 361 (P =0.034). There were significant correlations between the presence of clinical mastitis / somatic cell counts with the spot density of cathelicidin-1 on 2-DE gels (P=0.043 and P=0.023, respectively). There was also a significant inverse correlation between the mean spot densities of cathelicidin-1 in milk samples and the milk yield of respective ewes on D10 (P =0.031). Conclusion: Potentially, cathelicidin-1 could be used as a marker to indicate the severity of damage to the mammary parenchyma.

Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle

2015 ◽  
Author(s):  
Nazira M. Mammadova ◽  
Ismail Keskin
Keyword(s):  
Somatic Cell ◽  
Fuzzy Inference ◽  
Sampling Period ◽  
Subclinical Mastitis ◽  
Error Rates ◽  
Ann Model ◽  
Inference System ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
The Difference

Mastitis is an important problem, while I guess AI is a possible solution to detect subclinical mastitis in Holstein cows milked with automatic milking systems. Mastitis alerts were generated via ANN and ANFIS model with the input data of lactation rank (current lactation number), milk yield, electrical conductivity, average milking duration and season. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the sampling period. Cattle were judged healthy or infected based on somatic cell counts. This study undertook a detailed scrutiny of ANN, and ANFIS AI methodology; constructed and examined models for each; and chose optimal methods based on that examination. The two mastitis detection models were evaluated as to sensitivity, specificity and error rate. The ANN model yielded 80% sensitivity, 91% specificity, and 64% error and the ANFIS, 55%, 91% and 35%. These results suggest the ANN model is better predictor of subclinical mastitis than ANN based on Z-test (the hypothesis control for the difference between rates). AI models such as these are useful tools in the development of mastitis detection models. Prediction error rates can be decreased through the use of more informative parameters.

Subclinical mastitis in problem herds in Scotland

1992 ◽  
Vol 1992 ◽  
pp. 118-118
Author(s):  
D N Logue ◽  
J Gunn ◽  
D Fenlon
Keyword(s):  
Somatic Cell ◽  
Cross Section ◽  
Subclinical Mastitis ◽  
Rapid Identification ◽  
Milk Products ◽  
Somatic Cell Counts ◽  
The Past ◽  
Cell Counts ◽  
Bulk Tank ◽  
The Impact

The recent introduction of penalties and premiums for bulk tank somatic cell counts (BTSCC) following the EC Directive of 5th August 1985 (no L 226/13) and subsequent EC Regulation of the 5th February 1990 (no COM(89)667) has reinforced the need for a greater awareness of the factors involved in herds with high BTSCC figures and their rapid identification. In the past a number of surveys have been conducted examining the major causes of mastitis in a cross section of the national herd (Wilson & Richards 1980) and these have demonstrated the importance of subclinical mastitis and in particular the so-called ‘cow-side organisms’, however more recently the impact of acute environmental mastitis has been of greater concern (Wilesmith Francis & Wilson 1986). Thus a reappraisal of priorities has become necessary, not least because these E C Regulations allow the sale of unpasteurised milk and milk products.

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