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Tuesday, 12 June 2018
Threshold somatic cell count for delineation of subclinical mastitis cases
Research (Published online: 12-06-2018)
9. Threshold somatic cell count for delineation of subclinical mastitis cases
P. V. Jadhav, D. N. Das, K. P. Suresh and B. R. Shome
Veterinary World, 11(6): 789-793
Aim: Somatic cell count (SCC) is the most widely used single reliable indicator of udder health. The present study was carried out with an objective to find the exact threshold of SCC.
Materials and Methods: Milk samples collected from a total of 214 Holstein Friesian crossbred dairy animals were subjected to bacterial DNA extraction and SCC estimation by digital PortaCheck. California Mastitis Test and polymerase chain reaction based on amplification of organism using reported primers were performed to diagnose subclinical mastitis. Receiver's operating characteristic (ROC) curve analysis and discriminate function analyses were performed using SPSS 18 software.
Results: ROC curve analysis represented that the area under the curve was 0.930 with the standard error of 0.02. Results indicated that 93% of the case could be correctly predicted as mastitis infected using SCC as a marker (p<0.001). At cut score level of 282 000 cells/ml, 285,000 cells/ml and 288,000 cells/ml, sensitivity remained 92.6% and specificity augmented as 86.3%, 87.2%, and 88%, respectively. At SCC value of 310,000 cells/ml of milk, sensitivity and specificity were optimal, namely, 92.6% and 91.5%, respectively. The function fitted demonstrated 89.2% accuracy with p<0.001. The functions at group centroids were -0.982 and 1.209, respectively, for normal and mastitis-infected animals and log_SCC value was the most important factor contributing 38.30% of the total distance measured.
Conclusion: Our study supports that the threshold value to delineate subclinical mastitis case from the normal is 310,000 somatic cells/ml of milk and a model so fitted using the variable SCC can be successfully used in field for the diagnosis of subclinical cases of mastitis which otherwise would be difficult to differentiate based on clinical signs.