Sheep mastitis caused by staphylococci and streptococci and their influence on oxidative status
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».