Presence of pathogen DNA in milk harvested from quarters is associated to changes in cows' milk yield and composition.

BACKGROUND: Intramammary infection is the result of invasion and multiplication of microorganisms in the mammary gland and commonly leads to mastitis in dairy animals. Although much has been done to improve cows' udder health, mastitis remains a significant and costly health issue for dairy farmers, especially if subclinical. In this study, quarter milk samples from clinically healthy cows were harvested to detect pathogens via quantitative PCR (qPCR) and evaluate changes in individual milk traits according to the number of quarters infected and the type of microorganism(s). A commercial qPCR kit was used for detection of Mycoplasma bovis, Mycoplasma spp., Staphylococcus aureus, coagulase-negative staphylococci (CNS), Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Prototheca spp., Escherichia coli, Klebsiella spp., Enterococcus spp. and Lactococcus lactis ssp. lactis. Quarter and pooled milk information of 383 Holstein, 132 Simmental, 129 Rendena, and 112 Jersey cows in 9 Italian single-breed herds was available. RESULTS: Among the cows with pathogen(s) present in at least 1 quarter, CNS was the most commonly detected DNA, followed by Streptococcus uberis, Mycoplasma bovis, and Streptococcus agalactiae. Cows negative to qPCR were 206 and had the lowest milk somatic cell count. Viceversa, cows with DNA isolated in ≥ 3 quarters were those with the highest somatic cell count. Moreover, when major pathogens were isolated in ≥ 3 quarters, milk had the lowest casein index and lactose content. In animals with pathogen(s) DNA isolated, the extent with whom milk yield and major solids were impaired did not significantly differ between major and minor pathogens. CONCLUSIONS: The effect of the number of affected quarters on the pool milk quality traits was investigated in clinically healthy cows using a commercial kit. Results remark the important negative effect of subclinical udder inflammations on milk yield and quality, but more efforts should be made to investigate the presence of untargeted microorganisms, as they may be potentially dangerous for cows. For a smarter use of antimicrobials, analysis of milk via qPCR is advisable - especially in cows at dry off - to identify quarters at high risk of inflammation and thus apply a targeted/tailored treatment.

Application of a short-wave pocket-sized near-infrared spectrophotometer to predict milk quality traits.

Portable handheld devices based on near-infrared (NIR) technology have improved and are gaining popularity, even if their implementation in milk has been barely evaluated. Thus, the aim of the present study was to assess the feasibility of using short-wave pocket-sized NIR devices to predict milk quality. A total of 331 individual milk samples from different cow breeds and herds were collected in 2 consecutive days for chemical determination and spectral collection by using 2 pocket-sized NIR spectrophotometers working in the range of 740 to 1,070 nm. The reference data were matched with the corresponding spectrum and modified partial least squares regression models were developed. A 5-fold cross-validation was applied to evaluate individual device performance and an external validation with 25% of the dataset as the validation set was applied for the final models. Results revealed that both devices' absorbance was highly correlated but greater for instrument A than B. Thus, the final models were built by averaging the spectra from both devices for each sample. The fat content prediction model was adequate for quality control with a coefficient of determination (R2ExV) and a residual predictive deviation (RPDExV) in external validation of 0.93 and 3.73, respectively. Protein and casein content as well as fat-to-protein ratio prediction models might be used for a rough screening (R2ExV >0.70; RPDExV >1.73). However, poor prediction models were obtained for all the other traits with an R2ExV between 0.43 (urea) and 0.03 (SCC), and a RPDExV between 1.18 (urea) and 0.22 (SCC). In conclusion, short-wave portable handheld NIR devices accurately predicted milk fat content, and protein, casein, and fat-to-protein ratio might be applied for rough screening. It seems that there is not enough information in this NIR region to develop adequate prediction models for lactose, SCC, urea, and freezing point.

Pathogen Detection via Quantitative PCR in Milk of Healthy Cows Collected Using Different Sampling Protocols.

In this study we evaluated the prevalence of pathogens detected via quantitative PCR (qPCR) in milk from apparently healthy cows to identify the most common etiological agents present in Italian dairy farms. Milk samples were collected using a sterile protocol at quarter-level (3239 samples, 822 cows) and a conventional protocol at udder level as composite milk from the functional quarters of each cow (5464 samples, 5464 cows). The qPCR commercial kit detected Mycoplasma bovis, Mycoplasma spp., Staphylococcus aureus, coagulase-negative staphylococci (CNS), Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Prototheca spp., Escherichia coli, Klebsiella spp., Enterococcus spp. and Lactococcus lactis ssp. lactis as well as DNA from the penicillin resistance ß-lactamase gene from staphylococci. The prevalence of specific DNA was calculated based on its presence or absence in the samples, factoring in both the sampling protocols and herds. Regardless of the sampling protocol used, the most frequently detected pathogens were CNS (26.6% in sterile and 13.9% in conventional protocol) and Streptococcus uberis (9.6% and 16.5%, respectively). These results underscore the necessity for pathogen-specific interventions at the farm level to enhance the udder health of dairy cows via management recommendations.