Differential Somatic Cell Count Research Articles

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, namely ML_SCS_DSCC, SCS_4_DSCC_65_binary, and ML_SCS_DSCC_binary, by using milk loss 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 daily heritability estimates over lactation were 0.086 (0.075 to 0.095) for SCS, 0.104 (0.073 to 0.127) for ML_SCS_DSCC, 0.137 (0.014 to 0.297) for SCS_4_DSCC_65_binary, and 0.138 (0.115 to 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.

A First Investigation into the Use of Differential Somatic Cell Count as a Predictor of Udder Health in 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.

Inferring Causalities of Environmental and Genetic Factors for Differential Somatic Cell Count and Mastitis Pathogens in Dairy Cows Using Structural Equation Modelling.

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.

Risk factors of high somatic cell count and differential somatic cells in early lactation associated with selective dry cow therapy.

The routine use of intramammary antimicrobial products in all dairy cows at the beginning of the dry period is no longer allowed in European Union (EU) countries due to the new Regulation (EU) 2019/6 to reduce antimicrobial resistance. This study investigated the application of a selective dry cow therapy scheme and the risk factors of high individual milk somatic cell count (SCC) and individual neutrophil count in early lactation, as a response to the application of a selective dry cow therapy (SDCT) protocol. The study was carried out on three commercial farms, and a total of 243 lactating cows were monitored at the end of lactation and at the beginning of the next one, 91 of which were dried off without the use of antimicrobials (NoT) based on milk SCC, differential somatic cell count (DSCC), and the response of Vetscan DC-Q milk analyser, using a secret algorithm. The remaining 152 cows received antimicrobials (T). After calving, similar means were observed between the two treatment groups for SCC (4.8 vs 4.9 log10 cells/ml for T and NoT, respectively, P=0.5) and total milk leucocyte count (TLC) (5 vs 5.1 log10 cells/ml for T and NoT, respectively, P=0.7) in milk. However, the use of antimicrobials led to a lower DSCC (58 vs 64% for T and NoT, respectively, P=0.01) and lower percentage of neutrophils (59 vs 64% for T and NoT, respectively, P=0.05), although the levels of DSCC and percentage of neutrophils in cows dried off without antimicrobials remained lower than the risk threshold suggested by the international literature. A logistic regression was computed after the application of selective dry cow therapy to identify risk factors of high milk SCC (≥100000 cells/mL) at the beginning of lactation. Increased milk SCC after calving was related to high SCC at the end of lactation and abandonment of antimicrobial therapy at dry-off. Moreover, the length of the dry period, milk protein content, and flank cleanliness in the last test day before dry-off were other factors in the logistic regression. Neutrophil counts at the beginning of the next lactation were affected by the same factors that influenced SCC, together with milk production, TLC, and macrophages on the last test day. The results obtained in these studied farms showed that selective dry cow therapy may be applied without adversely affecting the next lactation.

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.

Usefulness of the total and differential somatic cell count based udder health group concept for evaluating herd management practices and udder health in dairy herds.

Subclinical mastitis and associated economic losses are a steady challenge in the dairy industry. The combination of the well-established somatic cell count (SCC) parameter and the new differential SCC (DSCC) opens up the possibility to categorise cows into four different udder health groups (UHG) based on results from a single milk recording/dairy herd improvement (DHI) test: 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 objectives of this study were to investigate 1) herd management practises among herds in different UHG categories and 2) herd performance parameters depending on the proportion of cows in UHG A. A total number of 41 herds in Styria, Austria, and Thuringia, Germany, were visited and interviewed for the first part of the study. The herds were categorised into 3 UHG categories depending on the proportion of cows in UHG A: I =>65 %; II =55-65 %; and III =<55 %. Those with good udder health and best herd performance (+9 % milk yields, +11 % longevity, -35 % antibiotic treatments) applied distinct preventive measures, in particular excellent cubicle management and early antibiotic treatment (P<0.05 each). However, preventive measures were applied to a lower extent in other herds. Herds were categorised differently using the UHG concept compared to SCC alone as the UHG-based categorisation allowed to clearer distinguish herds with medium-good from those with good udder health. A total number of 129,812 regular milk recording/DHI test day results of 890 Austrian and 183 German herds was used for the second part of the study. Results revealed a trend of increasing daily production as proportions of cows in UHG A increase. In conclusion, the UHG concept allowed clearer distinction of herds with good, medium-good, and poor udder health and could be used to promote practises leading to better animal health, less antibiotic treatments, and higher milk quality.

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 impact. 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-year period: i) the heritability and repeatability values of Somatic Cell Score (SCS) and DSCC, ii) the genetic and phenotypic correlations between these 2 traits and milk production and milk composition traits, iii) 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. Since 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.

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 impacts 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.

Assessment of the relationship between differential somatic cell count and presence of subclinical mastitis

Somatic cell count (SCC) is commonly used for the identifica­tion of cows with subclinical mastitis. Differential somatic cell count (DSCC) represents a novel diagnostic method that allows for the identification and quantification of the different types of inflammatory cells in milk samples (neutrophils (NEU), lym­phocytes (LYM) and macrophages (MAC)) and thus the propor­tions of different cell types could be utilized for subclinical mastitis screening. The objective of this study was to investi­gate if the counts and abundance of different leukocytes types could be used for the identification of cows with subclinical mastitis.

Investigation of the relationship between differential somatic cell count and milk culture

Milk culture is the gold standard for detection of intramam­mary infections (IMI) in dairy cows. Somatic cell count (SCC) is widely used for diagnosing subclinical mastitis, but its predic­tive value for detecting IMI is limited. Differential SCC (DSCC) provides information about the changes in the relative abun­dance of different cell types during the inflammatory response and is a potential alternative for the identification of quarters with IMI. However, its ability to detect IMI is unclear and re­quires further investigation. Thus, the objective of this study was to investigate the predictive value of DSCC in the identifica­tion of quarters with IMI.

Udder health-related traits in cow milk: phenotypic variability and effect on milk yield and composition

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.

How heat stress conditions affect milk yield, composition, and price in Italian Holstein herds

An edited data set of 700 bulk and 46,338 test-day records collected between 2019 and 2021 in 42 Holstein-dominated farms in the Veneto Region (North of Italy) was available for the present study. Information on protein, fat and lactose content, somatic cell count, and somatic cell score was available in bulk milk as well as individual test-day records, whereas urea concentration (mg/dL), differential somatic cell count (%), and milk yield (kg/d) were available for test-day records only. Milk features were merged with meteorological data retrieved from 8 weather stations located maximum 10 km from the farms. The daily and weekly temperature-humidity index (THI; wTHI) and maximum daily (MTHI) and weekly temperature-humidity index were associated with each record to evaluate the effect of heat stress conditions on milk-related traits through linear mixed models. Least squares means were estimated to evaluate the effect of THI and, separately, of MTHI on milk characteristics correcting for conventional systematic factors. Overall, heat stress conditions lowered the quality of both bulk milk and test-day records, with fat and protein content being greatly reduced, and somatic cell score and differential somatic cell count augmented. Milk yield was not affected by either THI or MTHI in this data set, but the effect of elevated THI and MTHI was in general stronger on test-day records than on bulk milk. Farm-level economic losses of reduced milk quality rather than reduced yield as consequence of elevated THI or MTHI was estimated to be between $23.57 and $43.98 per farmer per day, which is of comparable magnitude to losses resulting from reduced production. Furthermore, MTHI was found to be a more accurate indicator of heat stress experienced by a cow, explaining more variability of traits compared with THI. The negative effect of heat stress conditions on quality traits commences at lower THI/MTHI values compared with milk yield. Thus, a progressive farmers' income loss due to climatic changes is already a reality and it is mainly due to deterioration of milk quality rather than quantity in the studied area.

Total and Differential Somatic Cell Count in Italian Local Cattle Breeds: Phenotypic Variability and Effect on Milk Yield and Composition.

Milk differential somatic cell count (DSCC) represents the percentage of polymorphonuclear neutrophils and lymphocytes out of the total somatic cell count (SCC) and has been proposed in recent years as a proxy for udder health in dairy cows. We investigated phenotypic factors affecting SCC and DSCC using 3978 records of 212 Alpine Grey and 426 Burlina cows farmed in Northern Italy. The linear mixed model accounted for the fixed effects of breed, parity, lactation stage, sampling season, and first-order interactions of breed with the other effects. Cow, herd-test-date nested within breed were random. Subsequently, four udder health status groups (UHS) were created by combining SCC and DSCC to assess the UHS impact on milk yield and quality. DSCC was greater in Alpine Grey (66.2 ± 0.8%) than Burlina cows (63.2 ± 0.6%) and, similarly to SCC, it increased with days in milk and parity regardless of breed. Milk yield and composition were affected by UHS in both breeds. These results suggest that also udder health of local breeds can be monitored on a large scale through SCC and DSCC for reduction in biodiversity loss and increased farm profitability. However, in addition to milk data, the introduction of mastitis recording and monitoring plans is advisable.

Lactoferrin concentration in bovine milk: validation of radial immunodiffusion technique, sources of variation, and association to udder health status

The present study aimed to evaluate analytical performances of radial immunodiffusion (RID) technique for the quantification of milk lactoferrin (LF), and to investigate the main sources of variation of LF concentration in individual milk samples of Holstein Friesian (HF, n = 1516) and Simmental (SI, n = 230) cows. Repeatability (RSDr) and reproducibility (RSDR) of RID method were assessed as relative standard deviation of 15 measurements within the same day and 45 measurements across 3 d, respectively. Sources of variation of milk LF were investigated through a mixed linear model which included the fixed effects of classes of stage of lactation and parity (and their interaction), cow breed, udder health status (UHS, based on somatic cell count and differential somatic cell count), and the random effects of the herd-test day nested within breed and the residual. Method performances were satisfactory in terms of repeatability (RSDr < 9%) and reproducibility (RSDR < 8%), suggesting that the RID can be considered as a consistent cow-side test for the quantification of milk LF. Milk LF was positively associated with somatic cell score (r = 0.40) and negatively with lactose content (r = −0.33). Lower concentration of LF was observed in milk of ‘healthy’ and ‘susceptible’ cows (13.03 and 12.87 mg/dL, respectively) compared to ‘mastitic’ and ‘chronic’ cows (17.91 and 17.33 mg/dL, respectively). In this light, milk LF has the potential to be a useful biomarker to detect early mastitis. Further research is advisable to deepen the association between milk LF content and observed clinical mastitis. Highlights Radial immunodiffusion method for milk lactoferrin quantification is repeatable and reproducible. Somatic cell count is positively associated with milk lactoferrin content. Milk lactoferrin is less concentrated in milk of healthy and susceptible than potentially mastitic and chronic cows.

Exploiting machine learning methods with monthly routine milk recording data and climatic information to predict subclinical mastitis in Italian Mediterranean buffaloes

Mastitis has detrimental effects on the world's dairy industry, reducing animal health, milk production and quality, as well as income for farmers. In addition, consumers' growing interest in food safety and rational usage of antibiotics highlights the need to develop novel strategies to improve mastitis detection, prevention, and management. In the present study we applied machine learning (ML) analyses to predict presence or absence of subclinical mastitis in Italian Mediterranean buffaloes, exploiting information collected the previous month during routine milk recording procedures, as well as climatic data. The data set included 3,891 records of 1,038 buffaloes from 6 herds located in Basilicata Region (South Italy). Prediction models were developed using 4 different ML algorithms (Generalized Linear Model, Support Vector Machines, Random Forest, and Neural Network) and 2 data set splitting approaches for the creation of the training and test sets (by record or by animal ID number, always with 80% of the data used for model training and the remaining 20% for model testing). Support Vector Machine was the best method to predict high or low somatic cell count at the subsequent test-day record in the validation set, and therefore it was used to estimate the contribution of each feature to the best model. Independently from the data set splitting approach, the most important features were somatic cell score, differential somatic cell count, electrical conductivity, and milk production. Among climatic data, the most informative were temperature and relative humidity. When the data were split by animal ID, an improvement in models' predictive performance on the test set was observed, suggesting this as the most appropriate data splitting approach in data sets with repeated measures to avoid data leakage. According to different metrics, Neural Network was the best method for making predictions on the test set. Our findings confirmed the promising role of ML methods to improve prevention and surveillance of subclinical mastitis, exploiting the large amount of data currently available to identify animals that would possibly have high somatic cell count the subsequent month.

Herd and animal factors affect the variability of total and differential somatic cell count in bovine milk.

The aim of this study was to quantify some environmental (individual herds, herd productivity, milking system, and season) and animal factors [individual animals, breed, days in milk (DIM) and parity] on the variability of the log-10 transformation of somatic cell count (LSCC) and differential somatic cell count (DSCC) on individual bovine milk. A total of 159,360 test-day records related to milk production and composition were extracted from 12,849 Holstein-Friesian and 9,275 Simmental cows distributed across 223 herds. Herds were classified into high and low productivity, defined according to the average daily milk net energy output (DMEO) yielded by the cows. Data included daily milk yield (DYM; kg/d), milk fat, protein, lactose, SCC, and DSCC, and information on herds (i.e., productivity, milking system). The daily production of total and differential somatic cells in milk was calculated and then log-10 transformed, obtaining DLSCC and DLDSCC, respectively. Data were analyzed using a mixed model including the effects of individual herd, animal, repeated measurements intra animal as random, and herd productivity, milking system, season, breed, DIM, parity, DIM × parity, breed × season, DIM × milking system and parity × milking system as fixed factors. Herds with a high DMEO were characterized by a lower content of LSCC and DSCC, and higher DLSCC and DLDSCC, compared to the low DMEO herds. The association between milking system and somatic cell traits suggested that the use of the automatic milking systems would not allow for a rapid intervention on the cow, as evidenced by the higher content of all somatic cell traits compared to the other milking systems. Season was an important source of variation, as evidenced by high LSCC and DSCC content in milk during summer. Breed of cow had a large influence, with Holstein-Friesian having greater LSCC, DSCC, DLSCC, and DLDSCC compared to Simmental. With regard to DIM, the variability of LSCC was mostly related to that of DSCC, showing an increase from calving to the end of lactation, and suggesting the higher occurrence of chronic mastitis in cows toward the end of lactation. All the somatic cell traits increased across number of parities, possibly because older cows may have increased susceptibility to intramammary infections.

Blood neutrophil extracellular traps: a novel target for the assessment of mammary health in transition dairy cows

BackgroundMammary 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).ResultsEighty-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).ConclusionsThe 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.

Relationship between total and differential quarter somatic cell counts at dry-off and early lactation.

Mastitis is a most common disease of dairy cows and causes tremendous economic loss to the dairy industry worldwide. Somatic cell counts (SCC) reflect the inflammatory response to infections and is a metric used as key indicator in mastitis screening programs, typically within the framework of national milk recording schemes. Besides the determination of total SCC, the differentiation of cell types has been described to be beneficial for a more definite description of the actual udder health status of dairy cows. Differential somatic cell count (DSCC) represents the combined proportion of polymorphonuclear leukocytes (PMN) and lymphocytes expressed as a percentage of the total. The aim of this study was to investigate the relationship between SCC and differential somatic cell count (DSCC) in individual quarter milk samples collected at different time points: at dry-off, after calving and at the lactation peak. We used individual quarter data from farms representing the specialized production system of Parmigiano Reggiano cheese in Northern Italy. Average DSCC values ranged between 44.9% and 56.3%, with higher values (60.4%-72.1%) in milk samples with ≥ 1 million SCC/ml (where the proportion of samples with DSCC > 70% can be as high as 0.73). Moderate overall correlations between DSCC and log(SCC) were estimated (Pearson = 0.42, Spearman = 0.38), with a clear increasing trend with parity and around the lactation peak (e.g. Pearson = 0.59 at 60 DIM in parity 4). Taking SCC values as indicators of subclinical mastitis, DSCC would diagnose mastitis with 0.75 accuracy. Data editing criteria do have an impact on results, with stricter filtering leading to lower correlations between log(SCC) and DSCC. In conclusion DSCC and SCC provide different descriptions of the udder health status of dairy cows which, at least to some extent, are independent. DSCC alone doesn’t provide more accurate information than SCC at quarter level but, used in combination with SCC, can be of potential interest within the framework of milk recording programs, especially in the context of selective dry-cow therapy (SDCT). However, this needs further investigation and updated threshold values need to be selected and validated.

Association between differential somatic cell count and California Mastitis Test results in Holstein cattle

The California Mastitis Test (CMT) has been used to estimate total somatic cell count (SCC) levels in milk; however, milk with similar SCC levels occasionally shows inconsistent CMT results, which limits the use of the CMT. This observational study aimed to investigate how differential cell counts in milk influence the CMT in Holstein cattle through the novel parameters differential somatic cell count (DSCC) and macrophage proportion (MAC). We performed the CMT on d 0, 3, 5, 7, 14, and 21 after identifying mastitis, and simultaneously measured SCC, DSCC, and MAC at the quarter level. We followed 58 mastitis events occurring in 41 cows and obtained 307 quarter-level records after data cleaning. We transformed SCC to somatic cell score (SCS) and MAC to its logarithm to fit the normal distribution and analyzed the data using the cumulative logit mixed model. Results showed that both an increase in SCS (odds ratio: 3.66, 95% confidence interval: 2.89–4.64) and the logarithm of MAC (odds ratio: 4.35, 95% confidence interval: 1.91–9.91) can contribute to a higher CMT score. During the healing process of mastitis, MAC tends to increase as SCC decreases; thus, even samples with low SCC can cause positive CMT reactions. We recommend that practitioners avoid making treatment decisions based on the CMT alone. We also noted that the CMT is sensitive to subclinical mastitis with high MAC, hence it could be considered an alternative to detecting high MAC (chronic) mastitis.