Fellow: Antonia Domino

Mentor: Daryl Nydam

DESCRIPTION (provided by applicant): 

Mastitis is the most common and costly disease of dairy cattle. It impacts farm economics through diagnostic and treatment costs and loss of income through discarded milk, decreased milk production, and loss of milk quality premiums. It negatively affects cow comfort and increases the culling risk. Frustratingly, most cases of mastitis are subclinical, leading to undetected economic losses. To mitigate these losses, surveillance for subclinical mastitis (SCM) indicators is necessary. One such indicator is the somatic cell count (SCC), or the concentration of immune cells in milk. Elevated SCC indicates inflammation, most likely caused by bacterial infection. Once a case of SCM is recognized, the next step is bacterial identification. Presumptive treatment of mastitis based on SCC alone is not cost effective; bacterial identification is necessary to discriminate between cases that will and will not benefit from antimicrobial treatment. One barrier to early detection of SCM is the lag between sample collection and availability of test results. Our primary objective is to evaluate a novel handheld instrument, the DeLaval ICC, for the enumeration of somatic cells and identification of bacteria in bovine milk. We will compare the ICC with two other methods for SCC, the DeLaval DCC and the Fossomatic Cell Counter, using 250 samples of milk from cows without clinical mastitis (CM), evenly divided among 5 categories of SCC. We will then conduct two experiments to evaluate bacterial identification. We will analyze 100 clinical mastitis milk samples using the ICC and aerobic culture. We will also analyze pasteurized milk samples inoculated with one of 3 organisms (Staphylococcus aureus, Streptococcus dysgalactiae, and Escherichia coli) at two different concentrations for a total of 6 positive controls. These 6 samples will be tested in replicate (n=10) and also aerobically cultured. The ANOVA and kappa coefficient will be used to measure agreement between SCC test methods; the bacteriological data will be analyzed using the chi-square test and kappa coefficient. Sensitivity and specificity will be calculated for all parameters, and compared to the gold standard. We hypothesize that the ICC will be equivalent to other methods of SCC and bacterial identification.