High Somatic Cell Count Research Articles

Efficiency and somatic cell count: Unraveling Holstein cow productivity through stochastic frontier modeling

The yield and quality decrease due to high somatic cell counts caused by mastitis, and this also negatively affects the profitability, efficiency, and sustainability of dairy farms. The main objective of this study was to investigate the effects of somatic cell counts on yield, milk chemicals, and the technical efficiency of Holstein dairy cows. A total of 165 lactating cows were involved in the research, and all cows were fed the diets as a total mixed ration three times a day. Milk samples were collected each day during milking and analyzed for chemical composition and somatic cell counts (SCC). The daily milk production of each cow was obtained from the SCR herd management program, which is integrated with the parlor. In conclusion, it was determined that for each group, the efficiency scores, SCC, and milk yield of cows varied between 0.80 and 0.99, 322.000 and 557.857 cells/mL, and 33.13 and 48.90 Kg/d, respectively; they also differed significantly in each group. Considering the findings, milk production can be increased by 7% without changing any input. Additionally, every 1% decrease in SCC will increase the efficiency of milk production by 0.55%. Cows with low technical efficiency (TE) scores produced 2.87 kg/d/cow less milk compared to animals with high TE. Reducing the SCC of the group with a low TE (456.878 cells/mL) to a SCC of high TE (438.869 cells/mL) will increase milk yield by 2.87 kg/d/cow on average. In conclusion, minimizing losses due to mastitis is paramount for enhancing dairy farm efficiency. This research underscores the interplay between TE and udder health, providing a comprehensive understanding of individual cow performance. Addressing inefficiencies and promoting udder health can significantly contribute to sustainable and economically viable dairy farming practices. Keywords: Dairy farm; Mastitis; Somatic cell count; Stochastic frontier analysis; Technical efficiency

Impact of a teat disinfectant based on Lactococcus cremoris on the cow milk proteome

BackgroundDairy cow milking practices require cleaning and disinfection of the teat skin before and after milking to ensure the safety and quality of milk and prevent intramammary infections. Antimicrobial proteins of natural origin can be valuable alternatives to traditional disinfectants. In a recent field trial, we demonstrated that a teat dip based on a nisin A-producing Lactococcus cremoris (L) had comparable efficacy to conventional iodophor dip (C) in preventing dairy cow mastitis. Here, we present the differential shotgun proteomics investigation of the milk collected during the trial.MethodsFour groups of quarter milk samples with low (LSCC) and high somatic cell count (HSCC) collected at the beginning (T0) and end (TF) of the trial were analyzed for a total of 28 LSCC (14 LSCC T0 and 14 LSCC TF) and 12 HSCC (6 HSCC T0 and 6 HSCC TF) samples. Milk proteins were digested into peptides, separated by nanoHPLC, and analyzed by tandem mass spectrometry (LC-MS/MS) on an Orbitrap Fusion Tribrid mass spectrometer. The proteins were identified with MaxQuant and interaction networks of the differential proteins were investigated with STRING. The proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD045030.ResultsIn healthy milk (LSCC), we detected 90 and 80 differential proteins at T0 and TF, respectively. At TF, the Lactococcus group showed higher levels of antimicrobial proteins. In mastitis milk (HSCC), we detected 88 and 106 differential proteins at T0 and TF, respectively. In the Lactococcus group, 14 proteins with antimicrobial and immune defense functions were enriched at TF vs. 4 proteins at T0. Cathelicidins were among the most relevant enriched proteins. Western immunoblotting validation confirmed the differential abundance.ConclusionsAt T0, the proteomic differences observed in healthy milk of the two groups were most likely dependent on physiological variation. On the other hand, antimicrobial and immune defense functions were higher in the milk of cows with mammary gland inflammation of the Lactococcus-treated group. Among other factors, the immunostimulatory action of nisin A might be considered as a contributor.

Exploring Longitudinal Associations between Farmer Health and Wellbeing, and Dairy Herd Subclinical Mastitis Prevalence and Productivity: The HUNT Study, Norway

The health and wellbeing of farmers is one aspect of the sustainability of agriculture, as is the health and productivity of livestock. Previous research suggests a possible association between the 2, but the complexity of the relationships between farmers and their livestock can hamper research efforts. This observational study utilizes high quality existing survey and register data from a cohort of Norwegian dairy enterprises to explore possible longitudinal associations between farmer health and wellbeing (general health, chronic disease or condition, chronic pain, anxiety or depression symptoms, or poor life satisfaction) and changes in dairy herd udder health (estimated by the proportion of milk tests with a high somatic cell count (SCC%)) or milk yield (kilograms per cow-year equivalent). We found evidence of a general trend toward increased SCC% with poor farmer health or wellbeing. If the farmer had poor self-reported general health, the odds ratio (OR) for a one-year increase in SCC% at the enterprise was 1.51 (95% CI: 0.76,2.99, OR 1.80 (0.71, 4.51) after adjustment for possible effect modifiers) compared with those with good health, and for poor life satisfaction the OR was 1.50 (0.71,3.15) (2.61 (0.92,7.39) after adjustment). We also find evidence that enterprises where a farmer was experiencing poor health or wellbeing tended to have lower odds of a decrease in milk yield than other enterprises. The OR for a one-year decrease in milk yield with any chronic disease or condition was 0.42 (0.24,0.76), and with symptoms of anxiety or depression the OR was 0.56 (0.23,1.34). Potential confounders and effect modifiers are discussed and explored. These results suggest that further longitudinal research to unravel the relationships between farmer health and wellbeing, and livestock health and productivity is needed.

Assessment of mammary stress in early lactating crossbred (Alpine × Beetal) does during heat stress in conjunction with milk somatic cells and non-invasive indicators

Milk somatic cell counts (SCC) are crucial for monitoring dairy animals' mammary health and milk quality. This study was initiated to assess the mammary health of early lactating crossbred (Alpine × Beetal) does during periods of heat stress. The udder skin surface temperature (USST), total and differential milk SCC, milk composition, milk cortisol levels and phagocytic activity (PA) of milk neutrophils and macrophages were estimated in these does. Results indicated a significant (p<0.01) increase in the milk neutrophils to macrophages (N: M) ratio and milk cortisol levels with milk somatic cell score (SCS). The PA of milk neutrophils decreased significantly (p<0.01), while that of macrophages increased significantly (p<0.01) with higher SCC. On Pearson correlation, milk SCS revealed strong positive correlation (p<0.01) with N: M ratio (r=0.90), USST (r=0.98), milk cortisol (r=0.97), milk pH (r=0.83) and conductivity (r=0.92). Conversely, milk lactose exhibited a moderate negative correlation of (r= −0.48) with SCS. These findings suggest that USST infrared thermography and milk cortisol levels are promising non-invasive methods for rapidly assessing mammary stress in early lactating does during heat stress.

514 Roles of core regulatory noncoding RNAs in bovine Staphylococcus aureus subclinical mastitis

Abstract Staphylococcus aureus (S. aureus) is an opportunistic pathogen frequently associated with subclinical mastitis and accounts for a large proportion of the economic losses due to mastitis on Canadian dairy farms. Despite a plethora of investigations on the molecular mechanisms of mastitis, little information is available on the roles of regulatory noncoding RNAs (ncRNA). This study aimed to uncover the regulatory roles of microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) in the response to subclinical mastitis due to S. aureus of cows. Transcriptome sequencing (RNA-Seq and miRNA-Seq) was conducted on milk somatic cells from 13 cows with S. aureus subclinical mastitis [high somatic cell counts (SCC) of ≥350,000 cells/mL for ≥3 consecutive months and positive for S. aureus only], and 5 healthy cows (low SCC&amp;lt; 100,000 cells/mL for ≥3 consecutive months and negative for mastitis pathogens). RNASeq and smRNASeq nf-core bioinformatics pipelines were used to process the data. Results showed that 97 miRNAs (36 up-regulated and 61 down-regulated) and 1,565 lncRNAs (617 up-regulated and 948 down-regulated) were differentially expressed (DE; FDR&amp;lt; 0.05) between S. aureus subclinical mastitic cows and healthy controls. Competing endogenous networks comprising co-expressed differentially expressed mRNAs (DEGs), miRNAs (DEMs), and lncRNAs (DELs) were constructed. CytoHubba, a Cytoscape plugin, was utilized to identify the top hub regulatory ncRNAs including bta-miR-2387, bta-miR-331, bta-miR-95, bta-miR-744, bta-miR-455, bta-novel-miR-60, bta-miR-3533, bta-miR-500, bta-miR-1249 and bta-novel-miR-66). The results revealed that the main hubs (downregulated bta-miR-2387 and upregulated bta-miR-331) potentially regulate numerous DEGs during S. aureus subclinical mastitis such as NFKB1, IL10RA, IL17RA, TLR10, LOC112442665, LOC112448481 and LOC112442015, etc., and TUNAR, LOC112445429, LOC112444484 and LOC101907369, respectively. Functional analysis of bta-miR-331 target genes revealed roles in several KEGG pathways (e.g., tight junction, bacterial invasion of epithelial cells) and biological processes gene ontology (GO) terms (e.g., cell adhesion, cell locomotion and cell motility). Similarly, roles for bta-miR-2387 in KEGG pathways like JAK-STAT signaling pathway, NOD-like receptor signaling pathway and inflammatory bowel disease, and biological process GO terms such as inflammatory response, neutrophil migration, leucocyte migration and response to cytokine were revealed. MiR-331 has been found to promote cell proliferation, migration, and invasion of breast cancer. Furthermore, bta-miR-2387 has been shown to regulate several immune pathways which aid in improving the movement of spermatids across the epithelium and preleptotene spermatocytes across the blood-testis barrier during spermatogenesis. This supports our findings and suggests that bta-miR-2387 and bta-miR-331 could be pivotal regulators of immune-related genes and pathways during S. aureus subclinical mastitis. Thus, they hold potential as biomarkers for the development of therapeutic and diagnostic tools for managing subclinical mastitis.

Comparison of a machine learning model with a conventional rule-based selective dry cow therapy algorithm for detection of intramammary infections

We trained machine learning models to identify intramammary infections (IMI) in late lactation cows at dry-off to guide antibiotic treatment, and compared their performance to a rule-based algorithm that is currently used on dairy farms in the US. We conducted an observational test-characteristics study using a data set of 3,645 cows approaching dry-off from 68 US dairy herds. The outcome variables of interest were cow-level IMI caused by all pathogens, major pathogens, and Streptococcus and Strep-like organisms (SSLO), which were determined using aerobic culture of aseptic quarter-milk samples and identification of isolates using MALDI-TOF. Individual cow records were extracted from the farm software to create 53 feature variables at the cow and 39 at the herd-level which were derived from cow-level descriptive data, records of clinical mastitis events, results from routine testing of milk for volume and concentrations of somatic cell count (SCC), fat, and protein. ML algorithms evaluated were logistic regression, decision tree, random forest, light gradient-boosting machine, naïve bayes, and neural networks. For comparison, cows were also classified according to a conventional rule-based algorithm that considered a cow as high risk for IMI if she had at one or more high SCC (>200,000 cells/ml) tests or ≥2 cases of clinical mastitis during the lactation of enrollment. Area under the curve (AUC) and Youden's index were used to compare models, in addition to binary classification metrics, including sensitivity, specificity, and predictive values. ML models had slightly higher AUC and Youden's index values than the rule-based algorithm for all IMI outcomes of interest. However, these improvements in prediction accuracy were substantially less than what we had considered necessary for the technology to be a worthwhile alternative to the rule-based algorithm. Therefore, evidence is lacking to support the wholesale use of ML-guided selective dry cow therapy at the moment. We recommend that producers wanting to implement algorithm-guided SDCT use a rule-based method.

Fluctuations in somatic cell count and their impact on individual goat milk quality throughout lactation

The somatic cell count (SCC) is used by the dairy industry as an indicator of milk quality and udder health. However, in goats, its reliability is significantly masked by non-infectious variables such as the milk secretion process and physiological lactation changes. Additionally, notable individual variability between goats might exist. This study aimed to investigate fluctuations in SCC in individual goats during an entire lactation and to examine the relationship between SCC and milk parameters such as bacterial count and chemical composition. Individual milk samples from 40 Norwegian dairy goats from the University herd were collected monthly across an entire lactation including the pasture period. The goats were categorized based on SCC levels to analyze patterns in chemical components within these groups. Notably, goats exhibited increased SCC and decreased bacterial counts when moved to summer grazing pastures. At that stage milk samples from goats with the highest SCC (>2000 ×103 cells/mL) showed a distinct decrease in individual bacterial counts (IBC). Several milk components, especially lactose, protein, and various minerals were affected by the SCC level, as well as pH. These effects were further amplified when considering the interaction with the lactation stage, influencing a broader range of variables. The results presented in this study provide new insights into the SCC's effect on milk composition and its critical role. These insights underscore the necessity for establishing lactation-specific thresholds for interpretation, as well as for making adjustments in quality payments.

Effects of subclinical mastitis on automatic milking system data, hematological and biochemical parameters, and milk composition in Holstein cows.

Subclinical mastitis decreases milk production and quality, despite the normal appearance of the mammary glands and milk. Herein, we aimed to investigate changes in factors monitored via automatic milking systems (AMS) prior to subclinical mastitis onset and identify differences in hematological and biochemical parameters and milk composition at subclinical mastitis onset. Thirty-two Holstein cows were divided into two groups according to somatic cell counts (SCC) from AMS and milk composition analysis and the California mastitis test (CMT): healthy cows (controls [CON], n=16, SCC <500×103 cells/ml and negative for CMT) and cows with subclinical mastitis (SCM, n=16, SCC ≥500×103 cells/ml and positive for CMT). Eventually, 121 milk samples from the CON ([mCON], n=60) and SCM ([mSCM], n=61) groups were obtained; SCM samples were categorized as those from non-inflamed (mNQ) or subclinically-inflamed (mIQ) quarters. We evaluated AMS factors; hematological, biochemical, and milk composition parameters; and bacterial isolation. In cows with SCM, milk yield decreased and electrical conductivity (EC) changed before disease onset. Milk EC decreased in mNQ although increased in mIQ (p<0.05). The SCM group had higher globulin levels and lower basophil counts; albumin-to-globulin ratio; and total cholesterol, albumin, and blood urea nitrogen levels than the CON group (p<0.05). The mIQ group had higher SCC but lower levels of lactose and milk solids-not-fat than those in the mCON and mNQ groups (p<0.05). The mCON group had higher levels of milk non-protein nitrogen than the mNQ group (p<0.05). Opportunistic mastitis pathogens were isolated in the mIQ group. Changes in milk yield and EC measured using AMS occurred prior to subclinical mastitis, which may be associated with variation in basophil counts; albumin-to-globulin ratio; and total cholesterol, albumin, BUN, globulin, SCC, milk lactose, and milk solids-not-fat levels at disease onset. These findings provide new insights into early-stage subclinical mastitis.

Exploring probiotic potential and antimicrobial properties of lactic acid bacteria from cow's milk

One of the most important foods in the world is milk, due to its nutrients and composition. Milk also contains lactic acid bacteria (LABs), which can produce antimicrobial compounds and organic acids; an essential factor in developing different methods to control other bacteria. Another critical factor is that several species of LABs are considered potential probiotics, offering several health benefits. This study aimed to isolate, identify LABs, characterize the antimicrobial susceptibility profile, and evaluate the inhibition potential against Staphylococcus aureus (ATCC 25923), Listeria monocytogenes (ATCC 7644), Escherichia coli (ATCC 25922) and Salmonella Typhimurium (ATCC 14028); as well as verifying the acidification capacity, survival of simulated in vitro gastrointestinal conditions and identification of virulence genes and bacteriocin-producing genes. The study was carried out with LABs previously isolated from milk of cows with clinical mastitis, low and high (Somatic Cells Count) in herds from all regions of Brazil (South, Southeast, Central-West, North, and Northeast). One hundred and ten strains were selected for identification using the MALDI-TOF technique. The genera with the most significant spectrum of action were Lactococcus, Lactobacillus, and Pediococcus in the antimicrobial action test against pathogens, concluding that Lactobacillus showed resistance to several antibiotics. The analysis of antimicrobial action made it possible to verify the potential production of antimicrobial compounds by LABs against the primary pathogens of food origin. The most significant reduction in pH could be observed from 24 h to 48 h, ranging from 6.8 (0 h) to 4.74 (48 h) for Lactobacillus and Lactococcus. All isolates survived tolerance tests at pH=3.0 and bile salts (0.5 %), with survival rates ranging from 34.2 to 69.9 % at pH = 3.0 and above 82 % at 0.5 % bile salts. Lacotococcus and Lactobacillus isolates presented the plnEF and plnNC8 genes. This study contributes to the search for alternatives in the treatment of bacterial infections with a reduction in the use of antibiotics, which, due to the increase in bacterial resistance, is causing difficulties in treating diseases caused by these microorganisms. pH is an important parameter when it comes to food safety and conservation. Tolerance to acid stress and bile salts are important properties in evaluating probiotic potential and the ability to maintain high counts under these conditions. The presence of the plnEF and plnNC8 genes suggests bacteriocin production.

Key udder health parameters on dairy farms with an automated milking system

Despite the increasing use of automated milking systems (AMS), udder health performance of farms has only been limitedly quantified on farms with a milking robot. The objective of this study was to quantify udder health parameters (UHP) at test-day and at herd level on robotic farms using routinely collected somatic cell counts (SCC) from test-day data, and to study the correlations between these UHP. Additionally, the associations between UHP and season, year and farm size as well as the association between the daily milk production and the herd milk SCC were investigated. To this end, we collected SCC test-day data from 48 Belgian and Dutch farms participating in the milk recording of a local dairy herd improvement program. The UHP were calculated based on individual animal records at each test day over three years (2017 until 2019). These UHP included herd milk SCC, average heifer SCC, average cow SCC, percentage of high SCC, percentage of new high SCC, percentage of chronic high SCC, percentage of high SCC cows after calving, and percentage of recovery during the lactation. By fitting a linear mixed model effect for each of the different UHP, significant associations between the UHP and season, year and farm size were observed at test-day level. A poorer udder health was observed during summer compared to other seasons and generally, larger farms performed worse than smaller farms. Using a linear regression model, a significant negative correlation between herd average SCC and average daily milk production was found: an increase of 6,085 cells/mL was associated with a decrease of one kg of milk production. Finally, the cow-level UHP were averaged over all test days of the entire study period per farm to investigate the farm-level udder health performance and correlations between them. At the farm level, UHP analysis revealed most farms had poor udder health performance (i.e. the herd milk SCC, average heifer SCC, average cow SCC, percentage of new high and chronic high SCC were all higher than their target values) as proposed by Barkema et al. (2013). Both at the farm and at test day level, strong correlations were found among the UHP. Similar to studies on farms with conventional milking systems, a strong correlation between herd milk SCC and the percentage of new and chronic high SCC cows was found. This study gives new insights into udder health performance on farms with an AMS and provides veterinarians and other dairy industry stakeholders with concrete benchmarks for udder health performance on farms.

Mastitis challenges in Serbian dairy farming: a study on somatic cell counts and pathogen distribution

Mastitis is pressing concern for dairy herds due to its economic impact and potential health risks. Somatic cell counts (SCC), reflecting udder health, plays a crucial role in mastitis diagnosis. Current research explores the distribution of SCC and its correlation with various mastitis-causing pathogens in dairy farms in Serbia. The study analyzed 194 individual cow milk samples and microbiological testing was conducted under aseptic conditions to isolate and identify mastitis pathogens. The microscopic reference method was employed for assessing SCC in the milk samples. Among mastitis-associated isolates, bacteria were present in 28.87 %, yeast in 12.38 %, while in 5.15 % of milk samples, both bacteria and yeast were present. The relationship between SCC in various sample types (negative, bacteria-positive, yeast-positive, and samples with both bacteria and yeast) was noted. Importantly, samples with both bacteria and yeast presence had the highest SCC. While SCC is a valuable tool for monitoring udder health and the effectiveness of mastitis control programs, its response to specific pathogens is complex and doesn’t allow differentiation between pathogen types easily. This research highlights the challenges in distinguishing pathogen types based solely on SCC.

Presence of pathogen DNA in milk harvested from quarters is associated to changes in cows’ milk yield and composition

BackgroundIntramammary infection is the result of invasion and multiplication of microorganisms in the mammary gland and commonly leads to mastitis in dairy animals. Although much has been done to improve cows’ udder health, mastitis remains a significant and costly health issue for dairy farmers, especially if subclinical. In this study, quarter milk samples from clinically healthy cows were harvested to detect pathogens via quantitative PCR (qPCR) and evaluate changes in individual milk traits according to the number of quarters infected and the type of microorganism(s). A commercial qPCR kit was used for detection of Mycoplasma bovis, Mycoplasma spp., Staphylococcus aureus, coagulase-negative staphylococci (CNS), Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Prototheca spp., Escherichia coli, Klebsiella spp., Enterococcus spp. and Lactococcus lactis ssp. lactis. Quarter and pooled milk information of 383 Holstein, 132 Simmental, 129 Rendena, and 112 Jersey cows in 9 Italian single-breed herds was available.ResultsAmong the cows with pathogen(s) present in at least 1 quarter, CNS was the most commonly detected DNA, followed by Streptococcus uberis, Mycoplasma bovis, and Streptococcus agalactiae. Cows negative to qPCR were 206 and had the lowest milk somatic cell count. Viceversa, cows with DNA isolated in ≥ 3 quarters were those with the highest somatic cell count. Moreover, when major pathogens were isolated in ≥ 3 quarters, milk had the lowest casein index and lactose content. In animals with pathogen(s) DNA isolated, the extent with whom milk yield and major solids were impaired did not significantly differ between major and minor pathogens.ConclusionsThe effect of the number of affected quarters on the pool milk quality traits was investigated in clinically healthy cows using a commercial kit. Results remark the important negative effect of subclinical udder inflammations on milk yield and quality, but more efforts should be made to investigate the presence of untargeted microorganisms, as they may be potentially dangerous for cows. For a smarter use of antimicrobials, analysis of milk via qPCR is advisable – especially in cows at dry off - to identify quarters at high risk of inflammation and thus apply a targeted/tailored treatment.

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

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

A simulation study to assess the impact missing values on the performance of different statistical methods for analysis of binary repeated measures data with an additional hierarchical structure

The primary objective of the study was to assess the impact of missing values on the analy- sis of binary repeated measures data with an additional hierarchical structure. One motivat- ing example for the present study was records of high somatic cell counts in milk samples obtained by approximately monthly sampling throughout the lactations of cows in dairy herds. Random effects models with autocorrelated (ρ = 1, 0.9 or 0.5) subject-level ran- dom effects were behind the simulated data. In general, the settings of the simulation were chosen to reflect a real somatic cell count dataset (scc40), except that the within-cow time series length was set to 8-time points for each cow. The estimation procedures consid- ered were: Ordinary Logistic Regression (OLR), Alternating Logistic Regression (ALR), Weighted Generalized Estimating Equations (WGEE), Penalized Quasi Likelihood (PQL), Maximum likelihood via numerical integration (ML) and Bayesian Markov chain Monte Carlo (MCMC). Multiple scenarios of simulated incomplete datasets were considered and include: a scenario corresponded to a combination of missingness patterns present in the scc40 dataset (scc40 scenario) The remaining scenarios involved only drop-outs, and corre- sponded to either moderate or high percentages of values either missing at random (MAR) or not missing at random (NMAR), respectively. In the scc40 scenario, all estimation procedures except OLR performed well and produced estimates with small relative bias (generally less than 5%) for levels of missingness that roughly corresponded to the scc40 data. In MAR missingness scenarios, some biases were found for ALR, WGEE and PQL procedures, whereas the likelihood-based procedures were largely unaffected by the miss- ing values. In NMAR scenarios, all procedures experienced similar and strong biases in the time coefficient; however, fixed effects estimates at the subject and cluster levels were relatively unaffected. Journal of Statistical Research 2023, Vol 57, No.1-2, pp.35-67

A scoping review on associations between paratuberculosis and productivity in cattle.

Paratuberculosis (PTB), or Johne's disease, is a disease with worldwide distribution caused by Mycobacterium avium subsp. paratuberculosis (MAP) that leads to chronic enteritis, primarily in ruminants. Even subclinical infection significantly reduces the animals' performance, and consequences of the disease lead to high economic losses for the cattle industry. To estimate the economic burden of bovine PTB and to evaluate the benefits of a potential control program, accurate estimates of the production effects associated with the disease are required. Therefore, the aim of this scoping review was to provide a comprehensive overview of associations between MAP infection and production parameters in cattle. The studies were collected from three electronic databases. Of the total 1,605 identified studies, 1,432 did not meet the set criteria in the title and abstract screening and a further 106 were excluded during full-text review. Finally, data on 34 different production parameters were extracted from 67 publications. Results show that the magnitude of reported performance losses varies depending on several factors, such as the type of diagnostic test applied, disease status or number of lactations. Studies reported a reduction in milk yield, changes in milk quality (e.g., higher somatic cell count, lower amount of produced milk fat and protein), reduced fertility (e.g., prolonged calving interval and service period, higher abortion rate and calving difficulties), reduced weaning weight, slaughter weight and slaughter value, or a higher risk for mastitis. Results from the studies included in our review show a median decrease of milk yield per infected cow of -452 kg/lactation for raw and -405 kg/lactation for modeled data. Similarly, the amount of produced milk protein fell by a median of -14.41 kg/lactation for modeled data and the amount of produced milk fat by a median of -13.13 kg/lactation. The reviewed studies revealed a prolonged calving interval by around 30 days and a 1.5 to 3 times higher likeliness of culling per lactation in PTB positive animals. Results from this scoping review provide evidence-based inputs for the development of economic models aiming at the estimation of the costs and benefits associated with different disease control scenarios for PTB.

Transcriptomic Changes and Regulatory Networks Associated with Resistance to Mastitis in Xinjiang Brown Cattle.

Xinjiang brown cattle are highly resistant to disease and tolerant of roughage feeding. The identification of genes regulating mastitis resistance in Xinjiang brown cattle is a novel means of genetic improvement. In this study, the blood levels of IL-1β, IL-6, IL-10, TNF-α, and TGF-β in Xinjiang brown cattle with high and low somatic cell counts (SCCs) were investigated, showing that cytokine levels were higher in cattle with high SCCs. The peripheral blood transcriptomic profiles of healthy and mastitis-affected cattle were constructed by RNA-seq. Differential expression analysis identified 1632 differentially expressed mRNAs (DE-mRNAs), 1757 differentially expressed lncRNAs (DE-lncRNAs), and 23 differentially expressed circRNAs (DE-circRNAs), which were found to be enriched in key pathways such as PI3K/Akt, focal adhesion, and ECM-receptor interactions. Finally, ceRNA interaction networks were constructed using the differentially expressed genes and ceRNAs. It was found that keynote genes or mRNAs were also enriched in pathways such as PI3K-Akt, cholinergic synapses, cell adhesion molecules, ion binding, cytokine receptor activity, and peptide receptor activity, suggesting that the key genes and ncRNAs in the network may play an important role in the regulation of bovine mastitis.

Characterisation of probiotic fermented milks produced from goat milk containing low or high somatic cell counts

Probiotic fermented milks were produced from goat milk containing two different somatic cell counts (SCC) at low (&lt;500.000 cells/mL) and high (&gt;1.500.000 cells/mL) levels. The produced samples were analysed for some chemical, textural, microbiological and sensory properties. The samples made from high SCC goat milk had higher lipolytic activity. The viscosity and hardness of probiotic fermented milks with low SCC were higher than that of the higher SCC samples. Probiotic fermented milks produced with low SCC achieved higher sensory scores and the viability of the starter bacteria was more stable in these samples.

A study on the development and the application strategy of FT-MIRS-based models for the diagnosis of subclinical mastitis and milk quality classification in buffaloes

Buffalo mastitis detection and buffalo milk quality are of great importance to the world dairy industry. Somatic cell count (SCC) can be employed to assess mammary gland health and milk quality in dairy cows, but SCC detection is costly, and the detection instrument is expensive. The purpose of this study was to develop high and low SCC identification models using Fourier-transform mid-infrared spectrum (FT-MIRS), which could be used to diagnose subclinical mastitis (SCM) in buffalo and to determine the milk quality grade at a low cost. The dataset contained 899 buffalo milk samples collected from two regions in China. Firstly, the samples were divided into positive group above the threshold (SCM or unqualified milk) and negative group below the threshold (healthy or qualified milk) with SCC = 200 × 103 cells/mL (SCM in buffalo), 400 × 103 cells/mL (EU standard for raw cow milk, ES), 500 × 103 cells/mL (Indian standard for raw buffalo milk, IS), and 750 × 103 cells/mL (US standard for raw cow milk, US) as thresholds, respectively. Then, with FT-MIRS as predictive variables, predictive models were developed using Partial Least Squares Discriminant Analysis (PLSDA), Random Forest (RF), and Gradient Boosting Machine (GBM). The main results were as follows: the AUCval of the diagnostic model of SCM in buffalo using SCM criteria as a threshold was 0.84 (PLSDA). The AUCval values of the three buffalo milk quality classification models with ES, IS, and US as thresholds were 0.76 (PLSDA), 0.78 (GBM), and 0.84 (PLSDA), respectively. The predictive models established in this paper had a weak predictive ability for positive samples, and the “stepwise discriminant analysis” was recommended to improve the model application effect: The models were applied to classify samples as positive and negative, and then the samples with higher predictive probability were selected. Finally, the remaining samples were further differentiated using the reference methods of SCC detection. To conclude, FT-MIRS has the potential to predict buffalo mammary gland health status and buffalo milk quality grade, reduce the cost of SCC detection, and improve work efficiency, which will lay the foundation for rapid buffalo milk quality classification by raw milk regulatory authorities and rapid diagnosis of SCM in buffalo by farms.

Concentrations of antimicrobial components in milk at dry off and postpartum and their relationships to new high somatic cell counts at quarter level in dairy cows.

We investigated the antimicrobial components in cow milk at dry off and postpartum and their contribution in preventing new high SCC at quarter level. Milk samples from 72 quarters of 19 lactating cows were collected at last milking before dry off and at 7 d after parturition. Milk yield of each cow was recorded and SCC, IgG, IgA, lactoferrin, lingual antimicrobial peptide (LAP), and S100A7 concentrations in each quarter milk sample were measured. The postpartum milk yield was significantly higher than that at dry off. The IgG, IgA and lactoferrin concentrations in milk at dry off were significantly higher than those at postpartum, whereas the LAP concentration was lower. Quarters with SCC < 300 000 cells/ml at both dry off and postpartum were classified as persistent low SCC (PL) whereas those that rose above that same threshold postpartum were classified as new high SCC (NH). At dry off, IgG and LAP concentrations in milk were significantly higher in PL than in NH. These results suggest that high LAP concentrations during the dry period may contribute toward the prevention of new high SCC.

Mastitis impact on high-yielding dairy farm's reproduction and net present value.

Poor udder health can have a negative impact on milk production and reproductive performance, which reduces the net present value (NPV) of dairy farms. The aim of this study is therefore to investigate the relationship between clinical mastitis and NPV and the financial impact of impaired reproductive function. For this purpose, 473 dairy cows were included in our study, 146 cows with clinical mastitis (CM group) and 327 clinically healthy cows (CH group) from a high-yielding dairy farm in Romania, milking approximately 780 dairy cows with an average milk production of 46 kg milk/day. We found that, in contrast to CH cows, CM cows had a significantly lower conception rate at first service (58.2% vs. 41.7%, p < 0.05), third service (45.3% vs. 30.2%, p < 0.05), and total services (49.2% vs. 36.4%, p < 0.05). However, this positive effect was not observed for the average days open, which were significantly lower in CM cows than in CH cows (112 ± 4.3 days vs. 142 ± 3.1 days, p < 0.05). The fact that the non-pregnant CH cows had higher somatic cell counts (>400,000 SCC/mL) in their milk around artificial insemination (AI) and 1 month earlier than the pregnant cows (<250,000 SCC/ml) supports the idea that poor uterine health affects the reproductive activity of high-yielding cows. However, by using the UW-DairyRepro$ decision support tool, we found that despite the impairment of reproductive function in dairy cows, the largest negative impacts on NPV are still the cost of milk loss (US$14,439.4/farm/year) and treatment costs (US$4,380/farm/year). We considered the costs associated with poor reproductive function in the CM group (US$3,577/farm/year) as an additional cost of mastitis. Finally, it appears that the impact of mastitis on reproduction is associated with a lower chance of conception than it is with a daily risk of services.