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.

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.

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.

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.

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 from 2020 to 2021, once from October to December and once from March to May. Dairy barns were observed, animal welfare was assessed, and freestall 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 from October to March. The prevalence of claw disorders and abnormal claw conformation were collected from the national claw health database for the period from October to May. On each farm visit, composite samples of unused bedding outside the barn and used bedding material from the freestalls, respectively, were taken for total bacterial count and DM 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 vs. 3.89 log10 cfu/mL) or SCC (median 243,800 vs. 229,200 cells/mL). The aspects of animal welfare assessed did not differ significantly between the 2 bedding systems, whereas the prevalence of total claw disorders (25.9% vs. 38.0% of trimmed cows), dermatitis (6.9% vs. 16.2% of trimmed cows) and sole ulcers (2.0% vs. 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.

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

Total bacterial count (TBC) and 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 yr. Second, to explore causes of these elevations, we studied the variation of TBC and SCC in individual goat milk from 3 wk before to 5 wk after kidding and their association with systemic response markers IFN-γ, calprotectin, BHB, BCS, and fecal consistency. We visited 4 Dutch dairy goat farms weekly for 10 to 16 wk 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 flow cytometric 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 readout for general immunity status, intestinal health, and metabolic diseases. Therefore, we assume that the TBC increase before kidding and the decrease after parturition are caused by other systemic, possibly hormonal, processes. To reduce BMTBC and bulk milk SCC, 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.

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.

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.

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.

Biochemical and Computational Assessment of Acute Phase Proteins in Dairy Cows Affected with Subclinical Mastitis.

Subclinical mastitis (SCM) is a predominant form of mastitis wherein major visible signs of disease are absent. The present study aimed to determine acute phase proteins (APPs) like ferritin, C-reactive protein (CRP), and microalbumin (Malb) in 135 composite milk and serum samples of healthy (n = 25) and SCM (n = 110) cows. As bovine mastitis is an inflammatory disease, the present study also aimed at finding novel anti-inflammatory compounds from natural sources by repurposing approach using computational studies. The findings of the present study revealed substantial elevation (p < 0.001) in milk SCC and an increase in ferritin, CRP, and Malb (p < 0.001) in milk and sera of the SCM group as compared to healthy animals. Receiver operating characteristics of milk SCC, milk, and serum APPs unraveled statistically substantial alteration (p < 0.001). Further, SCC was correlated with milk APPs ferritin (r = 0.26 **, p < 0.002), CRP (r = 0.19 *, p < 0.02), and Malb (r = 0.21 *, p < 0.01). Additionally, milk SCC was correlated with serum ferritin (r = 0.28 **, p < 0.001), CRP (r = 0.16, p > 0.05), and Malb (r = 0.16, p > 0.05). The findings of molecular docking revealed that Chaetoglobosin U was the most effective molecule that showed the highest binding affinity (kcal/mol) of -10.1 and -8.5 against ferritin and albumin. The present study concluded that the estimation of cow-side tests, SCC, and APPs in milk/serum is suitable to detect SCM and screening herd community. Furthermore, Chaetoglobosin U could be developed as a promising anti-inflammatory inhibitor; however, further studies are required to validate these findings.

Comparative study between efficacy of dexamethasone-prostaglandin-receptal combination and mechanical correction in uterine torsion cases in Egyptian buffalo-cows (Bubalus bubalis).

BACKGROUND: According to reports, the majority of domesticated species exhibited uterine torsion. It was occasionally noted as a cause of dystocia in buffaloes. The uterus might twist more frequently late in pregnancy because of certain animal traits. The current research monitored the clinical findings and laboratory assays associated with uterine torsion cases in pregnant buffalo-cows through comparing between normal labored buffalo-cows (Norm-Labgr; n = 20), mechanically corrected uterine torsed animals without medicament interference (UtrTorsgr; n = 160), and mechanically corrected uterine torsed animals with medicament interference (UtrTors-Medgr; n = 40) through focusing on placental characterization, calves body weight, milk constituents and milk somatic cell count (SCC) in normal labored buffaloes and uterine torsed ones. Through clinical and laboratory investigations of these buffaloes (N = 220) had been conducted 3 times; 7 h pre-calving and post calving (Post uterine correction) i.e. 48 and 96 h. Uterine torsion prevalence parameters, placental characterization, calves body weight, milk constituents and milk somatic cell counts were evaluated in normal labored buffaloes and uterine torsed ones. RESULTS AND CONCLUSIONS: The study concluded pre-calving remarkable variations in clinical findings, leukogram picture, calf birth weight and some placental characterization parameters between Norm-Labgr and each of UtrTorsgr and UtrTors-Medgr whereas these variations disappeared post-partum as a result to either only mechanical correction or mechanical correction plus medicaments interference. No pre-or post-calving significant changes between UtrTorsgr and UtrTors-Medgr except for the abnormal clinical findings were more representative in UtrTors-Medgr than those in UtrTorsgr particularly pre-calving. The applied pre-calving therapeutic regimen including dexamethasone-prostaglandin-receptal combination had a powerful potential efficacy that induced vaginal delivery of calves in UtrTors-Medgr as well as prepartum mechanical correction of torsed uterus approved higher efficacy in UtrTorsgr. The applied prepartum mechanical correction of torsed uterus and/or pre-calving therapeutic regimen as well as subsequent post-calving, post uterine correction applied medicament treatment accelerated rapid recovery of affected buffalo-cows through achieving rapid restoring of their physiological parameters. Buffalo-cow's milk composition, milk pH and milk SCC were not affected whereas no significant variations were reported between Norm-Labgr, UtrTorsgr and UtrTors-Medgr.

Early detection of subclinical mastitis in lactating dairy cows using cow-level features.

Subclinical mastitis in cows affects their health, well-being, longevity, and performance, leading to reduced productivity and profit. Early prediction of subclinical mastitis can enable dairy farmers to perform interventions to mitigate its effect. The present study investigated how well predictive models built using machine learning techniques can detect subclinical mastitis up to 7 d before its occurrence. The data set used consisted of 1,346,207 milk-day (i.e., a day when milk was collected on both morning and evening) records spanning 9 yr from 2,389 cows producing on 7 Irish research farms. Individual cow composite milk yield and maximum milk flow were available twice daily, whereas milk composition (i.e., fat, lactose, protein) and somatic cell count (SCC) were collected once per week. Other features describing parity, calving dates, predicted transmitting ability for SCC, body weight, and history of subclinical mastitis were also available. The results of the study showed that a gradient boosting machine model trained to predict the onset of subclinical mastitis 7 d before a subclinical case occurs achieved a sensitivity and specificity of 69.45 and 95.64%, respectively. Reduced data collection frequency, where milk composition and SCC were recorded only every 15, 30, 45, and 60 d was simulated by masking data, to reflect the frequency of recording of this data on commercial dairy farms in Ireland. The sensitivity and specificity scores reduced as recording frequency reduced with respective scores of 66.93 and 80.43% when milk composition and SCC were recorded just every 60 d. Results demonstrate that models built on data that could be recorded routinely available on commercial dairy farms, can achieve useful predictive ability of subclinical mastitis even with reduced frequency of milk composition and SCC recording.

The effect of antibiotic versus no treatment at dry-off on udder health and milk yield in subsequent lactation: A retrospective analysis of Austrian health recording data from dairy herds.

Bovine mastitis is the most commonly diagnosed disease of dairy cows worldwide and causes extensive economic losses to milk producers. Intramammary infection status before dry-off plays a decisive role with respect to udder health and milk yield in the subsequent lactation. The aim of this study was to compare the effect of antibiotic dry cow therapy (DCT) versus no treatment at dry-off on milk yield, somatic cell count (SCC), inflammation of the mammary gland (IMG), and the incidence of clinical mastitis in the subsequent lactation. Dairy herd data from 251 Austrian dairy farms were recorded over an observation period of 12 mo and subsequently analyzed. The data set included 5,018 dairy cows: 2,078 were treated with antibiotics (abDCT group) and 2,940 were not treated (noDCT group) at dry-off. The abDCT group was subdivided, based on the antimicrobial active substances used for drying off, into 4 different groups (penicillins, cloxacillin, cephalosporins, and rifaximin). Based on bacteriological culture results, infections were grouped into those caused by major, minor, and other pathogens. Additionally, the IMG was defined via SCC from milk recording data using a cutoff of 200,000 cells/mL before drying off and after calving. The incidence of clinical mastitis cases within 30 and 90 d in milk was calculated using veterinary diagnosis data. To investigate the effect of different dry cow therapies on the following parameters: milk yield, SCC, and diagnosed clinical mastitis cases, different linear mixed models were constructed. Overall, the abDCT group was determined to have a significantly higher milk yield over 305 d in milk in the subsequent lactation (increase of 6.18%), compared with the noDCT group (increase of 4.29%). Both groups (abDCT and noDCT) demonstrated a decrease in the first SCC after calving compared with the SCC before dry-off, although the treated cows had a significantly higher reduction. Regarding the different antibiotic groups, with exception of the rifaximin treated cows, all antibiotic groups showed a significant difference from not treated cows with respect to SCC. Additionally, we were able to demonstrate that cows with IMG before dry-off had a 2.073 times higher chance of an increased SCC (>200,000 cells/mL) after calving. With respect to the veterinary diagnosis data, neither the IMG before drying off nor the type of DCT had a significant influence on the probability of developing clinical mastitis within 30 or 90 d in milk. Only a small number of treatments was accompanied with a bacteriological examination before drying off. However, the existing data in this study indicates that the intramammary infection status before dry-off in combination with different dry cow treatments influences udder health and milk yield after calving. Nevertheless, further studies with larger data sets of bacteriological examinations are necessary to enable a more in-depth investigation into the effects of different antibiotic substances used for DCT.

Efficacy of feeding hydroxy-selenomethionine on plasma and milk selenium in mid-lactation dairy cows.

In this study, we aimed to determine the amount of Se transferred to milk and blood of mid- to late-lactation dairy cows when supplemental Se from hydroxy-selenomethionine (OH-SeMet) was fed compared with an unsupplemented group and a group supplemented with a seleno-yeast (SY). Twenty-four lactating Holstein cows (178 ± 43 d in milk) were used in a complete randomized block design for 91 d (7-d covariate period and 84-d treatment period). Treatments were (1) basal diet with an analyzed Se background of 0.2 mg of Se per kg as-fed (control); (2) basal diet + 0.3 mg of Se/kg as-fed from SY (SY-0.3); (3) basal diet + 0.1 mg of Se/kg as-fed from OH-SeMet (OH-SeMet-0.1); and (4) basal diet + 0.3 mg of Se/kg as-fed from OH-SeMet (OH-SeMet-0.3). During the trial, plasma and milk were analyzed for total Se, and plasma was analyzed for glutathione peroxidase activity. The mean plasma and milk Se concentrations exhibited the same relationship, where OH-SeMet-0.3 resulted in the highest values (142 µg/L of plasma and 104 µg/kg of milk), followed by SY-0.3 (134 µg/L and 85 µg/kg), OH-SeMet-0.1 (122 µg/L and 67 µg/kg), and the control group had the lowest values (120 µg/L and 50 µg/kg). The increment of Se in milk induced by OH-SeMet-0.3 (+54 µg/kg) was 54% higher than that induced by SY-0.3 (+35 µg/kg). Additionally, dietary supplementation of 0.2 mg/kg Se from OH-SeMet in the total mixed ration was estimated to be similar to 0.3 mg/kg Se from SY in the total mixed ration when considering the level of Se in the milk. There was no difference in plasma glutathione peroxidase activity between groups; however, OH-SeMet-0.3 significantly decreased somatic cell count. The results confirmed that supplementation with organic Se increases milk and plasma Se concentrations. Moreover, when administered at the same level of supplementation, OH-SeMet was shown to be more efficient than SY in improving milk quality by increasing Se content and decreasing milk somatic cell count.

Blood biochemical changes upon subclinical intramammary infection and inflammation in Holstein cattle.

The aim of this study was to investigate the associations between subclinical intramammary infection (IMI) from different pathogens combined with inflammation status and a set of blood biochemical traits including energy-related metabolites, indicators of liver function or hepatic damage, oxidative stress, inflammation, innate immunity, and mineral status in 349 lactating Holstein cows. Data were analyzed with a linear model including the following fixed class effects: days in milk, parity, herd, somatic cell count (SCC), bacteriological status (positive and negative), and the SCC × bacteriological status interaction. Several metabolites had significant associations with subclinical IMI or SCC. Increased SCC was associated with a linear decrease in cholesterol concentrations which ranged from -2% for the class ≥50,000 and <200,000 cells/mL to -11% for the SCC class ≥400,000 cells/mL compared with the SCC class <50,000 cells/mL. A positive bacteriological result was associated with an increase in bilirubin (+24%), paraoxonase (+11%), the ratio paraoxonase/cholesterol (+9%), and advanced oxidation protein product concentration (+23%). Increased SCC were associated with a linear decrease in ferric reducing antioxidant power concentrations ranging from -3% for the class ≥50,000 and <200,000 cells/mL to -9% for the SCC class ≥400,000 cells/mL (respect to the SCC class <50,000 cells/mL). A positive bacteriological result was associated with an increase in haptoglobin concentrations (+19%). Increased SCC were also associated with a linear increase in haptoglobin concentrations, which ranged from +24% for the class ≥50,000 and <200,000 cells/mL (0.31 g/L) to +82% for the SCC class ≥400,000 cells/mL (0.45 g/L), with respect to the SCC class <50,000 cells/mL (0.25 g/L). Increased SCC were associated with a linear increase in ceruloplasmin concentrations (+15% for SCC ≥50,000 cells/mL). The observed changes in blood biochemical markers, mainly acute phase proteins and oxidative stress markers, suggest that cows with subclinical IMI may experience a systemic involvement.

Estimating the nonlinear interaction between somatic cell score and differential somatic cell count on milk production by parity using generalized additive models.

This observational study aimed to use somatic cell score (SCS) and differential somatic cell count (DSCC), the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells, to investigate how mastitis affected milk production. Using generalized additive models, we analyzed 50,618 test-day records from 8,081 lactations from 7,912 cows in 197 herds between January 2021 and March 2022 to estimate the nonlinear interaction between SCS and DSCC, and the effects of lactation stages and seasons on milk yield, milk component percentages, and milk component yields by parity of cows. The results show that the interaction between SCS and DSCC on these traits was significant, nonlinear, and complex. When DSCC was high, the negative effects of SCS were minimal, even when SCS reached 8 (i.e., 3,200,000 somatic cells/mL). Cows with high DSCC could have milk yields similar to healthy cows, implying that these cows may have been in the early stages of mastitis and that the milk yield had yet to be affected. Contrastingly, when DSCC was low, milk loss due to high SCS was drastic, especially for cows in third or later lactations, whose milk yield could reduce from more than 35 kg/d to less than 15 kg/d (-59.9%). This tremendous milk loss in high-parity cows was likely due to their higher milk yield and higher risks of chronic mastitis. High SCS and low DSCC also led to a pronounced change in milk composition. The decrease in the percentage of lactose can be directly related to the damage of inflammation to the mammary gland, while the increase in fat and protein percentages was more attributable to the concentration effect resulting from the reduced milk yield. Compared with analyses based on categorized SCS and DSCC values, modeling these 2 indices directly helps us more precisely assess mastitis effects on milk yield and milk composition. For efficient milk production, our results indicate that we should prevent high-parity cows from entering a state of high SCS and low DSCC.

Heritability and genetic correlations of total and differential somatic cell count with milk yield and composition traits in Italian Simmental cows.

Costs of production have deeply increased each year in the last decades, breeders are continuously looking for more cost effective and more efficient ways to produce milk. Despite the major signs of progress in productivity, it is fundamental to optimize rather than maximize the performances of the dairy cows. Mastitis is still a highly prevalent disease in the dairy sector which causes several economic losses and environmental effect. Its accurate and early diagnosis is crucial to improve profitability of dairy cows and contribute to a more sustainable dairy industry. Among mastitis reduction strategies, there is the urgent need to implement breeding objectives to select cows displaying mastitis resistance by investigating the genetic mechanisms at the base of the inflammatory response. Therefore, in this study we aimed to further understand the genetic background of the differential somatic cell count (DSCC), which provides thorough insights on the actual inflammatory status of the mammary glands. The objectives of this study were to estimate on a cohort of 20,215 Italian Simmental cows over a 3-yr period: (1) the heritability and repeatability values of somatic cell score (SCS) and DSCC, (2) the genetic and phenotypic correlations between these 2 traits and milk production and milk composition traits, (3) the heritability and repeatability values of SCS and DSCC within class of udder health status. Heritability was low both for SCS (0.06) and DSCC (0.08), whereas the repeatability values for these traits were 0.43 and 0.36, suggesting that the magnitude of cow permanent environmental effect for these traits is remarkable. The genetic and phenotypic correlation of SCS with DSCC was 0.612 and 0.605, respectively. Because both significantly differed from the unit, we must consider those traits as different ones. This latter aspect corroborates the need to consider the DSCC as a further indicator of inflammatory status which might be implemented in the Simmental breed genetic evaluation. It is worthy to mention that heritability estimates for SCS and DSCC were the highest in healthy cows compared with the other udder health classes. This implies that when the udder health status changes, it is most likely due to environmental factors rather than aspects related to the animal's genetics. In contrast, the highest additive genetic variance and heritability found for SCS and DSCC in the healthy group might reveal the potential to further implement breeding strategies to select for healthier animals.

Evaluation of test-day milk somatic cell count to predict intramammary infection in late lactation grazing dairy cows.

Use of selective dry cow antimicrobial therapy requires to precisely differentiate cows with an intramammary infection (IMI) from uninfected cows close to drying-off to enable treatment allocation. Milk somatic cell count (SCC) is an indicator of an inflammatory response in the mammary gland and is usually associated with IMI. However, SCC can also be influenced by cow-level variables such as milk yield, lactation number and stage of lactation. In recent years, predictive algorithms have been developed to differentiate cows with IMI from cows without IMI based on SCC data. The objective of this observational study was to explore the association between SCC and subclinical IMI, taking cognizance of cow-level predictors on Irish seasonal spring calving, pasture-based systems. Additionally, the optimal test-day SCC cut-point (maximized sensitivity and specificity) for IMI diagnosis was determined. A total of 2,074 cows, across 21 spring calving dairy herds with an average monthly milk weighted bulk tank SCC of ≤200,000 cells/mL were enrolled in the study. Quarter-level milk sampling was carried out on all cows in late lactation (interquartile range = 240-261 d in milk) for bacteriological culturing. Bacteriological results were used to define cows with IMI, when ≥1 quarter sample resulted in bacterial growth. Cow-level test-day SCC records were provided by the herd owners. The ability of the average, maximum and last test-day SCC to predict infection were compared using receiver operator curves. Predictive logistic regression models tested included parity (primiparous or multiparous), yield at last test-day and a standardized count of high SCC test-days. In total, 18.7% of cows were classified as having an IMI, with first parity cows having a higher proportion of IMI (29.3%) compared with multiparous cows (16.1%). Staphylococcus aureus accounted for the majority of these infections. The last test-day SCC was the best predictor of infection with the highest area under the curve. The inclusions of parity, yield at last test-day, and a standardized count of high SCC test-days as predictors did not significantly improve the ability of last test-day SCC to predict IMI. The cut-point for last test-day SCC which maximized sensitivity and specificity was 64,975 cells/mL. This study indicates that in Irish seasonal pasture-based dairy herds, with low bulk tank SCC control programs, the last test-day SCC (interquartile range days in milk = 221-240) is the best predictor of IMI in late lactation.

Pathogens in milk of goats and their relationship with somatic cell count.

We evaluated the presence of bacterial pathogens in the milk of goats and their relationship with somatic cell count (SCC) and milk composition. The study was performed on a dairy farm in northern Slovakia. Half udder milk samples were collected from goats in June and July. The samples were divided on the basis of SCC into 4 bands (SCC1 lowest to SCC4 highest). Bacterial pathogens were only detected in 13% of samples. SCC3 and SCC4 had 15 and 25% positive samples respectively compared with SCC1 (2%) and SCC2 (14%). Coagulase-negative staphylococci (CNS) were the most common isolates (73%), of which Staphylococcus caprae was the most frequently isolated (65%). In samples with ≥ 1000 × 103 cells ml- 1 (SCC3, SCC4) there was higher somatic cell score (SCS) in the presence of a pathogen (7.48 ± 0.11) than without a pathogen (7.16 ± 0.05, P < 0.01). Statistically significant but weak negative correlations were observed between SCS and lactose, dry matter and non-fat dry matter. In conclusion, a higher percentage of bacteriologically positive milk samples was observed in both SCC3 and SCC4 groups but this does not explain the aetiology of high SCC in the milk of goats that are apparently free of bacteria. As a diagnostic tool, SCC is probably less useful in goats than in cows.