Interference of Streptococcus agalactiae Blitz Therapy in Staphylococcus aureus Microbiological Diagnosis in Subclinical Bovine Mastitis.

Bovine mastitis is an important and costly disease to dairy cattle. Diagnostic methods usually performed in Brazil are somatic cell counts (SCC) and milk microbiology. Low bacteria shedding in milk implies no colony growth in microbiological tests and false negative results. Streptococcus agalactiae and Staphylococcus aureus are principal pathogens of mixed mastitis. However, S. agalactiae has a higher bacterial release from the mammary gland than S. aureus, affecting microbiological sensitivity to diagnose S. aureus. This study aimed to estimate the SCC and total bacterial count (TBC) from cows according to pathogen isolated in milk and to evaluate variation in S. aureus diagnosis by a microbiological test during S. agalactiae treatment, which is called blitz therapy. Both S. agalactiae and S. aureus presented high SCC means, although S. agalactiae showed shedding of bacteria 2.3 times greater than S. aureus. Microbiological sensitivity to S. aureus increased for 5 months during S. agalactiae treatment. The prevalence of S. agalactiae fell after 5 months of therapeutic procedures. The prevalence of S. aureus increased to 39.0. The results showed that due to high sensitivity, the polymerase chain reaction (PCR) could be used at the beginning of blitz therapy with the goal of S. agalactiae eradication from the dairy herd.

The determination of mastitis severity at 4-level using Milk physical properties: A deep learning approach via MLP and evaluation at different SCC thresholds.

Current research aims to generate an alternative model to classical methods in the determination of subclinical mastitis at 4 levels (healthy, suspicious, subclinical, and clinical). For this purpose, multilayer perceptron (MLP) artificial neural networks (ANN) was developed as test model. 5 variables from the physical properties of milk somatic cell count (SCC), electrical conductivity (EC), pH, density, and temperature at fore milking (TFM) were included in the model in the classification of mastitis. Model performance was validated on test data (%25) and compared with the multinomial logistic regression (MNLR). MLP model has shown a satisfactory performance with an accuracy of 95.14% and - 141 of AIC score better than the control model (MNLR) of 80.27% and - 133 AIC despite using higher number of parameters (104). Since the main problem is to diagnose subclinical mastitis, which does not cause any visible symptoms, it was important to distinguish between absolute subclinical (suspicious excluded positives) and absolute healthy (suspicious included positives) ones. Therefore, optimum cut-off threshold was evaluated for these two different scenarios with only variable SCC the gold standard indicator of subclinical mastitis and results were compared in the interpretation of model performance. The results show that the 5-variable MLP model exhibits a high sensitivity of 93.22% (AUC = 0.95 for healthy ones) at low cutoff thresholds as well. New studies should provide a better understanding by evaluating economics, sustainability, animal welfare and health aspects together to determine the optimal threshold value.

Effect of low milking frequency on the concentration of antimicrobial proteins in goat milk.

This study examined the effects of low frequency milking on the concentrations of antimicrobial components in goat milk. Sixteen goats were divided into two groups of eight each: milking once every 2 d three times (for six days, three times group) or five times (for 10 days, five times group). On other days, milking was performed once daily. Milk was collected, and milk yield, somatic cell count (SCC), and the concentrations of some antimicrobial proteins such as lactoferrin (LF), S100A7, IgA, and sodium ions (Na+) in milk were measured. Milk yield significantly decreased in both the groups during the low-milking frequency period, followed by an increase above the low frequency milking period in both groups. In contrast, SCC and LF concentrations in milk increased in both groups during the low frequency milking period. The concentration of S100A7 in milk temporarily decreased after the low frequency milking period, followed by a significant increase. The S100A7 concentration during this period was higher in the five times group than in the three times group. These results indicated that low frequency milking induced a gradual decrease in milk yield and a concomitant increase in antimicrobial components, such as LF and S100A7, in milk. This increase in the antimicrobial components may be useful in preventing mastitis.

Effects of Parity and Somatic Cell Count Threshold on Udder Morphology, Milkability Traits, and Milk Quality in Canarian Goats.

The effects of parity and somatic cell count in milk (SCC) threshold on the udder morphology, milkability traits, and milk composition was evaluated in 41 Canarian goats in mid-lactation. The animals were divided according to parity (1st, 2nd, and 3rd), and a SCC threshold of 2000 × 103 cells/mL in milk was set to evaluate the effect of this factor on the different measured parameters. Results showed that primiparous goats had the udder smaller and less distended than multiparous goats, but no differences were detected on milk flow parameters. Furthermore, SCC and total bacterial count (TBC) tended to be higher when the parity increased. On the other hand, goats with SCC ≤ 2000 × 103 had higher cistern-floor distance (CF) and lower TBC values compared with those goats with a count above the predetermined threshold. The results suggest that a reduction in SCC can be achieved by a selection of udder morphological traits. Moreover, milk flow parameters do not seem to be a tool to determine the udder health status in Canarian goats, but long-term studies are needed to verify it.

Alternative therapeutic approach of ovine subclinical mastitis using the ethanolic roots extract of Capparis spinosa.

Background: Over the past decades, Capparis spinosa has been considered a traditional therapy for relieving different illnesses. Mastitis causes a decrease in milk production and is usually treated with injectable and intra-mammary antibiotics. Aim: Investigating the therapeutic effects of C. spinosa root extract on subclinically mastitic ewes. Methods: Totally, 164 lactating ewes were selected randomly from the flocks that existed in some areas belonging to Al-Najaf City (Najaf, Iraq) from September to December (2022). Each study animal was subjected to direct sampling of milk before and once each week for 6 weeks (42 days) post treatment to be tested directly by the California mastitis test (CMT). Results: Concerning phytochemical testing of ethanolic root extract, the findings revealed a significant increase in the concentration of alkaloids, flavonoids, polyphenols, and tannins when compared to other components such as coumarins, saponin, glycosides, amino acids, and steroids. In this study, there were 44.51% infected ewes with subclinical mastitis, involving 25.61%, 13.41%, and 5.49% for scores 1, 2, and 3, respectively. In comparison with pre-treatment week, insignificant alteration was seen in the values of all scores in therapeutic week 1. However, significant differences were initiated in values of score 0 in week 2; score 0 and score 2 in week 3; score 0, score 1, and score 2 in week 4; and values of all scores in weeks 5 and 6. Conclusion: This represents the first Iraqi study aimed at the treatment of subclinical mastitis in sheep using the root extract of C. spinosa. Phytochemical testing of ethanolic extract revealed the presence of variable amounts of chemical compounds that reflect their effects on treated animals by decreasing the number of infected ewes with the disease. Moreover, studies are greatly important to estimate the therapeutic effects of other parts of C. spinosa such as leaves and seeds, on the disease and other animal diseases.

Genetic parameters for mid-infrared-spectroscopy-predicted mastitis phenotypes and related traits.

Genetic improvement of udder health in dairy cows is of high relevance as mastitis is one of the most prevalent diseases. Since it is known that the heritability of mastitis is low and direct data on mastitis cases are often not available in large numbers, auxiliary traits, such as somatic cell count (SCC) are used for the genetic evaluation of udder health. In previous studies, models to predict clinical mastitis based on mid-infrared (MIR) spectral data and a somatic cell count-derived score (SCS) were developed. Those models can provide a probability of mastitis for each cow at every test-day, which is potentially useful as an additional auxiliary trait for the genetic evaluation of udder health. Furthermore, MIR spectral data were used to estimate contents of lactoferrin, a glycoprotein positively associated with immune response. The present study aimed to estimate heritabilities (h2) and genetic correlations (ra) for clinical mastitis diagnosis (CM), SCS, MIR-predicted mastitis probability (MIRprob), MIR + SCS-predicted mastitis probability (MIRSCSprob) and lactoferrin estimates (LF). Data for this study were collected within the routine milk recording and health monitoring system of Austria from 2014 to 2021 and included records of approximately 54,000 Fleckvieh cows. Analyses were performed in two datasets, including test-day records from 5 to 150 or 5 to 305 days in milk. Prediction models were applied to obtain MIR- and SCS-based phenotypes (MIRprob, MIRSCSprob, LF). To estimate heritabilities and genetic correlations bivariate linear animal models were applied for all traits. A lactation model was used for CM, defined as a binary trait, and a test-day model for all other continuous traits. In addition to the random animal genetic effect, the fixed effects year-season of calving and parity-age at calving and the random permanent environmental effect were considered in all models. For CM the random herd-year effect, for continuous traits the random herd-test day effect and the covariate days in milk (linear and quadratic) were additionally fitted. The obtained genetic parameters were similar in both datasets. The heritability found for CM was expectedly low (h2 = 0.02). For SCS and MIRSCSprob, heritability estimates ranged from 0.23 to 0.25, and for MIRprob and LF from 0.15 to 0.17. CM was highly correlated with SCS and MIRSCSprob (ra = 0.85 to 0.88). Genetic correlations of CM were moderate with MIRprob (ra = 0.26 and 0.37) during 150 and 305 days in milk, respectively and low with LF (h2 = 0.10 and 0.11). However, basic selection index calculations indicate that the added value of the new MIR-predicted phenotypes is limited for genetic evaluation of udder health.

Total bacterial count and somatic cell count in bulk and individual goat milk around kidding: two longitudinal observational studies.

Total bacterial count (TBC) and somatic cell count (SCC) are important quality parameters in goat milk. Exceeding the bulk milk TBC (BMTBC) thresholds leads to price penalties for Dutch dairy goat farmers. Controlling these milk quality parameters can be challenging, especially around kidding. First, we describe the variation and the peaks around kidding of TBC and SCC in census data on Dutch bulk milk over the last 22 years. Second, to explore causes of these elevations, we studied the variation of TBC and SCC in individual goat milk from 3 weeks before to 5 weeks after kidding and their association with systemic response markers interferon-γ (IFN-γ), calprotectin, ß-hydroxybutyrate (BHB), body condition score (BCS) and fecal consistency. We visited 4 Dutch dairy goat farms weekly for 10 to 16 weeks around kidding. Some of the goats had been dried off, other goats were milked continuously throughout pregnancy. A total of 1,886 milk samples from 141 goats were collected for automated flowcytometric quantification of TBC and SCC measurement. IFN-γ, calprotectin and BHB were determined twice in blood of the same goats, most samples were collected after kidding. The BCS and fecal consistency were scored visually before and after kidding. We found a strong correlation between TBC and SCC (Spearman's rho = 0.87) around kidding. Furthermore, in the third week before kidding, the average TBC (5.67 log10 cfu/mL) and SCC (6.70 log10 cells/mL) were significantly higher compared with the fifth week after kidding, where the average TBC decreased to 4.20 log10 cfu/mL and the average SCC decreased to 5.92 log10 cells/mL. In multivariable linear regression models, farm and stage of lactation were significantly associated with TBC and SCC, but none of the systemic response markers correlated with TBC or SCC. In conclusion, TBC and SCC in dairy goats were high in late lactation and decreased shortly after parturition. For SCC, the dilution effect might have caused the decrease, but this was not plausible for TBC. Moreover, the excretion of bacteria and cells in goat milk was not associated with the selected systemic response markers that were chosen as a read out for general immunity status, intestinal health and metabolic diseases. Therefore, we assume that the TBC increase before kidding and the decrease after parturition is caused by other systemic, possibly hormonal, processes. To reduce BMTBC and BMSCC, it would be advisable to keep milk of goats with highest numbers of bacteria and cells in their milk out of the bulk milk during end lactation. Further studies are needed to investigate the effects of withholding this end lactation milk from the bulk tank.

Concentrations of antimicrobial components in milk of Japanese Black cows and their differences from dairy cows.

The components of milk from beef cows remain to be elucidated. This study examined the differences in the antimicrobial components of milk between dairy and beef cows. Quarter milk was collected from both Japanese Black (beef type) and Holstein (dairy type) cows to compare the concentrations of antimicrobial components. The concentration of lingual antimicrobial peptide (LAP) was higher, whereas that of the other antimicrobial components (lactoferrin, S100A7, and S100A8) was lower in beef cows than in dairy cows. Overall, these results indicate that the differences in antimicrobial components between beef and dairy cows may be associated with the difference in the prevalence of mastitis between them.

Comparisons of recycled manure solids and wood shavings/sawdust as bedding material -implications for animal welfare, herd health, milk quality and bedding costs in Swedish dairy herds.

Increasing shortages and costs of common bedding materials have led dairy farmers in Sweden to consider using recycled manure solids (RMS), which are readily available and low-cost, as an alternative bedding material. The main risks are effects on udder health and milk quality, but RMS could also affect animal welfare and claw health. The advantages and disadvantages of using RMS bedding have not been fully investigated, and findings in other countries cannot be directly applied to Swedish conditions and climate. This observational cross-sectional study investigated the use of RMS as bedding regarding associations with certain aspects of animal welfare, herd health, milk quality, and bedding costs in Swedish dairy herds. Thirty-four dairy farms using RMS or wood shavings/sawdust (each n = 17) were compared. Each farm was visited 2 times during the housing period 2020-2021, once in October-December and once in March-May. Dairy barns were observed, animal welfare was assessed, and free-stall dimensions were measured. Farm owners were interviewed about housing system characteristics, herd performance, and herd management. Data on milk production and herd health were obtained from the Swedish official milk recording scheme for the indoor period October-March. The prevalence of claw disorders and abnormal claw conformation were collected from the national claw health database for the period, October-May. On each farm visit, composite samples of unused bedding outside the barn and used bedding material from the free stalls, respectively, were taken for total bacterial count and dry matter analysis. Samples of bulk tank milk for determination of total bacterial count were taken in connection to the visits. In addition, samples of unused and used bedding material and manure from alleys for analysis of 3 Treponema species associated with digital dermatitis (DD) were gathered and analyzed. Total bacterial count was significantly higher in unused (8.50 log10 cfu/g) and used RMS bedding (9.75 log10 cfu/g) than in wood shavings/sawdust (used 4.74; unused 8.63 log10 cfu/g), but there were no significant differences in bulk milk total bacterial count (median 4.07 versus 3.89 log10 cfu/mL) or somatic cell count (median 243,800 versus 229,200 cells /mL). The aspects of animal welfare that were assessed did not differ significantly between the 2 bedding systems, while the prevalence of total claw disorders (25.9 versus 38.0% of trimmed cows), dermatitis (6.9 versus 16.2% of trimmed cows) and sole ulcers (2.0 versus 4.0% of trimmed cows) were significantly lower in the RMS herds. Treponema spp. were not detected in unused RMS material, but all RMS herds had presence of DD recorded at foot trimming. An economic assessment based on the interview results and price level from winter 2021 revealed that the costs of RMS bedding varied with amount of RMS produced. Thus, RMS is a potential alternative bedding material for dairy cows in Sweden and can be a profitable option for large dairy herds. However, the high level of total bacteria in the material requires attention to bedding and milking routines as well as regular monitoring of herd health.

A New Method to Detect Buffalo Mastitis Using Udder Ultrasonography Based on Deep Learning Network.

Mastitis is one of the most predominant diseases with a negative impact on ranch products worldwide. It reduces milk production, damages milk quality, increases treatment costs, and even leads to the premature elimination of animals. In addition, failure to take effective measures in time will lead to widespread disease. The key to reducing the losses caused by mastitis lies in the early detection of the disease. The application of deep learning with powerful feature extraction capability in the medical field is receiving increasing attention. The main purpose of this study was to establish a deep learning network for buffalo quarter-level mastitis detection based on 3054 ultrasound images of udders from 271 buffaloes. Two data sets were generated with thresholds of somatic cell count (SCC) set as 2 × 105 cells/mL and 4 × 105 cells/mL, respectively. The udders with SCCs less than the threshold value were defined as healthy udders, and otherwise as mastitis-stricken udders. A total of 3054 udder ultrasound images were randomly divided into a training set (70%), a validation set (15%), and a test set (15%). We used the EfficientNet_b3 model with powerful learning capabilities in combination with the convolutional block attention module (CBAM) to train the mastitis detection model. To solve the problem of sample category imbalance, the PolyLoss module was used as the loss function. The training set and validation set were used to develop the mastitis detection model, and the test set was used to evaluate the network's performance. The results showed that, when the SCC threshold was 2 × 105 cells/mL, our established network exhibited an accuracy of 70.02%, a specificity of 77.93%, a sensitivity of 63.11%, and an area under the receiver operating characteristics curve (AUC) of 0.77 on the test set. The classification effect of the model was better when the SCC threshold was 4 × 105 cells/mL than when the SCC threshold was 2 × 105 cells/mL. Therefore, when SCC ≥ 4 × 105 cells/mL was defined as mastitis, our established deep neural network was determined as the most suitable model for farm on-site mastitis detection, and this network model exhibited an accuracy of 75.93%, a specificity of 80.23%, a sensitivity of 70.35%, and AUC 0.83 on the test set. This study established a 1/4 level mastitis detection model which provides a theoretical basis for mastitis detection in buffaloes mostly raised by small farmers lacking mastitis diagnostic conditions in developing countries.

Effects of Elevating Zinc Supplementation on the Health and Production Parameters of High-Producing Dairy Cows.

This study's objective was to determine the effects of increasing the dietary added zinc (Zn) on the milk production, milk somatic cell count (SCC), and immunoglobulin and antioxidant marker concentrations in the blood of dairy cows. Twelve Holstein cows (67 ± 2.5 days in milk) were assigned randomly to (1) a diet containing Zn-methionine at 76 mg/kg of DM (CTL) or (2) CTL top-dressed with about 21 mg/kg of DM extra Zn-methionine (+Zn) for 70 d. The concentrations of reduced (GSH) and oxidized (GSSG) glutathione, malondialdehyde (MDA), catalase (CAT), superoxide dismutase (SOD), and immunoglobulins in the blood were measured on d 0, 35, and 70. Compared to CTL, +Zn decreased the dry matter intake (DMI) throughout the trial and the milk yield (MY) during the first phase of feeding (0-35 d). It, however, increased the milk yield during the last phase (36-70 d). The +Zn tended to have lower and greater milk protein yields than CTL during the first and last feeding phases, respectively. The +Zn tended to decrease the SCC and was associated with lower plasma GSH: GSSG and lower serum SOD concentrations relative to CTL. The +Zn did not affect the immunoglobulins, MDA, or CAT. Despite the early DMI and MY reduction, the prolonged Zn-methionine supplementation at about 100 mg/kg of DM improved the milk yield, possibly as a result of the improved udder health of dairy cows.

Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: Bagging, boosting, stacking, and super-learner ensembles versus single machine learning models.

Mastitis has a substantial impact on the dairy industry across the world, causing dairy producers to suffer losses due to the reduced quality and quantity of produced milk. A further problem, related to this issue, is the excessive use of antibiotics that leads to the development of resistance in different bacterial strains. The growing consumer awareness oriented toward food safety and rational use of antibiotics has promoted the search for new methods of early identification of cows that may be at risk of developing the disease. Subclinical mastitis does not cause any visible changes to the udder or milk, and therefore it is more difficult to detect than clinical mastitis. The collection of large amounts of data related to milk performance of cows allows using machine learning (ML) methods to build models that could be used for classifying cows into healthy and at risk of subclinical mastitis. The data used for the purpose of this study included information from routine milk recording procedures. The dataset consisted of 19,856 records of 2,227 Polish Holstein-Friesian cows from 3 herds. The authors decided to use the approach of building ensemble ML models, in particular bagging, boosting, stacking, and super-learner models, and comparing them for accuracy of identification of disease-affected cows against single ML models based on the support vector machines, logistic regression, Gaussian Naive Bayes, k-nearest neighbors, and decision tree algorithms. The models were trained and evaluated based on the information recorded for herd 1 and using an 80:20 train-test split ratio according to animal ID (to avoid data leakage). The information recorded for herds 2 and 3 was only used to evaluate on unseen data models developed using the herd 1 dataset. Among the single ML models, the support vector machines model was found to be the most accurate in predicting subclinical mastitis at subsequent test day when used both for the training set (mean F1-score of 0.760) and the testing sets containing data for herds 1, 2, and 3 (F1-score of 0.778, 0.790, and 0.741 respectively). The gradient boosting model was found to be the best performing model among the ensemble ML models (F1-score of 0.762, 0.779, 0.791, and 0.723 for the training set and the testing sets, respectively). The super-learner model, featuring the most advanced design and logistic regression in the meta layer, achieved the highest mean F1-score of 0.775 during the cross validation; however, it was characterized by a slightly worse prediction accuracy of the testing sets (mean F1-score of 0.768, 0.790, and 0.693 for herds 1, 2 and 3 respectively). The study findings confirm the promising role of ensemble ML methods, which were found to be slightly superior with respect to most of the single ML models.

Economic losses caused by mastitis and the influence of climate variation on the occurrence of the disease in a dairy cattle farm in southern Brazil.

This study evaluated the economic impacts caused by mastitis in a small dairy farm with similar characteristics and production to most dairy farms in southern Brazil and investigated if climatic variations influenced mastitis occurrence in the region. A farm with, on average, 45 lactating Holstein cattle was monitored from November 2021 to October 2022, and data on mastitis cases, bulk tank milk somatic cell count, animal treatment costs, milk production, animal disposal costs, and production losses were collected. Monthly averages of temperature, relative humidity (RH), and rainfall in the region were obtained. The greatest loss was related to the drop in milk production, resulting in 63.8% of total losses, followed by animal disposal (29.5%), milk disposal (4.6%), and treating animals with mastitis (2.0%), totaling a 10.6% reduction in the annual gross income. There were negative correlations between the clinical mastitis rate and monthly RH and between subclinical mastitis and temperature; the occurrence of subclinical mastitis and average RH were positively correlated. Our findings showed that mastitis negatively impacted the economy and that climate influenced mastitis occurrence.

Genetic parameters for novel mastitis traits defined by combining test-day somatic cell score and differential somatic cell count in the first lactation of Japanese Holsteins.

In this study, we aimed to improve current udder health genetic evaluations by addressing the limitations of monthly sampled somatic cell score (SCS) for distinguishing cows with robust innate immunity from those susceptible to chronic infections. The objectives were to (1) establish novel somatic cell traits by integrating SCS and the differential somatic cell count (DSCC), which represents the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells and (2) estimate genetic parameters for the new traits, including their daily heritability and genetic correlations with milk production traits and SCS, using a random regression test-day model (RRTDM). We derived 3 traits, termed ML_SCS_DSCC, SCS_4_DSCC_65_binary, and ML_SCS_DSCC_binary, by using milk loss (ML) estimates at corresponding SCS and DSCC levels, thresholds established in previous studies, and a threshold established from milk loss estimates, respectively. Data consisted of test-day records collected during January 2021 through March 2022 from 265 herds in Hokkaido, Japan. From these records, we extracted records between 7 to 305 d in milk (DIM) in the first lactation to fit the RRTDM. The model included the random effect of herd-test-day, the fixed effect of year-month, fixed lactation curves nested with calving age groups, and random regressions with Legendre polynomials of order 3 for additive genetic and permanent environmental effects. The analysis was performed using Gibbs sampling with Gibbsf90+ software. The averages (ranges) of the daily heritability estimates over lactation were 0.086 (0.075-0.095) for SCS, 0.104 (0.073-0.127) for ML_SCS_DSCC, 0.137 (0.014-0.297) for SCS_4_DSCC_65_binary, and 0.138 (0.115-0.185) for ML_SCS_DSCC_binary; the heritability curve for SCS_4_DSCC_65_binary was erratic. Genetic correlations within the trait decreased as the DIM interval widened, especially for those integrating DSCC, indicating that these traits should be analyzed using RRTDM rather than repeatability models. The averages (ranges) of genetic correlations with milk yield over lactation were 0.01 (-0.22 to 0.28) for SCS, -0.05 (-0.40 to 0.13) for ML_SCS_DSCC, -0.08 (-0.17 to 0.09) for SCS_4_DSCC_65_binary, and -0.08 (-0.22 to 0.27) for ML_SCS_DSCC_binary. Compared with SCS, the newly defined traits exhibited slightly stronger negative genetic correlations with milk yield. Especially in late lactation stages, the genetic correlation between ML_SCS_DSCC and milk yield was significantly below zero, with a posterior median of -0.40. Furthermore, the new traits showed positive correlations with SCS, having estimates varying from 0.68 to 0.85 for ML_SCS_DSCC, 0.14 to 0.47 for SCS_4_DSCC_65_binary, and 0.61 to 0.66 for ML_SCS_DSCC_binary, depending on DIM. Considering that ML_SCS_DSCC and ML_SCS_DSCC_binary have relatively high heritability (compared with SCS) and favorable genetic correlations with milk production traits and SCS, their incorporation into breeding programs appears promising. Nevertheless, their genetic relationships with (sub)clinical mastitis require further investigation.

Exploring the sources of variation of electrical conductivity and total and differential somatic cell count in Italian Mediterranean buffaloes.

In the buffalo dairy sector, a huge effort is still needed to improve mastitis prevention, detection, and management. Electrical conductivity (EC) and total somatic cell count (SCC) are well-known indirect indicators of mastitis. Differential somatic cell count (DSCC), which represents the proportion of neutrophils and lymphocytes on the total SCC, is instead a novel phenotype collected in the dairy cattle sector in the last lustrum. As little is known about this novel trait in dairy buffalo, in the present study we explored the nongenetic factors affecting DSCC, as well as EC and total somatic cell score (SCS), in the Italian Mediterranean buffalo. The data set used for the analysis included 14,571 test-day (TD) records of 1,501 animals from 6 herds, and climatic information of the sampling locations. The original data were filtered to exclude animals with less than 3 TD per lactation and, for the investigated traits, outliers beyond 4 standard deviations. In the statistical model we included the fixed effects of herd (6 classes), days in milk (DIM; 10 classes of 30 d, with the last being an open class until 360 d), parity (6 classes, from 1 to 6+), year-season of calving (11 classes, from summer 2019 to winter 2021/2022), year-season of sampling (9 classes, from spring 2020 to spring 2022), production level (4 classes based on quartiles of average milk yield by herd), and temperature-humidity index (THI; 4 classes based on quartiles, calculated using the average temperature and relative humidity of the 5 d before sampling). Average EC, SCS, and DSCC vary across herds. Considering DIM, greater EC values were observed at the beginning and the end of lactation; SCS was slightly lower, but DSCC was greater around the lactation peak. Increased EC, SCS, and DSCC levels with increasing parity were reported. Year-season calving and year-season sampling only slightly affected the variation of the investigated traits. Milk of high-producing buffaloes was characterized by lower EC and SCS mean values, nevertheless it had slightly greater DSCC percentages. Buffaloes grouped in the highest THI classes (classes 3 and 4) showed, on average, greater EC, SCS, and DSCC in comparison to the lower classes, especially to class 2. Results of the present study represent a preliminary as well as necessary step for the possible future inclusion of EC, SCS, or DSCC in breeding programs aimed to improve mastitis resistance in dairy buffaloes.

Effect of intramammary infection and inflammation on milk protein profile assessed at the quarter level in Holstein cows.

In this study we wanted to investigate the associations between naturally occurring subclinical intramammary infection (IMI) caused by different etiological agents (i.e., Staphylococcus aureus, Streptococcus agalactiae, Streptococcus uberis, and Prototheca spp.), in combination with somatic cell count (SCC), on the detailed milk protein profile measured at the individual mammary gland quarter. An initial bacteriological screening (time 0; T0) conducted on individual composite milk from 450 Holstein cows reared in 3 herds, was performed to identify cows with subclinical IMI. We identified 78 infected animals which were followed up at the quarter level at 2 different sampling times: T1 and T2, 2 and 6 wk after T0, respectively. A total of 529 quarter samples belonging to the previously selected animals were collected at the 2 sampling points and analyzed with a reversed phase HPLC (RP-HPLC) validated method. Specifically, we identified and quantified 4 caseins (CN), namely αS1-CN, αS2-CN, κ-CN, and ß-CN, and 3 whey protein fractions, namely ß-lactoglobulin, α-lactalbumin, and lactoferrin (LF), which were later expressed both quantitatively (g/L) and qualitatively (as a percentage of the total milk nitrogen content, % N). Data were analyzed with a hierarchical linear mixed model with the following fixed effects: days in milk (DIM), parity, herd, SCC, bacteriological status (BACT), and the SCC × BACT interaction. The random effect of individual cow, nested within herd, DIM and parity was used as the error term for the latter effects. Both IMI (i.e., BACT) and SCC significantly reduced the proportion of ß-CN and αS1-CN, ascribed to the increased activity of both milk endogenous and microbial proteases. Less evident alterations were found for whey proteins, except for LF, which being a glycoprotein with direct and undirect antimicrobial activity, increased both with IMI and SCC, suggesting its involvement in the modulation of both the innate and adaptive immune response. Finally, increasing SCC in the positive samples was associated with a more marked reduction of total caseins at T1, and αS1-CN at T2, suggesting a synergic effect of infection and inflammation, more evident at high SCC. In conclusion, our work helps clarify the behavior of protein fractions at quarter level in animals having subclinical IMI. The inflammation status driven by the increase in SCC, rather the infection, was associated with the most significant changes, suggesting that the activity of endogenous proteolytic enzymes related to the onset of inflammation might have a pivotal role in directing the alteration of the milk protein profile.

Intramammary infections in lactating Jersey cows: Prevalence of microbial organisms and association with milk somatic cell count and persistence of infection.

There are limited data available regarding pathogens causing intramammary infections (IMI) in Jersey cows. The objectives of this study were to characterize the prevalence of IMI caused by different microorganisms in lactating Jersey cattle and evaluate the associations among microbes and somatic cell count (SCC) and persistence of IMI. This prospective, observational, longitudinal study included lactating Jersey cows (n = 753) from 4 farms within a 415 km radius of Columbia, Missouri. Quarter foremilk samples were aseptically collected monthly for 3 consecutive months. Microorganisms were identified using aerobic milk culture and MALDI-TOF mass spectrometry. A commercial laboratory measured SCC using flow cytometry. Milk culture results were used to classify single microorganism infections as persistent (same microorganism species identified at first sampling and one other sampling) or nonpersistent infection. Mixed models were built to evaluate the associations between IMI status and SCC natural logarithm (lnSCC), as well as persistence and lnSCC. Overall, staphylococci were the most commonly isolated microorganisms among the 7,370 quarter-level milk samples collected. Median prevalence (using all 3 samplings) of specific microbes varied among farms; however, Staphylococcus chromogenes was a common species found at all farms. The most common microbial species that persisted were Staph. chromogenes, Staphylococcus aureus, Staphylococcus simulans, and Streptococcus uberis. Streptococcus dysgalactiae and Staph. aureus were the IMI associated with the most inflammation based on lnSCC. The small number of herds included in this study with the large variation in herd type limits the generalizability of the data. However, results of this study seem to be similar to those of previous studies in other breeds, suggesting management factors are more important than breed-specific differences when evaluating causes of IMI and associated subclinical mastitis.

Milk as diagnostic fluid for udder health management.

BACKGROUND: Mastitis is the major disease affecting milk production of dairy cattle, and milk is an obvious substrate for the detection of both the inflammation and its causative infectious agents at quarter, cow, or herd levels. In this review, we examine the use of milk to detect inflammation based on somatic cell count (SCC) and other biomarkers, and for the detection of mastitis pathogens through culture-based and culture-free methods. FINDINGS: The use of SCC at a cow or bulk milk level to guide udder health management in lactation is well-established, and SCC is increasingly used to guide selective dry cow treatment. Other markers of inflammation include electrical conductivity, which is used commercially, and markers of disease severity such as acute phase proteins but are not pathogen-specific. Some pathogen-specific markers based on humoral immune responses are available, but their value in udder health management is largely untested. Commercial pathogen detection is based on culture or polymerase chain reaction, with other tests, for example, loop-mediated isothermal amplification or 16S microbiome analysis still at the research or development stage. Matrix-assisted laser desorption ionisation time of flight (MALDI-ToF) is increasingly used for the identification of cultured organisms whilst application directly to milk needs further development. Details of test sensitivity, specificity, and use of the various technologies may differ between quarter, cow, and bulk milk applications. CONCLUSIONS: There is a growing array of diagnostic assays that can be used to detect markers of inflammation or infection in milk. The value of some of these methods in on-farm udder health improvement programs is yet to be demonstrated whilst methods with proven value may be underutilised.

Novel insights into the associations between immune cell population distribution in mammary glands and milk minerals in Holstein cows.

Udder health has a crucial role in sustainable milk production, and various reports have pointed out that changes in udder condition seem to affect milk mineral content. The somatic cell count (SCC) is the most recognized indicator for the determination of udder health status. Recently, a new parameter, the differential somatic cell count (DSCC), has been proposed for a more detailed evaluation of intramammary infection patterns. Specifically, the DSCC is the combined proportions of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) on the total SCC, with macrophages (MAC) representing the remainder proportion. In this study, we evaluated the association between DSCC in combination with SCC on a detailed milk mineral profile in 1,013 Holstein-Friesian cows reared in 5 herds. An inductively coupled plasma-optical emission spectrometry was used to quantify 32 milk mineral elements. Two different linear mixed models were fitted to explore the associations between the milk mineral elements and first, the DSCC combined with SCC, and second, DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the proportion of PMN-LYM and MAC by SCC. We observed a significant positive association between SCC and milk Na, S, and Fe levels. Differential somatic cell count showed an opposite behavior to the one displayed by SCC, with a negative association with Na and positive association with K milk concentrations. When considering DSCC as count, Na and K showed contrasting behavior when associated with PMN-LYM or MAC counts, with decreasing of Na content and increasing K when associated with increasing PMN-LYM counts, and increasing Na and decreasing K when associated with increasing MAC count. These findings confirmed that an increase in SCC is associated with altered milk Na and K amounts. Moreover, MAC count seemed to mirror SCC patterns, with the worsening of inflammation. Differently, PMN-LYM count exhibited patterns of associations with milk Na and K contents attributable more to LYM than PMN, given the nonpathological condition of the majority of the investigated population. An interesting association was observed for milk S content, which increased with increasing of inflammatory conditions (i.e., increased SCC and MAC count) probably attributable to its relationship with milk proteins, especially whey proteins. Moreover, milk Fe content showed positive associations with the PMN-LYM population, highlighting its role in immune regulation during inflammation. Further studies including individuals with clinical condition are needed to achieve a comprehensive view of milk mineral behavior during udder health impairment.

Pathogen and severity-dependent immune responses in bovine mastitis: highlight the dynamics of differential somatic cell count.

Immune responses in bovine clinical mastitis (CM) probably differ depending on the causative pathogen and disease severity. The observational study aimed to investigate whether both factors are associated with the dynamics of immune indicators, including somatic cell score (SCS), white blood cell count (WBC), serum albumin/globulin (A/G) ratio, and differential somatic cell count (DSCC). We collected blood and milk samples 0, 3, 5, 7, 14, and 21 days after CM occurred in 38 cows, and grouped the cases (n=49) by disease severity and pathogen. We analyzed data using a linear mixed model considering the effects of pathogens and severity, calculated estimated-marginal means for indicators at each time point, and compared the means between groups. The dynamics of WBC varied depending on both pathogen and severity. WBC changed drastically in either severe or coliform-caused CM, slightly elevated in streptococcal mastitis, but unchanged in staphylococcal mastitis. This possibly relates to the deficiency in innate immune response toward staphylococci. The A/G ratio also changed depending on severity, as it dropped sharply only in severe CM. We observed a non-linear relationship between DSCC and SCS, possibly due to mammary epithelial cells shedding in milk when CM occurred. When cows recovering from Streptococcus dysgalatiae mastitis, DSCC decreased while SCS remained high, suggesting a healing process requiring more macrophages. Our results demonstrate that both the severity and pathogen are associated with immune responses in CM, providing insights into mastitis pathogenesis.