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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. [ABSTRACT FROM AUTHOR]

ABSTRACT: 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.

ABSTRACT: 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.

ABSTRACT: 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.

ABSTRACT: 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.

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. [ABSTRACT FROM AUTHOR]

Simple Summary: To determine the use of differential somatic cell count as an indicator of mammary health in sheep, the receiver-operating characteristic curve analysis approach was used to find a threshold value that can be used to discriminate healthy animals from those with a probable intramammary infection. This threshold will enhance the precision of diagnosing mammary gland inflammation in dairy sheep. Differential somatic cell count (DSCC), the percentage of somatic cell count (SCC) due to polymorphonuclear leukocytes (PMNs) and lymphocytes (LYMs), is a promising effective diagnostic marker for dairy animals with infected mammary glands. Well-explored in dairy cows, DSCC is also potentially valid in sheep, where clinical and subclinical mastitis outbreaks are among the principal causes of culling. We pioneered the application of DSCC in dairy ewes by applying receiver-operating characteristic (ROC) curve analysis to define the most accurate thresholds to facilitate early discrimination of sheep with potential intramammary infection (IMI) from healthy animals. We tested four predefined SCC cut-offs established in previous research. Specifically, we applied SCC cut-offs of 265 × 103 cells/mL, 500 × 103 cells/mL, 645 × 103 cells/mL, and 1000 × 103 cells/mL. The performance of DSCC as a diagnostic test was assessed by examining sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) analyses. The designated threshold value for DSCC in the detection of subclinical mastitis is established at 79.8%. This threshold exhibits Se and Sp of 0.84 and 0.81, accompanied by an AUC of 0.88. This study represents the inaugural exploration of the potential use of DSCC in sheep's milk as an early indicator of udder inflammation. [ABSTRACT FROM AUTHOR]

The aim of this study was to establish and evaluate a structural equation model to infer causal relationships among environmental and genetic factors on udder health. For this purpose, 537 Holstein Friesian cows were genotyped, and milk samples were analyzed for novel traits including differential somatic cell counts and specific mastitis pathogens. In the structural model, four latent variables (intramammary infection (IMI), production, time and genetics) were defined, which were explained using manifest measurable variables. The measurable variables included udder pathogens and somatic differential cell counts, milk composition, as well as significant SNP markers from previous genome-wide associations for major and minor pathogens. The housing system effect (i.e., compost-bedded pack barns versus cubicle barns) indicated a small influence on IMI with a path coefficient of −0.05. However, housing system significantly affected production (0.37), with ongoing causal effects on IMI (0.17). Thus, indirect associations between housing and udder health could be inferred via structural equation modeling. Furthermore, genotype by environment interactions on IMI can be represented, i.e., the detection of specific latent variables such as significant SNP markers only for specific housing systems. For the latent variable genetics, especially one SNP is of primary interest. This SNP is located in the EVA1A gene, which plays a fundamental role in the MAPK1 signaling pathway. Other identified genes (e.g., CTNNA3 and CHL1) support results from previous studies, and this gene also contributes to mechanisms of the MAPK1 signaling pathway. [ABSTRACT FROM AUTHOR]

Abstract Background Mammary health is important for transition dairy cows and has been well recognized to exert decisive effects on animal welfare. However, the factors influencing mammary health are still unclear. Differential somatic cell count (DSCC) could reflect the mastitis risk since it is the percentage of neutrophils plus lymphocytes in total somatic cells and could be reflective of mammary health of dairy cows. This work aimed to investigate the assessment and prognosis of the health of transition cows based on blood neutrophil extracellular traps (NETs). Results Eighty-four transition Holstein dairy cows were selected. The serum was sampled in all the animals at week 1 pre- and postpartum, and milk was sampled at week 1 postpartum. Based on the DSCC in milk at week 1, cows with lower (7.4% ± 4.07%, n = 15) and higher (83.3% ± 1.21%, n = 15) DSCCs were selected. High DSCC cows had higher levels of red blood cell counts (P

ABSTRACT: Udder health in dairy herds is a very important issue given its implications for animal welfare and the production of high-quality milk. Somatic cell count (SCC) is the most widely used means of assessing udder health status. However, differential somatic cell count (DSCC) has recently been proposed as a new and more effective means of evaluating intramammary infection dynamics. Differential SCC represents the combined percentage of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) in the total SCC, with macrophages (MAC) accounting for the remaining proportion. The aim of this study was to evaluate the association between SCC and DSCC and the detailed milk protein profile in a population of 1,482 Holstein cows. A validated reversed-phase HPLC method was used to quantify 4 caseins (CN), namely αS1-CN, αS2-CN, κ-CN, and β-CN, and 3 whey protein fractions, namely β-lactoglobulin, α-lactalbumin, and lactoferrin, which were expressed both quantitatively (g/L) and qualitatively (as a percentage of the total milk nitrogen content, %N). A linear mixed model was fitted to explore the associations between somatic cell score (SCS) combined with DSCC and the protein fractions expressed quantitatively and qualitatively. We ran an additional model that included DSCC expressed as PMN-LYM and MAC counts, obtained by multiplying the percentages of PMN-LYM and MAC by SCC for each cow in the data set. When the protein fractions were expressed as grams per liter, SCS was significantly negatively associated with almost all the casein fractions and positively associated with the whey protein α-lactalbumin, while DSCC was significantly associated with αS1-CN, β-CN, and α-lactalbumin, but in the opposite direction to SCS. We observed the same pattern with the qualitative data (i.e., %N), confirming opposite effects of SCS and DSCC on milk protein fractions. The PMN-LYM count was only slightly associated with the traits of concern, although the pattern observed was the same as when both SCS and DSCC were included in the model. The MAC count, however, generally had a greater impact on many casein fractions, in particular decreasing both β-CN content (g/L) and proportion (%N), and exhibited the opposite pattern to the PMN-LYM count. Our results show that information obtained from both SCS and DSCC may be useful in assessing milk quality and protein fractions. They also demonstrate the potential of MAC count as a novel udder health trait.

The milk differential somatic cell count (DSCC) has been proposed in recent years as a mean by which to better monitor the udder health status (UHS) in dairy cows. Milk DSCC is the amount of polymorphonuclear neutrophils and lymphocytes contributing to the total somatic cell count (SCC) and can be determined on a routine basis in individual milk samples subjected to official analysis. In the present study, 522 865 milk test-day records of 77 143 cows were scrutinised to identify factors affecting the variability of both DSCC and SCC in Holstein Friesian, Jersey, Simmental and Rendena cows through linear mixed models. The fixed effects were breed, parity, lactation stage, sampling season, and all the first-order interactions of breed. Cow and herd‐test‐date were considered as random. Subsequently, four UHS groups were created (1: SCC ≤ 200 000 cells/mL and DSCC ≤ 65%; 2: SCC ≤ 200 000 cells/mL and DSCC > 65%; 3: SCC > 200 000 cells/mL and DSCC > 65%; 4: SCC > 200 000 cells/mL and DSCC ≤ 65%) to compare milk yield and quality. Milk SCS and DSCC differed across lactation, parity, sampling season and breed. In particular, Simmental cows had the lowest SCC and Jersey the lowest DSCC. Depending on the breed, UHS affected daily milk yield and composition to a different extent. The UHS group 4, i.e. the one grouping test-day records with high SCC and low DSCC, presented the lowest estimate of milk yield and lactose content no matter the breeds. Our findings support that udder health-related traits (SCS and DSCC) are useful information to improve udder health at individual cow and herd levels. Moreover, the combination of SCS and DSCC is useful to monitor milk yield and composition.

Background: Mammary health is important for transition dairy cows and has been well recognized to exert decisive effects on animal welfare. However, the factors influencing mammary health are still unclear. Differential somatic cell count (DSCC) could reflect the mastitis risk since it is the percentage of neutrophils plus lymphocytes in total somatic cells and could be reflective of mammary health of dairy cows. This work aimed to investigate the assessment and prognosis of the health of transition cows based on blood neutrophil extracellular traps (NETs). Results: Eighty-four transition Holstein dairy cows were selected. The serum was sampled in all the animals at week 1 pre- and postpartum, and milk was sampled at week 1 postpartum. Based on the DSCC in milk at week 1, cows with lower (7.4% ± 4.07%, n = 15) and higher (83.3% ± 1.21%, n = 15) DSCCs were selected. High DSCC cows had higher levels of red blood cell counts (P < 0.05), hemoglobin (P = 0.07), and hematocrit (P = 0.05), higher concentrations of serum oxidative variables [(reactive oxygen species (P < 0.05), malondialdehyde (P < 0.05), protein carbonyl (P < 0.05), and 8-hydroxy-2-deoxyguanosine (P = 0.07)], higher levels of serum and milk NETs (P < 0.05) and blood-milk barrier indicators, including serum β-casein (P = 0.05) and milk immunoglobulin G2 (P = 0.09), than those of low DSCC cows. In addition, lower concentrations of serum nutrient metabolites (cholesterol and albumin) (P < 0.05) and a lower level of serum deoxyribonuclease I (P = 0.09) were observed in high DSCC cows than in low DSCC cows. Among the assessments performed using levels of the three prepartum serum parameters (NETs, deoxyribonuclease I and β-casein), the area under the curve (0.973) of NETs was the highest. In addition, the sensitivity (1.00) and specificity (0.93) were observed for the discrimination of these cows using NETs levels with a critical value of 32.2 ng/mL (P < 0.05). Conclusions: The formation of NETs in blood in transition dairy cows may damage the integrity of the blood-milk barrier and thereby increase the risk for mastitis in postpartum cows. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effect of interaction between total (SCC) and differential somatic cell count (DSCC) on milk yield, composition and coagulation traits from individual dairy cows. A total of 159 224 test‐day records (daily milk yield, composition and coagulation traits) have been collected during a period of 14 months from 12 849 Holstein‐Friesian and 9275 Simmental cows farmed in 223 herds. The interaction between somatic cell traits was highly significant for almost all the evaluated traits. This study showed that the combined use of SCC and DSCC can be employed for assessing the performances of dairy cows and for monitoring the improvement of milk quality. [ABSTRACT FROM AUTHOR]

The combined use of somatic cell count (SCC) and differential somatic cell count (DSCC), which is the ratio of neutrophils plus lymphocytes to total milk SCC, represents a novel approach to define cow’s udder health status, as it allows to identify healthy animals (those with low SCC and DSCC), cows susceptible to mastitis (those where an immune response has begun, so that there is an increase of neutrophils, i.e. DSCC, but not yet of total SCC), animals with a mastitic event in progress (those with high SCC and DSCC) and animals with possible chronic inflammation (those with high SCC and low DSCC, as macrophages prevail). We investigated the association of cow’s udder health status with milk yield, composition and coagulation properties in four cattle breeds. Results demonstrated that milk traits vary among cows with different udder health status, especially in terms of fat percentage, lactose percentage and coagulation ability. The most pronounced worsening in milk yield and coagulation ability was observed for animals with chronic inflammation. Our findings support the new approach based on the combined use of SCC and DSCC to screen for cow’s udder health, as it would allow to identify susceptible cows that will probably undergo a mastitic event and chronic cows that would possibly reduce the herd milk production and quality.Highlights The combined use of SCC and DSCC is a novel approach to screen for udder health status. Differences in milk yield, composition and coagulation exist among cows with different udder health status defined on the basis of combined SCC and DSCC. The information about udder health status may be used to develop mastitis pre-screening protocols.

Somatic cell count (SCC) has been extensively used as indicator of udder health and milk quality. Recent developments in milk-testing technology have led to cell differentiation in milk in a high throughput manner. Information on the proportion of the different cell types in milk would represent a valuable asset for a more precise definition of udder health status. The aim of the present study was to apply receiver-operating characteristic curve analysis to define the most accurate thresholds of milk differential somatic cell count (DSCC), which represents the percentage of neutrophils plus lymphocytes in the total SCC. The dataset accounted for 117,482 test-day records of 60,009 Holstein Friesian, Brown Swiss and Simmental cows. Different thresholds were defined so that DSCC trends were analysed throughout the lactation, considering also the classification factors of breed and parity. Finally, cows were classified as healthy, susceptible, mastitic or chronic on the basis of their health status, which was defined combining the information of SCC (below or above 200,000 cells/mL) and DSCC (below or above the specific cut-off). Our findings offered new insights for a practical use of DSCC to screen for mastitis, in order to help farmers make decisions to reduce the use of antimicrobials in the herd.

The study evaluated the dynamics of macrophages and polymorphonuclear leukocytes milk-secreted by Mediterranean Buffaloes (MBs). Sixty quarter-milk-samples were collected and divided into three groups (n = 20 units each one): clinical mastitis (CM), subclinical mastitis (SCM), and intramammary infection (IMI). The control group consisted of an additional 20 healthy quarters. Their health status was assessed by clinical examination, quantitative somatic cell count (QSCC) and bacteriological milk culture. Finally, a differential somatic cell count (DSCC) was performed on all the milk samples. The mean percentage of macrophages, both in CM- and SCM-quarters, showed a significant difference as compared with the healthy-ones. Significant differences were also detected comparing the mean percentages of polymorphonuclear leukocytes between CM- and healthy-quarters, SCM and healthy, IMI and healthy. The QSCC revealed a weak-significant-negative-correlation with the quantitation of macrophages (r = −0.388), and a moderate-significant-positive-correlation with the polymorphonuclear leukocytes (r = 0.477). Macrophages and polymorphonuclear leukocytes showed a weak-significant-negative-correlation between them (r = −0.247). The interpretation of macrophages and polymorphonuclear leukocytes dynamics in milk provided beneficial information regarding the clinical status of the quarters enrolled. Future studies exploring the potential use of DSCC to improve udder health represent an interesting perspective in these ruminants. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the associations between differential somatic cell count (DSCC) and milk quality and udder health traits, and for the first time, between DSCC and milk coagulation properties and cheesemaking traits in a population of 1,264 Holstein cows reared in northern Italy. Differential somatic cell count represents the combined proportions of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in the total somatic cell count (SCC), with macrophages (MAC) making up the remaining proportion. The milk traits investigated in this study were milk yield (MY), 8 traits related to milk composition and quality (fat, protein, casein, casein index, lactose, urea, pH, and milk conductivity), 9 milk coagulation traits [3 milk coagulation properties (MCP) and 6 curd firming (CF) traits], 7 cheesemaking traits, 3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits. A linear mixed model was fitted to explore the associations between SCS combined with DSCC and the aforementioned milk traits. An additional model was run, which included DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the percentage of PMN-LYM and MAC by SCC in the milk for each cow in the data set. The unfavorable association between SCS and milk quality and technological traits was confirmed. Increased DSCC was instead associated with a linear increase in MY, casein index, and lactose proportion and a linear decrease in milk fat and milk conductivity. Accordingly, DSCC was favorably associated with all MCP and CF traits (with the exception of the time needed to achieve maximum, CF), particularly with rennet coagulation time, and it always displayed linear relationships. Differential somatic cell count was also positively associated with the recovery of milk nutrients in the curd (protein, fat, and energy), which increased linearly with increasing DSCC. The PMN-LYM count was rarely associated with milk traits, even though the pattern observed confirmed the results obtained when both SCS and DSCC were included in the model. The MAC count, however, showed the opposite pattern: MY, casein index, and lactose percentage decreased and milk conductivity increased with an increasing MAC count. No significant association was found between PMN-LYM count and MCP, CF, CY, and REC traits, whereas MAC count was unfavorably associated with MCP, CF traits, some CY traits, and all REC traits. Our results showed that the combined information derived from SCS and DSCC might be useful to monitor milk quality and cheesemaking-related traits. [ABSTRACT FROM AUTHOR]

Somatic cell count is frequently used as an indicator of intramammary infections (IMI) in dairy cattle worldwide. The newly introduced differential SCC (DSCC) can potentially contribute to detection of IMI. The purpose of this study was to investigate the dynamics of SCC and DSCC after IMI. We used a data set with monthly samples from 2 Danish dairy herds through 1 yr, using bacterial culture to identify IMI. The dynamics of SCC and DSCC with regard to IMI were assessed at quarter level following new IMI with each of 3 defined pathogen groups, major, minor, or "other" pathogens, using general additive models. Both SCC and DSCC increased after IMI, with a more pronounced increase if major or other pathogens were detected compared with minor pathogens. We found that DSCC increased after IMI with other pathogens in both herds and, in herd 2, after IMI caused by major and minor pathogens. We also estimated the duration of increased SCC and DSCC when they exceeded a threshold, done separately for each pathogen group. Major pathogens had the longest-lasting effect in both herds for both SCC and DSCC. We conclude that the magnitude and duration of response of SCC and DSCC to IMI differs between herds and causative pathogens. [ABSTRACT FROM AUTHOR]

Intramammary infection (IMI) can cause mastitis, which is one of the costliest and most prevalent diseases in dairy cattle herds. Somatic cell count (SCC) is a well-established parameter to indicate IMI, and it represents the total count of immune cells in the milk. The differential somatic cell count (DSCC) has also long been suggested to indicate IMI, but no machine was available until recently to provide this parameter automatically. Two new machines have recently been introduced to measure the milk DSCC as an additional indicator of IMI. Here we provide insights about the DSCC measured by these two machines and the value it may provide for udder health management, based on the available literature. We also provide perspectives for future research to investigate potential value in using the DSCC to improve udder health.

The progressive decrease of mean SCC in dairy herds worldwide is affecting SCC accuracy as a subclinical mastitis marker. This evidence supports studies aiming to apply differential cell count (DSCC) as a tool to identify mastitis. Two of the major obstacles to apply DSCC were the unavailability of high-throughput milk analysers and the cost of these analyses. Recently availability of high-throughput milk analysers, able to perform a partial DSCC on milk, allowed designing a study aiming to identify subclinical mastitis in individual milk samples. This paper reports the result of this first Italian study performed under field conditions. The study considered 4386 milk test records from four dairy herds with different size, management and milking management. DSCC data were analysed by ROC procedure. This procedure allows identifying the threshold giving the highest accuracy and the highest combined value for sensitivity and specificity, among all the possible thresholds. Among the different ways used to classify milk samples, the analysis applied to days in milk (three classes) showed the highest mean values for sensitivity plus specificity, and the value for accuracy was very close to the highest one observed. At the time of submission, this is the first paper available on peer-reviewed scientific journals reporting the evaluation of DSCC as a marker for subclinical mastitis on individual milk samples collected during routine milk test. The results will help the improvement of mastitis diagnosis and will help dairy farmers to increase the levels of herd management and efficiency.Highlights At the time of submission, this is the first paper available on peer-reviewed scientific journals on the evaluation of DSCC as a marker for subclinical mastitis on individual milk samples. The analysis of data showed as DSCC has not consistent performances, confirming the presence of confounding factors such as parity and days in milk. The thresholds calculated on samples classified by days in milk (three classes) showed to have the overall best test performances with an accuracy of 82.3%.

The combined use of somatic cell count (SCC) and differential somatic cell count (DSCC), which is the ratio of neutrophils plus lymphocytes to total milk SCC, represents a novel approach to define cow's udder health status, as it allows to identify healthy animals (those with low SCC and DSCC), cows susceptible to mastitis (those where an immune response has begun, so that there is an increase of neutrophils, i.e. DSCC, but not yet of total SCC), animals with a mastitic event in progress (those with high SCC and DSCC) and animals with possible chronic inflammation (those with high SCC and low DSCC, as macrophages prevail). We investigated the association of cow's udder health status with milk yield, composition and coagulation properties in four cattle breeds. Results demonstrated that milk traits vary among cows with different udder health status, especially in terms of fat percentage, lactose percentage and coagulation ability. The most pronounced worsening in milk yield and coagulation ability was observed for animals with chronic inflammation. Our findings support the new approach based on the combined use of SCC and DSCC to screen for cow's udder health, as it would allow to identify susceptible cows that will probably undergo a mastitic event and chronic cows that would possibly reduce the herd milk production and quality. The combined use of SCC and DSCC is a novel approach to screen for udder health status. Differences in milk yield, composition and coagulation exist among cows with different udder health status defined on the basis of combined SCC and DSCC. The information about udder health status may be used to develop mastitis pre-screening protocols. [ABSTRACT FROM AUTHOR]

Mastitis, often caused by intramammary infection (IMI), is a significant problem in dairy farming globally. Somatic cell count (SCC) is widely used as a parameter for screening IMI in cows that are then treated or culled. We investigated the potential of a new parameter, differential SCC (DSCC), to detect IMI at cow level when SCC is already known. We achieved this using bacterial culture (BC) and PCR to detect 4 categories of pathogens (major, minor, other, and any) in 2 Danish dairy herds. Quarter milk samples were collected from monthly dairy herd improvement samplings over 1 yr and analyzed with BC, whereas cow-level dairy herd improvement samples were analyzed using PCR. Days in milk, parity, and IMI status had a significant effect on DSCC. Using DSCC in addition to SCC significantly improved the indication of IMI compared with using only SCC in the any pathogen category in both herds as well as the minor pathogens category in herd 2 when BC was used for detection. When PCR was used to detect IMI, the use of DSCC in addition to SCC was significant for the other pathogens category in herd 1 and the minor pathogens category in herd 2. Thus, our data revealed that DSCC can add significant information describing IMI status even when SCC is already known; however, this depends on the causative pathogen. Future studies may address how to use DSCC in practice as well as consider the availability of temporal data to potentially gain insight into the course of infection. [ABSTRACT FROM AUTHOR]

The study evaluated the dynamics of macrophages and polymorphonuclear leukocytes milk-secreted by Mediterranean Buffaloes (MBs). Sixty quarter-milk-samples were collected and divided into three groups (n = 20 units each one): clinical mastitis (CM), subclinical mastitis (SCM), and intramammary infection (IMI). The control group consisted of an additional 20 healthy quarters. Their health status was assessed by clinical examination, quantitative somatic cell count (QSCC) and bacteriological milk culture. Finally, a differential somatic cell count (DSCC) was performed on all the milk samples. The mean percentage of macrophages, both in CM- and SCM-quarters, showed a significant difference as compared with the healthy-ones. Significant differences were also detected comparing the mean percentages of polymorphonuclear leukocytes between CM- and healthy-quarters, SCM and healthy, IMI and healthy. The QSCC revealed a weak-significant-negative-correlation with the quantitation of macrophages (r = −0.388), and a moderate-significant-positive-correlation with the polymorphonuclear leukocytes (r = 0.477). Macrophages and polymorphonuclear leukocytes showed a weak-significant-negative-correlation between them (r = −0.247). The interpretation of macrophages and polymorphonuclear leukocytes dynamics in milk provided beneficial information regarding the clinical status of the quarters enrolled. Future studies exploring the potential use of DSCC to improve udder health represent an interesting perspective in these ruminants.

The aim of the present study was to assess genetic variation and heritability of a novel indicator of udder health, milk differential somatic cell count (DSCC), which represents the percentage of neutrophils plus lymphocytes in the total somatic cell count (SCC). Furthermore, we estimated genetic and phenotypic correlations of DSCC with other milk traits routinely measured in Italian Holstein cows. Besides DSCC, test-day data included milk yield, composition traits (i.e., fat, protein, casein, and lactose percentages), pH, milk urea nitrogen, and SCC. After editing, the final data set included 10,709 test-day records of 5,142 cows in 299 herds. Mean of DSCC was 62.07%, which means that macrophages were approximately 38% of total SCC. Comparing our results with the literature offered compelling evidence of the importance of acquiring information about the proportion of the different cell types in milk to better define the udder health status. In addition, our analysis revealed, for the first time, that DSCC is a heritable trait, and heritability (0.08 ± 0.02) was higher than that of traditional somatic cell score (0.04 ± 0.02). Nevertheless, heritability of DSCC is still low compared with milk yield and quality traits. Single-trait analysis within parity showed that DSCC was less heritable in primiparous than in multiparous cows, whereas bivariate analysis confirmed that DSCC and somatic cell score were 2 different traits, as their genetic and phenotypic correlations differed from unity. From a genetic point of view, the DSCC was positively weakly associated with milk yield, lactose percentage, and milk urea nitrogen, and negatively associated with pH. Our findings contributed to the understanding of the genetic background of DSCC and are a precursor to the potential use of DSCC in breeding programs to enhance cow resistance to mastitis. However, further research is needed to determine the weight this novel trait should receive in a selection program aimed to reduce udder health problems. [ABSTRACT FROM AUTHOR]

Recent available instruments allow to record the number of differential somatic cell count (DSCC), representing the combined proportion of polymorphonuclear leukocytes and lymphocytes, on a large number of milk samples. Milk DSCC provides indirect information on the udder health status of dairy cows. However, literature is limited regarding the effect of DSCC on milk composition at the individual cow level, as well as its relation to the somatic cell score (SCS). Hence, the aims of this study were to (i) investigate the effect of different levels of DSCC on milk composition (fat, protein, casein, casein index, and lactose) and (ii) explore the combined effect of DSCC and SCS on these traits. Statistical models included the fixed effects of days in milk, parity, SCS, DSCC and the interaction between SCS × DSCC, and the random effects of herd, animal within parity, and repeated measurements within cow. Results evidenced a decrease of milk fat and an increase in milk fatty acids at increasing DSCC levels, while protein, casein and their proportion showed their lowest values at the highest DSCC. A positive association was found between DSCC and lactose. The interaction between SCS and DSCC was important for lactose and casein index, as they varied differently upon high and low SCS and according to DSCC levels.

The progressive decrease of mean SCC in dairy herds worldwide is affecting SCC accuracy as a subclinical mastitis marker. This evidence supports studies aiming to apply differential cell count (DSCC) as a tool to identify mastitis. Two of the major obstacles to apply DSCC were the unavailability of high-throughput milk analysers and the cost of these analyses. Recently availability of high-throughput milk analysers, able to perform a partial DSCC on milk, allowed designing a study aiming to identify subclinical mastitis in individual milk samples. This paper reports the result of this first Italian study performed under field conditions. The study considered 4386 milk test records from four dairy herds with different size, management and milking management. DSCC data were analysed by ROC procedure. This procedure allows identifying the threshold giving the highest accuracy and the highest combined value for sensitivity and specificity, among all the possible thresholds. Among the different ways used to classify milk samples, the analysis applied to days in milk (three classes) showed the highest mean values for sensitivity plus specificity, and the value for accuracy was very close to the highest one observed. At the time of submission, this is the first paper available on peer-reviewed scientific journals reporting the evaluation of DSCC as a marker for subclinical mastitis on individual milk samples collected during routine milk test. The results will help the improvement of mastitis diagnosis and will help dairy farmers to increase the levels of herd management and efficiency. At the time of submission, this is the first paper available on peer-reviewed scientific journals on the evaluation of DSCC as a marker for subclinical mastitis on individual milk samples. The analysis of data showed as DSCC has not consistent performances, confirming the presence of confounding factors such as parity and days in milk. The thresholds calculated on samples classified by days in milk (three classes) showed to have the overall best test performances with an accuracy of 82.3%. [ABSTRACT FROM AUTHOR]

Intramammary infections induce the initiation of the inflammatory response, resulting in an increase in somatic cell count (SCC) in milk. The SCC includes several different types of cells but does not differentiate between them. On the contrary, the new differential somatic cell count (DSCC) parameter allows for the differentiation between 2 groups of cells: polymorphonuclear neutrophils (PMN) and lymphocytes versus macrophages. Therefore, the aim of this paper was to describe the changes of both DSCC and SCC during mastitis induced by cell wall components from typical mastitis-causing pathogens [lipopolysaccharide (LPS), Escherichia coli; lipoteichoic acid (LTA), Staphylococcus aureus] known to trigger different severities of mastitis. In addition, the effect the glucocorticoid prednisolone (PRED), which is known to attenuate the immune response in the mammary gland, was investigated. Twenty dairy cows were equally divided into 5 groups and treated with LPS, LTA, LPS+PRED, LTA+PRED, or a saline control. Milk samples were taken at the following time points: baseline (d −3, −2, and −1), right before treatment (d 0), 5 h after treatment (d 0.2), early cure phase (d 1 and 2), and late cure phase (d 3, 4, 5, 6, 7, and 14) and analyzed for DSCC and SCC. Mean DSCC values increased significantly from <60% at baseline and right before treatment to >81% 5 h after treatment and the early cure phase in all groups, except for the groups control and LTA+PRED. This increase clearly reflects a shift in cell populations to predominantly PMN. The SCC increased significantly following the stimulation, too, as expected. Interestingly, we observed cases where SCC increased moderately only whereas DSCC showed an evident increase, meaning that the shift in cell populations occurred even at low SCC levels. The PRED clearly lowered the cell migration in group LTA+PRED. This is the first ever study investigating DSCC during induced mastitis under controlled conditions. The combination of DSCC and SCC could be employed for the earlier detection of mastitis by revealing the shift in cell population independent from the SCC level. Furthermore, combining DSCC and SCC information could help to determine the stage of mastitis because we observed high DSCC and SCC results in the early stage of mastitis but evidently lower DSCC and high SCC in the cure phase. Hence, our results offer the first fundamental insights on how mastitis monitoring could be improved in the frame of dairy herd improvement programs. [ABSTRACT FROM AUTHOR]

Somatic cell count (SCC) is used as key indicator in mastitis screening programs typically applied in the frame of Dairy Herd Improvement (DHI) testing programs. However, mastitis is still causing tremendous economic losses to the dairy industry. Hence, new biomarkers are needed that can be used for enhanced management of mastitis on dairy farms. Besides the determination of SCC, differentiation of cells has been described to be beneficial for a more definite description of the actual udder health status of dairy cows. The aim of this study was to develop a new method for rapid and simultaneous determination of SCC and a new parameter, differential somatic cell count (DSCC), in individual cow milk samples using flow cytometry. The method is sought to be applied in central milk testing laboratories, so that existing DHI infrastructures can be used. The DSCC represents the combined proportion of polymorphonuclear leukocytes (PMN) and lymphocytes expressed in percentage. The proportion of macrophages can be calculated by subtracting DSCC from 100%. Our research revealed increasing proportions of PMN, but decreasing proportions of macrophages as SCC increased. However, lymphocytes occurred fairly constantly with low proportions across the entire SCC range. Hence, the DSCC parameter reflects the antidromic trend of PMN and macrophages. Fluorescence microscopy was used to evaluate the specificity of the new Foss DSCC method in terms of DSCC and a high correlation was found. Apart from that, the accuracy of cell differentiation using the Foss DSCC method was confirmed in a cell sorting trial. Total SCC could be determined equally well using the new method as compared with existing methods. The new method was further proven to be robust toward a range of method and milk-sample-related factors. In an initial field trial, regular DHI samples of a local dairy herd were analyzed. The DSCC values occurred in a broad range from 34 to 79% in samples with <400,000 cells/mL. Higher DSCC values (53-89%) were found in samples with >400,000 cells/mL. In conclusion, the new Foss DSCC method allows reliable, repeatable, fast, robust, and accurate determination of both DSCC and SCC at low cost. This, in turn, provides more accurate information on the actual udder health status of dairy cows. The practical application of DSCC in the frame of DHI testing programs, however, needs further investigation. [ABSTRACT FROM AUTHOR]

• Differential somatic cell count has been proposed as an indicator of cow udder health. • When combined with somatic cell count, it should improve mastitis identification. • Part of the variability of udder health traits is attributable to the cow effect. • Milk yield and composition are affected by the udder health status. • Monitoring udder health through milk traits can result in smarter decision-making. The milk differential somatic cell count (DSCC) has been proposed in recent years as a mean by which to better monitor the udder health status (UHS) in dairy cows. Milk DSCC is the amount of polymorphonuclear neutrophils and lymphocytes contributing to the total somatic cell count (SCC) and can be determined on a routine basis in individual milk samples subjected to official analysis. In the present study, 522 865 milk test-day records of 77 143 cows were scrutinised to identify factors affecting the variability of both DSCC and SCC in Holstein Friesian, Jersey, Simmental and Rendena cows through linear mixed models. The fixed effects were breed, parity, lactation stage, sampling season, and all the first-order interactions of breed. Cow and herd‐test‐date were considered as random. Subsequently, four UHS groups were created (1: SCC ≤ 200 000 cells/mL and DSCC ≤ 65%; 2: SCC ≤ 200 000 cells/mL and DSCC > 65%; 3: SCC > 200 000 cells/mL and DSCC > 65%; 4: SCC > 200 000 cells/mL and DSCC ≤ 65%) to compare milk yield and quality. Milk SCS and DSCC differed across lactation, parity, sampling season and breed. In particular, Simmental cows had the lowest SCC and Jersey the lowest DSCC. Depending on the breed, UHS affected daily milk yield and composition to a different extent. The UHS group 4, i.e. the one grouping test-day records with high SCC and low DSCC, presented the lowest estimate of milk yield and lactose content no matter the breeds. Our findings support that udder health-related traits (SCS and DSCC) are useful information to improve udder health at individual cow and herd levels. Moreover, the combination of SCS and DSCC is useful to monitor milk yield and composition. [ABSTRACT FROM AUTHOR]

• A routine mastitis screening is crucial for udder health management on dairy farms. • Somatic cell count and its log score (SCS) are routinely used for this purpose. • Differential somatic cell count (DSCC) and milk MIR-spectra are new parameters. • All parameters were evaluated as potentially useful for routine mastitis screening. • Models combining SCS, MIR and DSCC showed best accuracies for predicting mastitis. Routine mastitis screening is crucial on dairy farms, and it usually involves analysis of somatic cell count (SCC), which serves as an indicator of mastitis. Milk mid-infrared (MIR) spectral analysis, the standard method to determine milk components, was evaluated as an additional tool for the prediction of mastitis, since health condition of a cow is affecting milk composition. Differential somatic cell count (DSCC), the percentage of SCC due to polymorphonuclear leukocytes (PMN) and lymphocytes, is another new indicator to detect cows having an infected udder mammary gland. In a previous study we developed mastitis prediction models based on somatic cell score (SCS) and MIR spectral data from routine milk recording. The present study compared various models to predict clinical mastitis based on the parameters DSCC, SCS and MIR spectral data, alone or in combination. Data on DSCC, SCC, MIR spectral data and mastitis diagnosis by veterinarians were extracted for 40,332 dairy cows of the breeds Fleckvieh, Brown Swiss and Holstein Friesian from the Austrian milk recording and health monitoring systems. Three data subsets were created based on whether test-day records were classified as "mastitis" when they occurred 21 days (subset 1), 14 days (subset 2), or 7 days (subset 3) before or after diagnosis of mastitis; all other records were classified as "healthy". The performance of seven models (DSCC alone, SCS alone, MIR alone, DSCC+SCS, DSCC+MIR, MIR+SCS and DSCC+SCS+MIR) was assessed in these subsets using partial least squares discriminant analysis. In all subsets, the DSCC model showed the highest sensitivities (0.710 to 0.792) but lowest specificities (0.564 to 0.562) during external validation. The SCS model showed lower sensitivities (0.624 to 0.662) but higher specificities (0.725 to 0.769) than DSCC. The MIR model showed sensitivities from 0.598 to 0.617 and specificities from 0.668 to 0.681. The models combining multiple predictors showed the highest balanced accuracies: the MIR+SCS model in subset 1 (0.697), DSCC+SCS+MIR in subset 2 (0.712) and MIR+SCS in subset 3 (0.744). Taken together, our analyses suggest that DSCC, SCS and MIR spectral data are useful for routine mastitis screening, with SCS performing as the best single predictor and the MIR+SCS model performing best overall. [ABSTRACT FROM AUTHOR]

Mastitis, in particular in its subclinical form, which may spread unnoticeable within a herd, continues to be a major challenge in the dairy industry. Somatic cell count (SCC) is a broadly used proxy for subclinical mastitis. The recently introduced Differential SCC (DSCC) representing the combined proportion of polymorphonuclear neutrophils and lymphocytes as a percentage of total SCC, can be used in combination with SCC to categorise cows into four different udder health groups (UHG) depending on actual test day results: UHG A: healthy/normal, ≤200,000 cells/mL and DSCC ≤65 %; B: suspicious, ≤200,000 cells/mL and DSCC >65 %; C: (subclinical) mastitis, >200,000 cells/mL and DSCC >65 %; D: chronic/persistent mastitis, >200,000 cells/mL and DSCC ≤65 %. The objective of our study was to investigate to what extent the UHG aid in determining different statuses of cows: I) leaving herd before next test day, II) having >200,000 cells/mL at the next test day, and III) having ≤200,000 cells/mL at the next 3 test days. Multivariable logistic regression analysis was used to evaluate these statuses based on routinely generated dairy herd improvement (DHI) data from Austria, China, Estonia, Germany, and Spain. Cows in groups C (odds ratio (OR): 2.13, 95 % confidence interval (CI): 1.95–2.34) and, particularly, D (OR: 3.91, 95 % CI: 3.31–4.62) were significantly more likely to leave herds compared to cows in group A. Late-lactating cows indicated the highest likelihood (OR: 16.03, 95 % CI: 14.44–17.81) to leave herds in our analysis. Interestingly, we found that cows in UHG B had significantly higher odds (OR: 2.77, 95 % CI: 2.58–2.98) to have >200,000 cells/mL at the next test day compared to cows in group A. As anticipated, cows in UHG B (OR: 0.40, 95 % CI: 0.38–0.42), C (OR: 0.08, 95 % CI: 0.07–0.09), and D (OR: 0.16, 95 % CI: 0.14–0.19) each were significantly less likely to have ≤200,000 cells/mL at the next 3 test days compared to cows in group A. Above described results are an example from Germany, but the same trends could be seen across all countries considered in our study. In conclusion, our findings illustrate that the UHG concept reveals additional valuable information about udder health and culling based a single test day over working with SCC only. Actual decisions in day-to-day farm management that could be taken were not investigated here and need to be further explored. [ABSTRACT FROM AUTHOR]

Mastitis is still the costliest disease in milk production. In particular, its subclinical form, which may spread unnoticeably within a herd, is a major challenge. Somatic cell count (SCC) is broadly used as an indicator for mastitis and thus the basis for udder health management programmes, e.g. through dairy herd improvement (DHI) testing. Since recently, differential somatic cell count (DSCC, representing the combined proportion of polymorphonuclear neutrophils and lymphocytes as a percentage of total SCC) is available in addition. Our study was aimed to investigate dairy cow performance in four newly defined udder health groups (UHG) based on SCC and DSCC results from DHI testing. In total, 961,835 test-day results generated in Austria, China, Estonia, Germany, and Spain between January 2019 and March 2020 were available for data analyses. Cows were categorised into four UHG depending on test day SCC and DSCC results (UHG A: healthy/normal, ≤200,000 cells/mL and ≤65 %; B: suspicious, ≤200,000 cells/mL and >65 %; C: mastitis, >200,000 cells/mL and >65 %; D: chronic/persistent mastitis, >200,000 cells/mL and ≤65 %). Linear mixed effect models were used to compare the performance of cows between the UHG based on the parameters milk weight, energy-corrected milk, fat, protein, lactose, and estimated milk value. Highest performance was found for cows in UHG A and ranged between 21.4 (Austria) and 38.3 kg per cow and day (Spain). Interestingly, cows in group B were significantly less productive (0.9–2.4% less daily milk production) compared to those in group A. Cows in groups C (6.0–9.8% less daily production compared to group A) and D were, as expected, even less productive with a particularly significant drop for cows in group D (17.5–38.5% less daily production). These trends could be observed in all countries involved in this study. Proportions of cows in the four different UHG differed between countries, changed slightly within countries depending on season, differed depending on parity and days in milk, and were seen to vary hugely between herds. In conclusion, this study demonstrates changes in performance of dairy cows depending on their udder health status as defined based on the combination of SCC and DSCC. In particular cows in UHG B and D are of interest as they cannot be identified working with SCC only. Nevertheless, the actual udder health management measures that could be taken based on the new UHG still require further investigation. [ABSTRACT FROM AUTHOR]

Mastitis, particularly in its subclinical form, is the costliest disease in milk production causing substantial financial losses to the dairy industry, impairing animal welfare, and one of the main reasons for treating dairy cows with antimicrobials. Somatic cell count (SCC) is broadly used as an indicator for mastitis or intramammary infection (IMI) and is the basis for udder health management programmes, e.g., through monthly dairy herd improvement (DHI) testing. While SCC shows the total number of cells in milk, the new Differential SCC (DSCC) shows also the combined proportion of polymorphonuclear neutrophils (PMN) and lymphocytes as a percentage of the total SCC. In this study, we investigated the test characteristics of DSCC as a new supplementary indicator for mastitis screening. We collaborated with 11 herds totalling 969 dairy cows and collected metered DHI samples once a month over four months. The IMI status was assessed through analysis of aseptic composite hand-stripped samples using culture and followed by species identification using MALDI-ToF. The pathogens detected were categorised as 'no', 'minor', 'major', or 'other' pathogens. The results of our study showed that the DSCC parameter was significantly associated with the IMI status and the cow's parity but not with days in milk or test-day milk weight. On the other hand, SCC was associated with all these four factors. DSCC counts were significantly higher in samples of cows with IMI caused by major pathogens as compared to cows with no IMI or IMI by minor or other pathogens. SCC alone, DSCC alone, and the combination of DSCC and SCC were further compared based on test characteristics using exemplary cut-offs. For example, working with a cut-off of 200,000 cells/ for SCC alone compared to working with the combination of DSCC of 65 % and/or 200,000 cells/mL to classify cows as infected by major pathogens, the sensitivity increased from 78 % to 92 % and the specificity decreased from 87 % to 66 %. With the combination, the positive predictive value changed from 52 % to 34 %, and the negative predictive value stayed at the same level (96 % vs 98 %). In summary, our study provides first insights on test characteristics of the DSCC parameter used in combination with the well-established SCC for monitoring udder health using DHI testing. This combination opens up the possibility to further improve udder health monitoring programmes (e.g., improved identification of IMI caused by major pathogens) but more work on the subject is needed. [ABSTRACT FROM AUTHOR]

Simple Summary: Recently, high-throughput instruments have been used to analyze milk differential somatic cell count, represented by the combined proportion of polymorphonuclear leukocytes and lymphocytes, providing indirect information on the udder inflammation status of dairy cows. No information is available about the relationship between differential somatic cell count and milk quality, so the aim of this study was to investigate the effect of differential somatic cell count on the composition of a large number of individual milk samples. Results showed that milk quality worsened when differential somatic cell count was high. In particular, it was evidenced lower milk fat, protein, casein contents and casein index, and augmented milk fatty acids could be found with an increasing differential somatic cell count level. These findings confirmed that differential somatic cell count could be a new informative tool for dairy farmers to monitor the quality of milk. Recent available instruments allow to record the number of differential somatic cell count (DSCC), representing the combined proportion of polymorphonuclear leukocytes and lymphocytes, on a large number of milk samples. Milk DSCC provides indirect information on the udder health status of dairy cows. However, literature is limited regarding the effect of DSCC on milk composition at the individual cow level, as well as its relation to the somatic cell score (SCS). Hence, the aims of this study were to (i) investigate the effect of different levels of DSCC on milk composition (fat, protein, casein, casein index, and lactose) and (ii) explore the combined effect of DSCC and SCS on these traits. Statistical models included the fixed effects of days in milk, parity, SCS, DSCC and the interaction between SCS × DSCC, and the random effects of herd, animal within parity, and repeated measurements within cow. Results evidenced a decrease of milk fat and an increase in milk fatty acids at increasing DSCC levels, while protein, casein and their proportion showed their lowest values at the highest DSCC. A positive association was found between DSCC and lactose. The interaction between SCS and DSCC was important for lactose and casein index, as they varied differently upon high and low SCS and according to DSCC levels. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the new differential somatic cell count (DSCC) as a supplementary indicator to SCC for the identification of intramammary infection (IMI) in dairy cows at the end of the lactation period. Different approaches for identification of cows with IMI (i.e. often based on SCC) and targeted antimicrobial treatment of those rather than of all cows have been developed (i.e. selective dry cow treatment). Recently, DSCC representing the proportion of polymorphonuclear neutrophils and lymphocytes, has been introduced as an additional indicator for the presence of IMI. We used the last dairy herd improvement (DHI) samples taken within 42 d prior to dry-off as well as hand-stripped samples collected within 5 days prior to dry-off to measure DSCC and SCC. The bacteriological status was determined using quarter foremilk samples collected close to drying off. In total, 582 cows were dried off during our study but not all of them could be included in the data analysis for different reasons (e.g. incomplete data, samples too old for reliable determination of SCC and DSCC, contamination). Eventually, the final data set comprised of 310 cows of which 64 and 149 were infected with major and minor pathogens, respectively, and 97 were uninfected. The area under receiver-operating characteristics curves (AUC) were calculated to compare the diagnostic abilities of the different parameters. The AUC for identification of IMI by major pathogens when using the combination of DSCC and SCC was 0.64 compared to 0.62 for SCC alone and 0.62 for DSCC alone. The different parameters were further compared based on test characteristics and predictive values. For example, classifying cows as infected based on a cut-off of 200,000 cells/ml for SCC alone and in terms of using DSCC combined with SCC based on either >60% and/or >200,000 cells/ml, the sensitivity changed from 47 to 66% and the specificity from 74 to 54%. At the same time, the negative predictive value changed from 84 to 86% and the positive predictive value from 32 to 27%. Test characteristics and predictive values of the parameters DSCC and SCC were similar using DHI and hand-stripped samples. In conclusion, our study provides first indications on test characteristics and predictive values for the combination of DSCC and SCC. However, more work on this subject and the actual practical application is needed. [ABSTRACT FROM AUTHOR]