Green synthesized silver nanoparticles from Ocimum sanctum: A potent inhibitor of biofilm forming ability and efflux pumps in bacteria causing bovine mastitis.

Therapeutic management of mastitis faces significant challenges due to multidrug resistance. In the present study, multi-drug-resistant (MDR) Staphylococcus spp, Klebsiella pneumoniae, and Escherichia coli were isolated from bovine clinical mastitis cases and the phenotypic and genotypic multidrug resistance profiling was carried out. Silver nanoparticles (AgNPs) were biosynthesized using Ocimum sanctum leaf extracts and characterized via UV Vis absorption, Fourier Transform Infrared Spectroscopy, X-ray diffraction studies, Energy dispersive spectroscopy and Electron Microscopy. The determined minimum inhibitory concentration and minimum bactericidal concentration of the AgNPs against the recovered MDR isolates were 62.5 µg/ml and 125 µg/ml respectively. At a concentration of 50 µg/ml, the AgNPs demonstrated biofilm inhibitory activities of 80.35 % for MDR E. coli, 71.29 % for S. aureus and 60.18 % for MDR K. pneumoniae. Post-treatment observations revealed notable differences in biofilm formation across bacterial isolates. Furthermore, AgNP treatment led to significant downregulation of expression of the efflux pump genes acrB, acrE, acrF, and emrB in Gram-negative isolates and norB in Staphylococci isolates. This research underscores the potential for the development of an eco-friendly antimicrobial alternative in the form of green synthesized silver nanoparticles to combat drug resistance offering potential antibiofilm and efflux pump inhibitory activities.

Genetic evaluation of udder health traits in the Spanish Holstein Cows.

The aims of this study were to estimate the genetic parameters of clinical mastitis (CM) and somatic cell score (SCS) traits, and to compare the performance of genetic evaluations of CM traits using univariate and bivariate analyses (CM-SCS). Data was edited according to the Udder Health Golden Standard harmonization and then, 6 CM traits and 6 SCS traits were considered, as the result of combining 3 lactation classification (1, 2, ≥ 3) and 2 milking periods (early, late). The linear mixed animal models included the ratio of period at risk as a covariate, herd-year of calving, month of calving, and lactation-age as fixed effects, and the permanent environmental effect for traits of ≥ 3 lactations. Prevalence of CM in early lactation was similar regardless the lactation number (5-6%) and the estimated heritabilities were 0.01. Prevalences in late lactation ranged from 10% to 24% and heritabilities ranged from 0.03 to 0.05. Estimated heritabilities of SCS ranged from 0.06 to 0.16 with univariate analyses. Somatic cell count (therefore its log-transformation SCS) showed higher probability of identify correctly healthy cows than infected cows but there was still up to 36% of healthy cows for CM not detected by SCS. Genetic correlations between CM-SCS traits ranged from 0.36 to 0.95, and SCS in lactation 3 and later did not add extra information to SCS in second lactation for predicting CM. Regarding reliabilities of estimated breeding value (EBV) for CM traits, bivariate CM-SCS analyses led to substantial increases with respect to the single-trait model for sires (7-12% more in first lactation and 16-28% more for second lactation). Sire's rank correlations for CM between univariate and bivariate analyses (0.47-0.92) suggest that discarding sires could be more accurate than selecting candidates for sires of dams. We can conclude that SCS in first lactation could be useful to supplement CM data in first and second lactations to improve udder health genetic evaluation.

Selective treatment of clinical mastitis: Assessment of the Net Cash Impact on Dairy Farms Under Diverse Scenarios. A European Perspective.

Selective treatment of clinical mastitis (STCM) potentially reduces antimicrobial use without negative implications on cow's milk production or health. However, this approach comes with additional costs. The aim of this study was to evaluate the net cash impact (NCI) of implementing STCM compared with blanket treatment of clinical mastitis (BTCM) under different diagnostic-test turnaround times (24 h, 14 h, and 8 h) using a stochastic partial budget analysis with Monte Carlo simulation. The target population was European commercial dairy herds; therefore, the model inputs were primarily from European sources. Additionally, variables associated with dairy management programs were obtained from USDA sources, worldwide multisite clinical trials, and expert opinion. The output was calculated by subtracting the cost of STCM from the cost of BTCM and it represented the expected NCI if a herd switched from BTCM to STCM. Depending on the time-to-treatment efficiency and diagnostic-test turnaround time, the expected mean NCI, assuming that STCM has no impact on the cow's future health or production, ranged from +€8.7 to +€12.4 per case with 72.4% to 84.8% of the iterations being ≥ €0. Moreover, using the numerically favorable health and production effects of STCM reported in the literature, the expected mean NCI ranged from +€44.5 to +€48.1 per case with 93.6% to 95.4% of the iterations being ≥ €0. The variables with the greatest contribution to NCI variance were proportion of gram-positive cases (39.2% of the variance) and days out of the tank for treated cows (22.0%). However, if future cow's health and production were accounted for, culling risk (24.6%), recurrence risk (19.4%), and milk yield (10.6%) would have the greatest contribution to NCI. The sensitivity analysis indicated that farms with high clinical mastitis incidence, low proportion of gram-positive cases, large number of days out of the tank for treated cows, higher milking frequency or using automatic milking systems, not using the highest priced diagnostic tests, and having high antimicrobial treatment costs are the best candidates for STCM. Improving time-to-treatment efficiency, for example, by using a rapid diagnostic test, leads to a favorable NCI, while high daily milk yield and milk price enhances the NCI in already positive scenarios. Finally, the cash flow entirely depends on future cow's health and milk yield. In conclusion, results indicate that overall, STCM is a practice that positively impacts the NCI of many herds.

Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations.

The standard single-step genomic prediction model assumes that all SNP markers explain an equal amount of genetic variance, which, however, may not be true. This is because SNPs are located in or near different genes with different functions. Therefore, it seems logical to consider SNP marker-specific weights when predicting genomic breeding values. We hypothesized that allowing differences in the amount of genetic variance explained by each SNP marker will improve prediction reliability and response to selection. To investigate this hypothesis, we first developed multi-trait standard single-step genomic models based on the current multi-trait random regression evaluation models for udder health traits of the Nordic Red (RDC) and Jersey (JER) dairy cattle populations. The models included 4 clinical mastitis (CM) traits, 3 test-day somatic cell score (SCS) traits, and the conformation traits fore udder attachment and udder depth. In the second step, we investigated the effect of applying different SNP marker weighting scenarios in the single-step genomic prediction models, for which a single-step SNP best linear unbiased prediction model was applied. We investigated the prediction reliability of the different models by forward prediction, where the last 4 years of the data were removed to estimate breeding values for validation candidates. In addition, genetic trends of the pedigree-based estimated breeding values (PEBV) and genomic enhanced breeding values (GEBV) were examined. The data sets for RDC and JER included 6.9 and 1.2 million animals of which 5.6 and 0.9 million cows had records, respectively. The number of genotyped animals was 125,789 and 64,777 for RDC and JER, respectively. Cows had repeated SCS observations but only single observations for all other traits and breeding values for all traits were modeled by one covariance function. This required modeling 12 eigenvalue breeding value coefficients for each cow and developing SNP marker weights for the principal components rather than for the biological traits. We investigated 3 SNP marker weighting scenarios: 1) a nonlinear method similar to BayesA, 2) using the classical formula 2pqû2 that accounts for allele heterozygosity, and 3) applying a mean SNP weight calculated by 2pqû2 for every 20 adjacent SNP markers. Bias, dispersion, and prediction reliability were calculated using PEBV or GEBV from the evaluation based on the full data set on those using the reduced data set. We found that the recent favorable genetic trend in CM and SCS has been accelerated since the introduction of genomic selection. The study also shows that a significant increase in prediction reliability, i.e., 0.74 vs. 0.48 for RDC and 0.72 vs. 0.41 for JER cows for CM, can be achieved with a standard single-step genomic prediction model compared with a pedigree-based prediction model. Almost all scenarios with SNP marker weighting further improved the prediction reliability between 0.5% and 12.7%. The highest improvement was achieved by weighing the SNP markers based on the 2pqû2 formula.

Advancing mastitis assessment in dairy bovines via short milking tube thermography: A seasonal perspective.

In India, where dairy production leads globally, infrared thermography (IRT) and short milking tube thermography specifically are vital for managing mastitis. Therefore, the present study focuses on thermal imaging of the udder and short milking tube (SMT) of the milking machine during the peak milking process of Sahiwal cows and Murrah buffaloes during winter, summer, rainy and autumn seasons to identify sub-clinical (SCM) and clinical mastitis (CM) cases using the Darvi DTL007 camera. The udder health was assessed using the California Mastitis Test, Somatic Cell Count (SCC) and IRT throughout the year. Log10SCC and thermogram analysis revealed a difference (p < 0.01) between healthy, SCM, and CM cases during different seasons in both breeds. Further results showed an increase (p < 0.01) in SMT thermograms of SCM and CM cases compared to healthy quarters in Sahiwal cows during winter, summer, rainy, and autumn were 4.26 and 7.51, 2.37 and 4.47, 2.20 and 3.64, 2.90 and 4.94 ºC, respectively and for Murrah buffaloes were 3.56 and 5.55, 2.70 and 3.81, 1.72 and 3.10, 3.14 and 4.42ºC, respectively. The highest degree of increase in milking udder skin surface temperature and SMT of SCM and CM cases compared to healthy quarters was observed during the winter and the least during the rainy season. Thus, regardless of the seasons examined in this study, SMT thermograms effectively assessed SCM and CM.

Seasonal assessment of mastitis using thermogram analysis in murrah buffaloes.

Mastitis is a global threat that challenges dairy farmers' economies worldwide. Sub-clinical mastitis (SCM) beholds the lion's share in it, as its visible clinical signs are not evident and are challenging to diagnose. The treatment of intramammary infection (IMI) demands antimicrobial therapy and subsequent milk withdrawal for a week or two. This context requires a non-invasive diagnostic tool like infrared thermography (IRT) to identify mastitis. It can form the basis of precision dairy farming. Therefore, the present study focuses on thermal imaging of the udder and teat quarters of Murrah buffaloes during different seasons to identify SCM and clinical mastitis (CM) cases using the Darvi DTL007 camera. A total of 30-45 lactating Murrah buffalo cows were screened out using IRT regularly throughout the year 2021-22. The IMI was further screened using the California mastitis test. The thermogram analysis revealed a significant difference (p < 0.01) in the mean values of the udder and teat skin surface temperature of Murrah buffaloes between healthy, SCM, and CM during different seasons. The mean values of udder skin surface temperature (USST) during different seasons ranged between 30.28 and 36.81 °C, 32.54 to 38.61 °C, and 34.32 to 40.02 °C among healthy, SCM, and CM-affected quarters. Correspondingly, the mean values of teat skin surface temperature (TSST) were 30.52 to 35.96 °C, 32.92 to 37.55 °C, and 34.51 to 39.05 °C, respectively. Further results revealed an increase (p < 0.01) in the mean values of USST during winter, summer, rainy, and autumn as 2.26, 4.04; 2.19, 3.35; 1.80, 3.21; and 1.45, 2.64 °C and TSST as 2.40, 3.99; 2.28, 3.26; 1.59, 3.09; and 1.68, 2.92 °C of SCM, CM-affected quarters to healthy quarters, respectively. The highest incidence of SCM was observed during autumn and CM during winter. Henceforth, irrespective of the seasons studied in the present study, IRT is an efficient, supportive tool for the early identification of SCM.

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem.

The serious drawback underlying the biological annotation of whole-genome sequence data is the p >> n problem, which means that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). We propose a way to circumvent the problem by combining a LASSO logistic regression with deep learning to classify cows as susceptible or resistant to mastitis, based on single nucleotide polymorphism (SNP) genotypes. Among several architectures, the one with 204,642 SNPs was selected as the best. This architecture was composed of two layers with, respectively, 7 and 46 units per layer implementing respective drop-out rates of 0.210 and 0.358. The classification of the test data resulted in AUC = 0.750, accuracy = 0.650, sensitivity = 0.600, and specificity = 0.700. Significant SNPs were selected based on the SHapley Additive exPlanation (SHAP). As a final result, one GO term related to the biological process and thirteen GO terms related to molecular function were significantly enriched in the gene set that corresponded to the significant SNPs. Our findings revealed that the optimal approach can correctly predict susceptibility or resistance status for approximately 65% of cows. Genes marked by the most significant SNPs are related to the immune response and protein synthesis.

An observational study of farmer-reported clinical mastitis in New Zealand dairy ewes.

AIMS: To describe the incidence, aetiology, treatment, and outcomes of farmer-reported clinical mastitis on New Zealand dairy sheep farms. METHODS: A prospective cohort study was conducted on 20 spring-lambing New Zealand sheep milking farms over the 2022-2023 season. Clinical mastitis was defined as a change in the appearance of milk and/or signs of inflammation in the gland. Farmers were required to report all cases of clinical mastitis and collect information on affected ewes' demographics, clinical features, treatments (where applicable), and outcomes. Milk samples from mastitic glands were submitted for microbiological culture and identification by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF). RESULTS: Partial or complete clinical mastitis data were available for 236 cases from 221 ewes on 18/20 study farms. Clinical mastitis was diagnosed in 0-6% of ewes at the farm level, with an overall incidence of 1.8 (95% CI = 1.0-3.2)% using the study data, or 2.3 (95% CI = 1.6-3.3)% using the study data and farmer estimates that included unreported cases. Cases occurred mostly in early lactation, with 59% detected during the lambing period (August-October), at a median of 7 (IQR 3, 40) days in milk. The majority of cases featured clots in the milk (59%), swelling (55%), and unevenness (71%) of the glands. Pyrexia (rectal temperature ≥ 40.0°C) was diagnosed in 25% of cases and depression (lethargy, inappetence, or inability to stand) in 26% of cases. Treatment was given to 46% of cases, with tylosin being the most commonly used treatment (50% of treated cases). The most common outcome was immediate drying off to be culled without treatment (32%), followed by still milking and recovered but with lasting problems (25%). Nearly half of all the milk samples submitted were culture negative. Streptococcus uberis (14%), non-aureus staphylococci (12%), and Staphylococcus aureus (11%) were the most common isolates, found on 12, 8 and 8 of the 16 farms with microbiological data, respectively. CONCLUSIONS: Clinical mastitis affected up to 6% of ewes at the farm level. Systemic signs were observed in one quarter of affected ewes, suggesting a role for supportive treatment. Clinical mastitis can be severe and challenging to fully resolve in New Zealand dairy sheep. CLINICAL RELEVANCE: This is the first systematic study of clinical mastitis in New Zealand dairy ewes. It provides baseline information specific to New Zealand conditions for farmers, veterinarians, and other advisors to guide the management of mastitis for the relatively new dairy sheep industry in New Zealand.

Genomic clues of association between clinical mastitis and SNPs identified by ddRAD sequencing in Murrah buffaloes.

The total milk production of India is 209.96 MT out of which 45% is contributed by the indigenous buffalo and due to their high producing virtue, the prevalence of mastitis is 5-20%. Despite the increasing level of technological advancement, mastitis is still an issue of concern for dairy industry in India as well as across the world. Therefore, the present study aimed to identify the SNPs and associate them with the incidence of clinical mastitis in Murrah buffalo using the ddRAD sequencing approach taking mastitis incidence data of 96 Murrah buffaloes. A total of 246 million quality controlled reads were obtained with an average alignment rate of 99.01% and at a read depth of 10, quality controlled SNPs obtained were 18,056. The logistic regression model was used and a total of seven SNPs were found significantly associated (p < 0.001) with mastitis incidence and seven genes were identified viz., NCBP1, FOXN3, TPK1, XYLT2, CPXM2, HERC1, and OPCML. The majority of them were having tumor suppressing action, related to immunogenetics or glycolytic and energy production. Conclusively, the SNPs identified in this study may be useful for future studies on mastitis incidence in Murrah buffalo and the SNP associations can be further validated.

Allelic diversity at BoLA DRB3 locus and association with predisposition to clinical mastitis in indicus and crossbred cattle.

Bovine lymphocyte antigen (BoLA) DRB3 locus in healthy and mastitis affected cattle has been genotyped by a polymerase chain reaction and restriction fragment length polymorphisms (PCR-RLFP) using RsaI restriction enzyme, followed by sequencing. In 130 farm animals, 25 BoLA DRB3 alleles have been detected by PCR-RFLP. Three distinct allelic patterns significantly associated with mastitis in Karan Fries crossbred and Sahiwal indicus cattle have been identified, whereas, four other allelic patterns were significantly high in frequency among healthy animals. Sequencing of RFLP genotypes revealed 25 and 47 alleles among healthy Sahiwal and Karan Fries, respectively, while 17 and 38 patterns observed in mastitis affected Sahiwal and Karan Fries animals, respectively. From Tajima's D-test of neutrality, it was concluded that alleles associated with mastitis were expanding in the population, whereas those of healthy were under contraction. Phylogenetic analysis carried out to delineate the evolutionary relationship of the farm and field animals at DRB3 locus, differentiating allelic patterns into six different clusters. Among the phylogenetic lineages, five patterns DRB3*028:01, DRB3*011:03, DRB3*031:01, DRB3*001:01 and DRB3*043:01, were previously reported, whereas one novel allelic variant was observed in indicus and crossbred cattle. This information will help in further exploring the association between BoLA-DRB3 genetic diversity and disease resistance in distinct cattle breeds, important in designing breeding strategies for increasing the distribution of favorable alleles.

Variation in partial direct costs of treating clinical mastitis among 37 Wisconsin dairy farms.

The objective of this observational study was to describe variations in partial direct costs of clinical mastitis (CM) treatments among 37 dairy herds using data obtained from herd management records. Animal health and drug purchase records were retrospectively collected from 37 Wisconsin dairy herds for a period of 1 yr. Each farm was visited to verify case definitions, recording accuracy, and detection criteria of CM cases. Descriptive statistics were used to summarize cost of drugs and milk discard. Differences in costs among protocols, intramammary (IMM) products, parities, days in milk, and recurrence were analyzed using ANOVA. Of 20,625 cases of CM, 31% did not receive antimicrobial treatment. The average cost of drugs and milk discard (including cases that were not treated) was $192.36 ± 8.90 (mean ± SE) per case and ranged among farms from $118.13 to $337.25. For CM cases treated only with IMM antimicrobials, milk discard accounted for 87% of total costs and was highly influenced by duration of therapy. Differences in costs were observed among parities, recurrence, and stage of lactation at case detection. Eight different treatment protocols were observed, but 64% of cases were treated using only IMM antimicrobials. Treatment costs varied among protocols; however, cases treated using both IMM and injectable antimicrobials as well as supportive therapy had the greatest costs as they were also treated for the longest duration. Ceftiofur was used for 82% of cases that received IMM antimicrobials while ampicillin was used for 51% of cases treated using injectable antimicrobials. With the exception of ceftiofur and pirlimycin IMM products, many IMM products were given for durations that exceeded the maximum labeled duration. For cases treated using only IMM therapy, as compared with observed costs, we estimated that partial direct costs could be reduced by $65.20 per case if the minimum labeled durations were used. Overall, partial direct costs per case varied among herds, cow factors, and treatment protocols and were highly influenced by the duration of therapy.

Frequency of non-aureus staphylococci and mammaliicocci species isolated from quarter clinical mastitis: a six-year retrospective study.

Non-aureus staphylococci and mammaliicocci (NASM) are the most frequently isolated bacterial group from bovine milk samples. Most studies focus on subclinical mastitis caused by NASM, however NASM can cause clinical mastitis (CM) as well. We evaluated retrospective data from 6 years (2017-2022) to determine the species and frequency of NASM isolated from quarter bovine CM. The data comprised of microbiological results from quarter CM samples routinely submitted to Quality Milk Production Services (QMPS), Cornell University, NY, US, for microbial identification by MALDI-TOF MS. A total of 9,909 microbiological results from 410 dairy herds were evaluated. Our results showed that 29 distinct NASM species were identified, with the 8 most prevalent NASM species being Staphylococcus chromogenes, S. haemolyticus, S. simulans, S. epidermidis, S. sciuri (now Mammaliicoccus sciuri), S. agnetis/S. hyicus, S. borealis, and S. xylosus. The NASM distribution remained similar among seasons, but the frequency of NASM CM cases was higher during the summer. Our results showed different patterns of variations in the isolation frequency over time, depending on the bacterial species: increasing or decreasing trends, cyclic fluctuations, and except for S. borealis, a significant seasonality effect for our study's most prevalent NASM was observed. This study showed that S. chromogenes remains the most frequent (43%) NASM species identified from bovine CM, followed by S. haemolyticus (18%), and S. simulans (12%).

Selective treatment of nonsevere clinical mastitis does not adversely affect cure, somatic cell count, milk yield, recurrence, or culling: A systematic review and meta-analysis.

Treatment of clinical mastitis (CM) contributes to antimicrobial use on dairy farms. Selective treatment of CM based on bacterial diagnosis can reduce antimicrobial use, as not all cases of CM will benefit from antimicrobial treatment, e.g., mild and moderate gram-negative infections. However, impacts of selective CM treatment on udder health and culling are not fully understood. A systematic search identified 13 studies that compared selective versus blanket CM treatment protocols. Reported outcomes were synthesized with random-effects models and presented as risk ratios or mean differences. Selective CM treatment protocol was not inferior to blanket CM treatment protocol for the outcome bacteriological cure. Noninferiority margins could not be established for the outcomes clinical cure, new intramammary infection, somatic cell count, milk yield, recurrence, or culling. However, no differences were detected between selective and blanket CM treatment protocols using traditional analyses, apart from a not clinically relevant increase in interval from treatment to clinical cure (0.4 d) in the selective group and higher proportion of clinical cure at 14 d in the selective group. The latter occurred in studies co-administering nonsteroidal anti-inflammatories only in the selective group. Bias could not be ruled out in most studies due to suboptimal randomization, although this would likely only affect subjective outcomes such as clinical cure. Hence, findings were supported by a high or moderate certainty of evidence for all outcome measures except clinical cure. In conclusion, this review supported the assertion that a selective CM treatment protocol can be adopted without adversely influencing bacteriological and clinical cure, somatic cell count, milk yield, and incidence of recurrence or culling.

Evaluation of reticuloruminal temperature for the prediction of clinical mastitis in dairy cows challenged with Streptococcus uberis.

Automated monitoring devices have become increasingly utilized in the dairy industry, especially for monitoring or predicting disease status. While multiple automated monitoring devices have been developed for the prediction of clinical mastitis (CM), limitations in performance or applicability remain. The aims of this study were to (1) detect variations in reticuloruminal temperature (RRT) relative to an experimental intramammary challenge with Streptococcus uberis and (2) evaluate alerts generated automatically based on variation in RRT to predict initial signs of CM in the challenged cows based on severity of clinical signs and the concentration of bacteria (cfu/mL) in the infected quarter separately. Clinically healthy Holstein cows without a history of CM in the 60 d before the experiment (n = 37, parity 1 to 5, ≥120 d in milk) were included if they were microbiologically negative and had a somatic cell count under 200,000 cells/mL based on screening of quarter milk samples 1 wk before challenge. Each cow received an intra-reticuloruminal automated monitoring device before the trial and was challenged with 2,000 cfu of Strep. uberis 0140J in 1 rear quarter. Based on interrupted time series analysis, intramammary challenge with Strep. uberis increased RRT by 0.54°C [95% confidence interval (CI): 0.41, 0.66] at 24 h after the challenge, which remained elevated until the end of the study. Alerts based on RRT correctly classified 78.3% (95% CI: 65.8, 87.9) of first occurrences of CM at least 24 h in advance, with a sensitivity of 70.0% (95% CI: 50.6, 85.3) and a specificity of 86.7% (95% CI: 69.3, 96.2). The accuracy of CM for a given severity score was 90.9% (95% CI: 70.8, 98.9) for mild cases, 85.2% (95% CI: 72.9, 93.4) for moderate cases, and 92.9% (95% CI: 66.1, 99.8) for severe cases. Test characteristics of the RRT alerts to predict initial signs of CM improved substantially after bacterial count in the challenged quarter reached 5.0 log10 cfu/mL, reaching a sensitivity of 73.5% (95% CI: 55.6, 87.1) and a specificity of 87.5% (95% CI: 71.0, 96.5). Overall, the results of this study indicated that RRT was affected by the intramammary challenge with Strep. uberis and the RRT-generated alerts had similar accuracy as reported for other sensors and algorithms. Further research that includes natural infections with other pathogens as well as different variations in RRT to determine CM status is warranted.

Invited review: Selective treatment of clinical mastitis in dairy cattle.

Treatment of clinical mastitis (CM) and use of antimicrobials for dry cow therapy are responsible for the majority of animal-defined daily doses of antimicrobial use (AMU) on dairy farms. However, advancements made in the last decade have enabled excluding nonsevere CM cases from antimicrobial treatment that have a high probability of cure without antimicrobials (no bacterial causes or gram-negative, excluding Klebsiella spp.) and cases with a low bacteriological cure rate (chronic cases). These advancements include availability of rapid diagnostic tests and improved udder health management practices, which reduced the incidence and infection pressure of contagious CM pathogens. This review informed an evidence-based protocol for selective CM treatment decisions based on a combination of rapid diagnostic test results, review of somatic cell count and CM records, and elucidated consequences in terms of udder health, AMU, and farm economics. Relatively fast identification of the causative agent is the most important factor in selective CM treatment protocols. Many reported studies did not indicate detrimental udder health consequences (e.g., reduced clinical or bacteriological cures, increased somatic cell count, increased culling rate, or increased recurrence of CM later in lactation) after initiating selective CM treatment protocols using on-farm testing. The magnitude of AMU reduction following a selective CM treatment protocol implementation depended on the causal pathogen distribution and protocol characteristics. Uptake of selective treatment of nonsevere CM cases differs across regions and is dependent on management systems and adoption of udder health programs. No economic losses or animal welfare issues are expected when adopting a selective versus blanket CM treatment protocol. Therefore, selective CM treatment of nonsevere cases can be a practical tool to aid AMU reduction on dairy farms.

Multivariable time series classification for clinical mastitis detection and prediction in automated milking systems.

In this study, we developed a machine learning framework to detect clinical mastitis (CM) at the current milking (i.e., the same milking) and predict CM at the next milking (i.e., one milking before CM occurrence) at the quarter level. Time series quarter-level milking data were extracted from an automated milking system (AMS). For both CM detection and prediction, the best classification performance was obtained from the decision tree-based ensemble models. Moreover, applying models on a data set containing data from the current milking and past 9 milkings before the current milking showed the best accuracy for detecting CM; modeling with a data set containing data from the current milking and past 7 milkings before the current milking yielded the best results for predicting CM. The models combined with oversampling methods resulted in specificity of 95 and 93% for CM detection and prediction, respectively, with the same sensitivity (82%) for both scenarios; when lowering specificity to 80 to 83%, undersampling techniques facilitated models to increase sensitivity to 95%. We propose a feasible machine learning framework to identify CM in a timely manner using imbalanced data from an AMS, which could provide useful information for farmers to manage the negative effects of CM.

Association of CXCR1 gene polymorphism with clinical mastitis, reproductive disorders and performance traits in Hardhenu (Bos taurus × Bos indicus) cattle.

The present study investigated the presence of CXCR1 gene polymorphisms and their association with clinical mastitis, reproductive disorders and performance traits in Hardhenu cattle. Genotyping of the targeted SNP rs211042414 (C>T) at the g.106216468 loci of the CXCR1 gene was performed through PCR amplification and Bsa1 restriction enzyme digestion. The genotypic frequencies revealed three genotypes: CC, CT and TT, with the C allele being the most prevalent. Significant associations were found between the targeted SNP and clinical mastitis occurrence using chi-square and logistic regression analyses. The CC genotype showed higher susceptibility to clinical mastitis with a higher odds ratio of 3.47 compared to TT (1.00) and CT (2.90) genotypes (p < .05). Furthermore, least squares analysis revealed significant associations between genotypes and performance traits such as total milk yield, 305-day milk yield and peak yield (p < .05). The CC genotype exhibited higher milk yields than CT and TT genotypes, indicating a positive association between the C allele and increased milk production. These findings have practical implications for the genetic improvement of Hardhenu cattle. Incorporating the identified CXCR1 gene polymorphisms into existing selection criteria can help enhance disease resistance and milk production traits. However, further validation with a larger sample size is necessary to strengthen the observed associations and ensure their practical applicability.

Impact of temporal variations and risk factors associated with udder inflammation in Hardhenu cattle (Bos taurus × Bos indicus).

This study aimed to explore the occurrence and risk factors associated with clinical mastitis within the Hardhenu cattle herd over a span of 14 years (2008-2021). A comprehensive analysis of 1515 lactation records was conducted to ascertain the incidence of clinical mastitis. The investigation determined an overall incidence rate of 26.80% in the studied population. A significant relationship between the year and clinical mastitis incidence was established through Chi-square analysis (p < .05). Temporal variations in clinical mastitis odds were apparent, with the highest odds (ranging from 0.91 to 1.00) observed during the initial years of 2008-2009 and 2009-2010. Logistic regression revealed that odds values for clinical mastitis incidence were highest in 2008-2009 (1.00), succeeded by 2009-2010 (0.91), 2012-2013 (0.88), 2018-2019 (0.67) and reaching the lowest in 2021-2022 (0.35). Subsequent rankings included 2010-2011 (0.39), 2014-2015 (0.43) and 2019-2020 (0.45). Parity was found to be significantly associated with clinical mastitis occurrence. When compared to Parity 3, both Parity 1 (odds ratio: 1.516, 95% confidence interval: 0.881-2.612) and Parity 2 (odds ratio: 2.626, 95% confidence interval: 1.568-4.398) exhibited higher odds values for clinical mastitis incidence. While the period of calving did not exert a significant influence on clinical mastitis incidence, a heightened occurrence was observed during the rainy season within the targeted population. These findings offer valuable insights into the patterns of incidence, temporal fluctuations, and non-genetic determinants impacting clinical mastitis within the Hardhenu cattle. The implications of this study can facilitate the development of targeted strategies and management protocols aimed at enhancing udder health and overall productivity in dairy cattle.

Practical challenges and potential approaches to predicting low-incidence diseases on farm using individual cow data: A clinical mastitis example.

Clinical mastitis (CM) incidence is considerable in terms of cows affected per year, but cases are much less common in terms of detections per cow per milking. From a modeling perspective, where predictions are made every time any cow is milked, low CM incidence per cow day makes training, evaluating, and applying CM prediction models a challenge. The objective of this study was to build models for predicting CM incidence using time-series sensor data and choose models that maximize net return based on a cost matrix. Data collected from 2 university dairy farms, the University of Florida and Virginia Polytechnic Institute and State University, were used to gather representative data, including 110,156 milkings and 333 CM cases. Variables used in the models were milk yield, protein, lactose, fat, electrical conductivity, days in milk, lactation number, and activity as the number of steps, lying time, lying bouts, and lying bout duration. Models that predicted either likelihood of CM caused by gram-negative (GN) or gram-positive (GP) bacteria on each day were derived using extreme gradient boosting with weighting favoring true-positive cases, logistic responses, and log-loss errors. Model accuracies were determined using data randomly held out from the training set on each run. All variables considered were in terms of change (slope) over previous days, including the day CM was visually detected. The GN models had a median sensitivity (Se) of 52.6% and specificity (Sp) of 99.8%, whereas the GP models had a median Se of 37.5% and Sp of 99.9% when tested on the held-out data. In our models optimized to reduce cost from predictions, the Se was much less than Sp, suggesting that CM models might benefit from greater model weighting placed on Sp. Results also highlight the importance of positive predictive value (true positive cases per predicted positive case) along with Sp and Se, as models built on sparse data tend to predict too many false-positive cases. The calculated partial net return of our GN and GP models were -$0.15 and -$0.10 per cow per lactation, respectively, whereas International Organization for Standardization (ISO) standard models with Se of 80% and Sp of 99% would return -$1.32 per cow per lactation. Models chosen that minimized the cost to the farmer differed markedly from models that met ISO guidelines, showing asymmetry in targets between Sp and Se when the disease incidence rate is low. Because of the unique challenges that low-incidence diseases like CM present, we recommend that future CM predictive models consider the economic and practical implications in addition to the traditional model evaluation metrics.

CTH/H2S Regulates LPS-Induced Inflammation through IL-8 Signaling in MAC-T Cells.

Hydrogen sulfide (H2S), as an endogenous gaseous signaling molecule, plays an important role in the inflammatory process. Our previous study found that Cystathionine-γ-lyase (CTH) and H2S are correlated with the occurrence and development of Clinical Mastitis (CM) in Holstein cows. However, the functions and regulatory mechanisms of CTH/H2S are still unknown. In this study, the inflammatory mammary cell model based on the MAC-T cell line was established by Lipopolysaccharide (LPS)-induced manner to further explore the function and regulatory mechanism of CTH/H2S in cows with CM. In the inflammatory MAC-T cell, the CTH expression and H2S production were both repressed in an LPS-dose dependent manner, which demonstrated that CTH/H2S is related to the progression of inflammation. The inhibition of CTH/H2S using a selective CTH inhibitor, ß-cyano-l-Alanine (BCA), promoted LPS-induced inflammation response and the expression of inflammatory cytokines. However, this was reversed by the H2S donor NaHS, demonstrating that H2S can protect cells from inflammatory damage. Intriguingly, interleukin-8 (IL-8) showed an inverse expression pattern correlated with the H2S-mediated cell protection effect during the inflammation process, and the inhibition test using a selective IL-8 receptor antagonist, SB225002, showed that IL-8 signaling plays a critical role in mediating endogenous H2S synthesis, and CTH/H2S exerts its anti-inflammation via IL-8-mediated signaling. This study provided support for the prevention and treatment of CM and the development of a novel anti-inflammatory strategy.