Sheep mastitis caused by staphylococci and streptococci and their influence on oxidative status
Bibliographic record
Abstract
Article Details: Received: 2020-10-14 | Accepted: 2020-11-27 | Available online: 2021-01-31 https://doi.org/10.15414/afz.2021.24.mi-prap.53-57 Abstract The objectives of this study were to determine the relationship of oxidative product levels, using malondialdehyde (MDA) as a marker on occurrence of mastitis and its causing pathogens in two dairy flocks of ewes situated in east and north of Slovakia. The diagnosis of mastitis was performed on the basis of clinical examination of the udder, macroscopic evaluation of milk, with the evaluation of Californian mastitis test (CMT) and bacteriological analysis of individual raw milk samples. From total 537 and 444 halves ewe’s milk samples were 16.6% and 23.2% positive to CMT, respectively. The prevalence of mastitis caused by bacterial pathogenes in the monitored herds was 14.3% to 19.1%, respectively. In all monitored sheep flocks were confirmed predominantly subclinical forms (SM) of intramammary infection (IMI). The highest of etiological agents in all monitored herds had coagulase negative staphylococci and coagulase positive staphylococci especially Staphylococcus aureus . Except for staphylococci were Streptococcus uberis and Streptococcus spp. most frequently pathogens isolated from mastitic sheep. The highest MDA level was observed from clinical cases of mastitis however, increased MDA levels were detectable from subclinical cases. Bacterial isolates from mastitc halves milk samples are different in levels of MDA. In this study, we found that milk samples infected with S. aureus were higher compared to other pathogens. In conclusion, differences in both severities of IMI and mastitis pathogens were associated with differences of MDA in infected udders. Keywords: sheep, milking, mastitis, lipid peroxidation, S. aureus , coagulase negative staphylococci References Contreras, A. et al. (2007). Mastitis in small ruminants. Small Ruminant Research, 68(1-2), 145–153. https://doi.org/10.1016/j.smallrumres.2006.09.011 Fthenakis, G. C. (1995). California mastitis test and White side test in diagnosis of subclinical mastitis of dairy ewes. Small Ruminant Research , 16(3), 271–276. https://doi.org/10.1016/0921-4488(95)00638-2 Hariharan, H. et al. (2004). Bacteriology and somatic cell counts in milk samples from ewes on a Scottish farm. Canadian Journal of Veterinary Research, 68(3), 188–192. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1142138/ Malinowski, E. et al. (2008). Etiological agents of dairy cows’ mastitis in western part of Poland. Polish Journal of Veterinary Sciences, 9(3), 191-194. https://pubmed.ncbi.nlm.nih.gov/17020014/ Mørk, T. et al. (2007). Clinical mastitis in ewes; bacteriology, epidemiology and clinical features. Acta Veterinaria Scandinavica , 49(1), 23. https://dx.doi.org/10.1186%2F1751-0147-49-23 Pyörälä, S. and Taponen, S. (2009). Coagulase-negative staphylococci - Emerging mastitis pathogens. Veterinary Microbiology , 34(2), 3–8. https://doi.org/10.1016/j.vetmic.2008.09.015 Sharma, N. (2011). Oxidative stress and antioxidant status during transition period in dairy cows. Asian-Australian Journal of Animal Science , 24(4), 479–484. https://www.ajas.info/upload/pdf/24-58.pdf Suriyasathaporn, W. (2006). Higher somatic cell counts resulted in higher malondialdehyde concentrations in raw cow´s milk. International Dairy Journal, 16(9), 1088–1091. https://doi.org/10.1016/j.idairyj.2005.11.004 Turk, R. et al. (2017). The role of oxidative stress and inflammatory response in the pathogenesis of mastitis in dairy cows. Mljekarstvo, 67(2), 91–101. https://doi.org/10.15567/mljekarstvo.2017.0201
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".