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Aims: The aim of this study was to assess a phage-displayed MilA protein of Myc. bovis in an indirect ELISA for the detection of Myc. bovis antibodies in milk samples., Methods and Results: The desired sequence of milA gene was synthesized and cloned into pCANTAB-F12 phagemid vector. The expression of the MilA on the phage surface was confirmed by Western blotting. The recombinant phage was used in the development of an indirect ELISA to detect Myc. bovis antibodies in milk samples. There was a significant agreement between the results of phage-based ELISA and recombinant GST-MilA ELISA for the detection of Myc. bovis antibodies in milk samples., Conclusions: The inexpensive and convenient phage-based ELISA can be used instead of recombinant protein/peptide ELISA as an initial screening of Myc. bovis-associated mastitis., Significance and Impact of Study: Mastitis associated with Myc. bovis is a continuous and serious problem in the dairy industry. Sero-monitoring of Myc. bovis infection cases are one of the key factors for surveillance of the infections in dairy farms. Despite the existence of some commercially serological assays for Myc. bovis antibodies, they have some limitations regarding their sensitivity and availability. The development of accurate diagnosis tools could contribute to control programmes of Myc. bovis-associated mastitis in the dairy herds., (© 2022 The Authors. Journal of Applied Microbiology published by John Wiley & Sons Ltd on behalf of Society for Applied Microbiology.)

Mastitis is probably the most common and costly disease in modern dairy cow husbandry. The aim of the present paper was to report the results concerning udder health after implementation of a specific strategy using both field and laboratory methods. During the period June 2010-December 2011 a total of 674 dairy cows from four dairy farms were included in the investigation. Clinical mastitis was diagnosed by detection of signs of inflammation in the udder, while subclinical mastitis was diagnosed at the animal level by an increased Somatic Cell Count (SCC) using laboratory tests, and subsequently confirmed at quarter level by California Mastitis Test (CMT).Microbiological analysis of the milk samples was carried out by standard procedures using Gram staining, biochemical tests and automated identification system.The distribution of somatic cell counts on cow level (n=674) was:305 (45.3%) with SCC less than 100,000SCC/mL, 236 (35.0%) 100,001 - 350,000 SCC/mL, and 133 (18.7%) with more than 350,000SCC/mL. From a total of 1684 quarters tested by CMT, 644 quarters (38.2%) were positive and 1040 quarters (61.8%) were negative. In 60 out of 101 quarters that had a positive CMT result and no current treatment and that were sampled for bacteriology, bacteria could be isolated. Main bacteria identified, were coagulase - negative staphylococci (40.0%), Streptococcus agalactiae was present in 25.0%, Escherichia coli in 16.6%, Proteus spp. in 11.7% and Staphylococcus aureus in 6.7% of the bacteriological positive samples. After introducing specific mastitis-control measures, focusing on milking hygiene, dry-off treatment, and antibiotic treatment of both clinical and sub-clinical mastitis cases, the prevalence of subclinical mastitis was reduced from 38.2to 10.8%, while the incidence of clinical mastitis decreased from 21.0% to 4.9%.In conclusion, the implementation of a standard mastitis control plan based on a regular assessment of the somatic cell count can reduce the prevalence of clinical and sub-clinical mastitis in dairy herds.

Lactoferrin (LF) is glycoprotein, which is normally excreted in cow's milk, but as a result of inflammatory processes in the mammary gland, its concentration is being increased. The aim of this research was to determinate whether the LF concentration in milk is influenced by increased number of milk somatic cell count. In this research we used milk from (n = 345) Holstein-Friesian cows in Pelagonia region, or more precisely milk from healthy (n = 112) and infected quarters (n = 233). A quarter was classified as healthy when no visible signs of mastitis were detected or the California Mastitis Test (CMT) was negative. A quarter was classified as subclinical when no visible signs of mastitis were detected but CMT was positive. A quarter was classified as clinical mastitis when there were visible signs of mastitis (cow, udder or milk appearance). The milk samples were analysed instrumentally for physico - chemical properties (Lactoscope C4+), somatic cell count (SCC) by Bentley Somacount CC 150, total number of bacteria (CFU) (by Bentley Bactocount IBC), and by ELISA for concentrations of lactoferrin. We used SPSS 15.0 for analysis of gained data. Significant differences were detected in concentrations of lactoferrin in healthy quarters (915.94 µg/mL) and in quarters with sub-clinical (2,095.87 µg/mL) and clinical mastitis (3,528.99 µg/mL) (p < 0.001). A correlation between lactoferrin and lactose (r = -0.21, p < 0.01), lactoferrin and CFU (r = 0.28, p < 0.01) as well as between lactoferrin and somatic cells (r = 0.32, p < 0.01) was observed. These results initiate that lactoferrin could be used as complementary test to somatic cell count, and potential to be a primary diagnostic test for subclinical and clinical mastitis in dairy cows. Furthermore, it could be used as natural antibiotic in treatment of mastitis in dairy cows, in order to decrease the presence of antibiotic residuals in milk, thereby to improve hygiene of raw milk. [ABSTRACT FROM AUTHOR]

The objective of this study was to detect Mycoplasma bovis specific antibodies using a recently reported MilA ELISA with the aim to detect M. bovis antibodies in milk. An indirect ELISA, based on a recombinant fragment of the Mycoplasma immunogenic lipase A (MilA) protein, was conducted on 291 milk samples for the detection of M. bovis antibodies. Samples were also tested with conventional Mycoplasma culture and M. bovis PCR. Samples were collected from individual cows from 2 commercial dairy herds in South Australia. Of 291 samples tested, 68 (23.4%) were detected positive for M. bovis antibodies, 150 (51.5%) were positive for M. bovis in PCR and 166 (57.0%) in bacterial culture. These results indicate that MilA indirect ELISA can be utilized for the detection of M. bovis antibodies in milk. [ABSTRACT FROM AUTHOR]

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Abstract: Increasing reports of multidrug‐resistant bacterial infections in animals has created a need for novel antimicrobial agents that do not promote cross‐resistance to critically important antimicrobial classes used in human medicine. In response to the recent emergence of antimicrobial resistance in several bovine mastitis pathogens, in vitro antimicrobial susceptibility was determined for four polyether ionophores (lasalocid, monensin, narasin and salinomycin) against Staphylococcus spp. and Streptococcus spp. isolated from clinical cases. In addition, erythrocyte haemolysis and WST‐1 cell proliferation assays were used to assess in vitro mammalian cell cytotoxicity and biofilm susceptibility testing was performed using the minimum biofilm eradication concentration (MBEC™) biofilm assay. Lasalocid, monensin, narasin and salinomycin exhibited bacteriostatic antimicrobial activity against all pathogens tested, including methicillin‐resistant staphylococci, with MIC90 values <16 μg/ml. Narasin and monensin displayed the least toxicity against mammalian cell lines and all compounds significantly reduced viable cell numbers in a Staphylococcus aureus biofilm. Based on in vitro characterization, all four ionophores offer potentially novel treatments against bovine mastitis but in vivo studies will be essential to determine whether acceptable safety and efficacy is present following intramammary administration. [ABSTRACT FROM AUTHOR]

Sub-clinical mastitis (SCM) affects milk composition. In this study, we hypothesise that large-scale mining of milk composition features by pattern recognition models can identify the best predictors of SCM within the milk composition features. To this end, using data mining algorithms, we conducted a large-scale and longitudinal study to evaluate the ability of various milk production parameters as indicators of SCM. SCM is the most prevalent disease of dairy cattle, causing substantial economic loss for the dairy industry. Developing new techniques to diagnose SCM in its early stages improves herd health and is of great importance. Test-day Somatic Cell Count (SCC) is the most common indicator of SCM and the primary mastitis surveillance approach worldwide. However, test-day SCC fluctuates widely between days, causing major concerns for its reliability. Consequently, there would be great benefit to identifying additional efficient indicators from large-scale and longitudinal studies. With this intent, data was collected at every milking (twice per day) for a period of 2 months from a single farm using in-line electronic equipment (346 248 records in total). The following data were analysed: milk volume, protein concentration, lactose concentration, electrical conductivity (EC), milking time and peak flow. Three SCC cut-offs were used to estimate the prevalence of SCM: Australian ≥ 250 000 cells/ml, European ≥200 000 cells/ml and New Zealand ≥ 150 000 cells/ml. At first, 10 different Attribute Weighting Algorithms (AWM) were applied to the data. In the absence of SCC, lactose concentration featured as the most important variable, followed by EC. For the first time, using attribute weighted modelling, we showed that the concentration of lactose in milk can be used as a strong indicator of SCM. The development of machine-learning expert systems using two or more milk variables (such as lactose concentration and EC) may produce a predictive pattern for early SCM detection. [ABSTRACT FROM AUTHOR]

Background: Mycoplasma mastitis is increasingly posing significant impact on dairy industry. Although the effects of major conventional mastitis pathogens on milk components has been widely addressed in the literature, limited data on the effects of different Mycoplasma and Acholeplasma spp. on milk quality and quantity is available. The aim of this study was to determine the casual relationship of Mycoplasma spp. and A. laidlawii to mastitis and compare them to subclinical mastitis caused by conventional mastitis pathogens from a single dairy herd in South Australia; Mycoplasma spp. and A. laidlawii were detected using PCR applied directly to milk samples. The herd had mastitis problem with high somatic cell count and low response rate to conventional antimicrobial therapy. A total of 288 cow-level milk samples were collected aseptically and used in this study. Results: Conventional culture showed a predominance of coagulase-negative staphylococci, followed by coagulasepositive staphylococci, Streptococcus spp., Enterococcus spp., E. coli, and Klebsiella spp. PCR results showed a high prevalence of mycoplasmas (76.7%), including A. laidlawii (10.8%), M. bovis (6.2%), M. bovirhinis (5.6%), M. arginini (2%), and (52.1%) of cows were co-infected with two or more Mycoplasma and Acholeplasma species. Mycoplasma co-infection significantly increased somatic cell counts (SCC) similar to conventional mastitis pathogens and compared to non-infected cows with 389.3, 550.3 and 67.3 respectively; and decreased the milk yield with 29.0, 29.9 and 34.4 l, respectively. Mycoplasma co-infection caused significant increase in protein percentage, and significant decrease in fat percentage and total milk solids, similar to other conventional mastitis pathogens. In contrast, changes in milk composition and yield caused by various individual Mycoplasma species were non-significant. Conclusions: Mycoplasma mastitis had on-farm economic consequences similar to common conventional mastitis pathogens. Results of our study indicate that co-infection Mycoplasma mastitis caused similar effect on milk composition to other mastitis pathogens and we hope these findings raise the awareness of the importance of their detection on routine diagnostic panels.e [ABSTRACT FROM AUTHOR]

Bovine mastitis is an important animal production disease that affects the dairy industry globally. Studies have estimated the prevalence of this disease in approximately 30% of African countries, with the highest prevalence found in Ethiopia. This is despite the wide cattle distribution in Africa, and the largest number of dairy farms and herds in countries such as South Africa, Kenya and Uganda. Furthermore, the estimated financial losses due to direct and indirect impacts of bovine mastitis are lacking in this continent. Therefore, intensive research efforts will help determine the continent-wide economic impacts and advance careful monitoring of disease prevalence and epidemiology. Here, published cases supporting the occurrence and importance of bovine mastitis in certain regions of Africa are outlined. [ABSTRACT FROM PUBLISHER]

Developing a reliable mastitis challenge infection model is required to test new intramammary antimicrobial preparations, other novel bovine mastitis treatments, and study mastitis pathogenesis. Three treatment groups of Holstein Friesian cows in active lactation were administered two doses (104 and 106 cfu/quarter) on a single occasion with one of the three Streptococcus uberis strains (BFR6019, MFF1283 and SA002) suspended in 5 ml of sterile PBS, administered via intramammary inoculation immediately after milking. All quarters that were challenged with S. uberis strains MLF1283 and BFR6019 showed clinical signs of mastitis on day 1 and 2 after the challenge. Strain SA002 had a lower rate of inducing clinical mastitis which was detected later than day 3 after the challenge. We successfully developed a rapid and reliable model for inducing experimental S. uberis mastitis with 100% success rate in cows in active lactation. On the basis of the correlation results between strains, RAPD fingerprinting results, clinical findings, and a 100% success rate of mastitis induction for low and high doses S. uberis strains MLF1283 and BFR6019, strain virulence seems to be a more important effect than challenge dose in induction of clinical mastitis following experimental challenge. [ABSTRACT FROM PUBLISHER]

Simple Summary: Invisible (subclinical) mastitis decreases milk quality and production. Invisible mastitis is linked to an increased use of antimicrobials. The risk of the emergence of antimicrobial-resistant bacteria is a major public health concern worldwide. Therefore, early detection of infected cows is of great importance. Machine learning has opened a new avenue for early mastitis prediction based on simple and accessible milking parameters, such as milk volume, fat, protein, lactose, electrical conductivity (EC), milking time, and milking peak flow. However, farm heterogeneity is a major challenge where multiple patterns can predict mastitis. Here, we employed a classification based on associations and scaling approach for multiple pattern discovery over multiple years. The approach we have developed helps to address farm heterogeneity and generalise machine learning-based diagnosis of mastitis worldwide. Subclinical mastitis, an economically challenging disease of dairy cattle, is associated with an increased use of antimicrobials which reduces milk quantity and quality. It is more common than clinical mastitis and far more difficult to detect. Recently, much attention has been paid to the development of machine-learning expert systems for early detection of subclinical mastitis from milking features. However, differences between animals within a farm as well as between farms, particularly across multiple years, are major obstacles to the generalisation of machine learning models. Here, for the first time, we integrated scaling by quartiling with classification based on associations in a multi-year study to deal with farm heterogeneity by discovery of multiple patterns towards mastitis. The data were obtained from one farm comprising Holstein Friesian cows in Ongaonga, New Zealand, using an electronic automated monitoring system. The data collection was repeated annually over 3 consecutive years. Some discovered rules, such as when the milking peak flow is low, electrical conductivity (EC) of milk is low, milk lactose is low, milk fat is high, and milk volume is low, the cow has subclinical mastitis, reached high confidence (>70%) in multiple years. On averages, over 3 years, low level of milk lactose and high value of milk EC were part of 93% and 83.8% of all subclinical mastitis detecting rules, offering a reproducible pattern of subclinical mastitis detection. The scaled year-independent combinational rules provide an easy-to-apply and cost-effective machine-learning expert system for early detection of hidden mastitis using milking parameters. [ABSTRACT FROM AUTHOR]

Methicillin-resistant coagulase-negative staphylococci (MRCoNS) have recently emerged as a significant cause of bovine mastitis worldwide. Here we describe the isolation of MRCoNS from cases of bovine mastitis from a single dairy farm in Australia. Fourteen CoNS isolates were identified as MRCoNS on the basis of having an oxacillin MIC of ≥0.5 μg/mL. The isolates were speciated as S. chromogenes (n = 1) S. fleurettii (n = 1) , S. haemolyticus (n = 2) , S. sciuri (n = 5) , S. simulans (n = 1) S. succinus (n = 2) and S. xylosus (n = 2). Five of the isolates ( S. fleuretti, S. haemolyticus S. sciuri and two S. succinus ) were mecA -positive. We also detected a previously described S. sciuri mecA homolog in four oxacillin-resistant S. sciuri isolates. The remainder of the putative MRCoNS did not contain any mecA -related resistance determinants in their genomes. Comparative genomic analysis of three previously published S. sciuri isolates, from humans, a squirrel and a cereal crop (rice), and a representative isolate from our study demonstrated clustering and a high degree of genetic homogeneity (>95%), suggesting S. sciuri has low host specificity. In conclusion, CoNS, in particular S. sciuri , may act as a reservoir for SCC mec elements that can easily be spread between different host species by direct cross-infection. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a predictive model for mastitis incidence, independent from Somatic Cell Count (SCC), to provide an alternative, simple, and cost-effective approach for mastitis risk management based on available milking parameters. The test-day Somatic Cell Count (SCC) is the most common indicator for Sub-Clinical Mastitis (SCM) surveillance in dairy industries worldwide. However, SCC is highly variable between days, raising major concerns for its reliability. This caveat highlights the need for longitudinal/frequent monitoring of SCC and/or developing alternative approaches for SCM surveillance. A considerable proportion of available milking data such as Milk Volume, Protein, Lactose, Electrical Conductivity (EC), Milking Time, and Peak Flow provide the possibility of pattern recognition and model discovery towards mastitis occurrence. Developing a predictive model involves: (1) finding the threshold (cutoff) of different predictive milking parameters and (2) finding the best combination of features that lead to mastitis and their hierarchical pattern/order. Here, in a large-scale study on 346,248 milking records, for the first time, we evaluated four different decision tree algorithms (Decision Tree, Stump Decision Tree, Parallel Decision Tree and Random Forest Decision Tree) with four different criteria (Accuracy, Info Gain, Gini Index and Gain Ratio) run on 11 datasets (original dataset and 10 created datasets by attribute weighting selection algorithms). Therefore, 572 models were evaluated and compared by 10-fold cross validation. The performance of each decision tree in drawing an inverted tree; with the most important feature at the root and less important variables as the leaf; was calculated by 10-fold cross validation. Random Forest Decision Tree with Gini Index criterion was the best model for predicting mastitis from milking parameters with a high accuracy of 90%. Decision Tree models identified a strong pattern for SCM in milking data where all (100%) of cows with low levels of lactose (Lactose ≤ 4.5 g/L) and low milk volume (Volume ≤ 21.7 L) had mastitis. In addition, a significant pattern was found for identifying healthy cows by high levels of lactose (Lactose ≥ 4.5 g/L) and low levels of EC (EC ≤ 5.2). This study doccuments that milking parameters mined by the Decision Tree Random Forest model can be utilised to accurately predict SCM. The findings can be employed to increase the reliability of test-day SCC or as SCC-independent and cost-effective predictors of SCM. [ABSTRACT FROM AUTHOR]