Bovine mastitis is a major cause of economical losses in dairy production. Identification of infected animals is often difficult, for the etiopathological aspects of the disease, and for the costs of cyto-bacteriological exams. In the present study we explored the possibility of identifying diseased animals using only cytological tests. Milk was sampled from 184 quarters of 50 cows, in 3 herds, and submitted to somatic cell counting and bacteriological analysis, according to NMC guidelines. Each sample was also centrifuged, the cell pellet was spread on a slide, stained with May-Grünwald Giemsa, and evaluated by light microscopy. Relative percentages of leukocytes and three ratios were calculated: lymphocytes (L%), macrophages (M%), neutrophils (PMN%), and phagocytes per lymphocytes (Ph/L), macrophages per neutrophils (M/PMN) and neutrophils per lymphocytes (PMN/L). The ratios were normalized to natural logarithm. Receiver operating characteristic (ROC) curves were generated for each percentage and ratio. Statistical analysis was performed using Student’s-t test. Quarters were classified as healthy or diseased based on SCC and bacteriological results. Herd B and C showed a low and high prevalence of Staphylococcus aureus infected quarters, respectively, while herd A was free from contagious pathogens. Differences between groups were significant (P<0.05) for all variables, except for M%. Natural logarithm (PMN/L) and L% gave the best area under curve values (0.813 and 0.795, respectively). The overall sensitivity was 82.7% (69.2%, 87.5% and 96.8% in herd A, B and C, respectively), and the specificity was 63.9%. Based on the different etiologic agents in herd A, a new ROC analysis for herds free of contagious pathogens was run for both Ln (PMN/L) and L%, thereby achieving a sensitivity of 79.4%. In conclusion, differential cell count could be used as a good screening method, reducing the number of bacteriological tests needed, and increasing the income for farmers.
How differential somatic cell counting can help in sustainable herd management / R. Piccinini, R. Pilla. ((Intervento presentato al convegno IDF World Dairy Summit : October, 12th - 19th tenutosi a Parma nel 2011.
How differential somatic cell counting can help in sustainable herd management.
R. Piccinini
;R. Pilla
2011
Abstract
Bovine mastitis is a major cause of economical losses in dairy production. Identification of infected animals is often difficult, for the etiopathological aspects of the disease, and for the costs of cyto-bacteriological exams. In the present study we explored the possibility of identifying diseased animals using only cytological tests. Milk was sampled from 184 quarters of 50 cows, in 3 herds, and submitted to somatic cell counting and bacteriological analysis, according to NMC guidelines. Each sample was also centrifuged, the cell pellet was spread on a slide, stained with May-Grünwald Giemsa, and evaluated by light microscopy. Relative percentages of leukocytes and three ratios were calculated: lymphocytes (L%), macrophages (M%), neutrophils (PMN%), and phagocytes per lymphocytes (Ph/L), macrophages per neutrophils (M/PMN) and neutrophils per lymphocytes (PMN/L). The ratios were normalized to natural logarithm. Receiver operating characteristic (ROC) curves were generated for each percentage and ratio. Statistical analysis was performed using Student’s-t test. Quarters were classified as healthy or diseased based on SCC and bacteriological results. Herd B and C showed a low and high prevalence of Staphylococcus aureus infected quarters, respectively, while herd A was free from contagious pathogens. Differences between groups were significant (P<0.05) for all variables, except for M%. Natural logarithm (PMN/L) and L% gave the best area under curve values (0.813 and 0.795, respectively). The overall sensitivity was 82.7% (69.2%, 87.5% and 96.8% in herd A, B and C, respectively), and the specificity was 63.9%. Based on the different etiologic agents in herd A, a new ROC analysis for herds free of contagious pathogens was run for both Ln (PMN/L) and L%, thereby achieving a sensitivity of 79.4%. In conclusion, differential cell count could be used as a good screening method, reducing the number of bacteriological tests needed, and increasing the income for farmers.
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