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.

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.

UNASSIGNED: To describe the incidence, aetiology, treatment, and outcomes of farmer-reported clinical mastitis on New Zealand dairy sheep farms.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.

The aim of this study was to construct an enhanced selection index using the genomic and economic parameters of new health disorders and current production and functional traits. Genomic evaluation for the incidence of clinical mastitis (CM), three claw disease traits, retained placenta (RET), metritis (MET), and cystic ovaries (CYS) was performed using linear animal models based on producer-recorded data. Good correlations among the health disorders were found, and their heritability estimates did not exceed 7%. Economic weights (EWs) for the health disorders were EUR -132.10 for CM, EUR -128.87 for overall claw diseases, EUR -52.10 for RET, EUR -80.48 for MET, and EUR -16.16 for CYS. These EWs indicate changes in the present value of the annual profit per cow when increasing the incidence of the traits by one case per cow year. Selection using the enhanced index resulted in favourable responses for most of the new health disorders (e.g., -0.001 and -0.006 cases of RET and MET per cow year, respectively), and also in the current breeding objective traits (+49 kg of milk, -0.02% of calf losses). An index contribution of 7% for the new health disorders was assessed as acceptable for the breeders.

Mycoplasma bovis has recently been identified increasingly in dairy cows causing huge economic losses to the dairy industry. M. bovis is a causative agent for mastitis, pneumonia, endometritis, endocarditis, arthritis, otitis media, and many other clinical symptoms in cattle. However, some infected cows are asymptomatic or may not shed the pathogen for weeks to years. This characteristic of M. bovis, along with the lack of adequate testing and identification methods in many parts of the world until recently, has allowed the M. bovis to be largely undetected despite its increased prevalence in dairy farms. Due to growing levels of antimicrobial resistance among wild-type M. bovis isolates and lack of cell walls in mycoplasmas that enable them to be intrinsically resistant to beta-lactam antibiotics that are widely used in dairy farms, there is no effective treatment for M. bovis mastitis. Similarly, there is no commercially available effective vaccine for M. bovis mastitis. The major constraint to developing effective intervention tools is limited knowledge of the virulence factors and mechanisms of the pathogenesis of M. bovis mastitis. There is lack of quick and reliable diagnostic methods with high specificity and sensitivity for M. bovis. This review is a summary of the current state of knowledge of the virulence factors, pathogenesis, clinical manifestations, diagnosis, and control of M. bovis mastitis in dairy cows.

Mastitis is a major health problem for bovines and can be categorized as non-severe or severe, based on clinical symptoms. A severe case of clinical mastitis is usually defined by the cow being affected systemically. It is important to consider how to handle severe cases because these cases can be fatal and cause high production losses. However, there are generally few detailed treatment guidelines. By conducting a scoping review on the topic, we aimed to synthesize the information that is available on treatment and outcomes, as reported from clinical trials and observational studies. This was facilitated by following the PRISMA-guidelines with a stepwise systematic screening of scientific literature on the subject, retrieved via Pubmed and Web of Science, using pre-defined selection criteria. The results yielded a total of 14 reports of treatment and outcomes in cases of naturally occurring severe clinical mastitis. Cross-trial comparison was difficult due to the different exclusion criteria and outcome definitions. Many studies focused on cases caused by gram-negative bacteria treated with intensive antibiotic protocols, often containing antibiotics that are categorized as critical for human health. Few focused on severe cases caused by gram-positive bacteria or on the relative use of non-antibiotic treatment. In general, only a small number of statistically significant differences were found in trials comparing different treatment protocols, with no obvious trends across trials. Our findings emphasize the need for more research into the treatment efficacy of antibiotic and non-antibiotic options for clinically severe mastitis. Furthermore, consideration of how trial conditions relate to the practical circumstances in a field setting could improve the applicability of reported results. This could help to provide practitioners with the information needed to make evidence-based treatment decisions in cases of clinically severe mastitis.

Automated milking systems (AMSs) already incorporate a variety of milk monitoring and sensing equipment, but the sensitivity, specificity, and positive predictive value of clinical mastitis (CM) detection remain low. A typical symptom of CM is the presence of clots in the milk during fore-stripping. The objective of this study was the development and evaluation of a deep learning model with image recognition capabilities, specifically a convolutional neural network (NN), capable of detecting such clots on pictures of the milk filter socks of the milking system, after the phase in which the first streams of milk have been discarded. In total, 696 pictures were taken with clots and 586 pictures without. These were randomly divided into 60/20/20 training, validation, and testing datasets, respectively, for the training and validation of the NN. A convolutional NN with residual connections was trained, and the hyperparameters were optimized based on the validation dataset using a genetic algorithm. The integrated gradients were calculated to explain the interpretation of the NN. The accuracy of the NN on the testing dataset was 100%. The integrated gradients showed that the NN identified the clots. Further field validation through integration into AMS is necessary, but the proposed deep learning method is very promising for the inline detection of CM on AMS farms.

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 was comprised of microbiological results from quarter CM samples routinely submitted to Quality Milk Production Services at Cornell University for microbial identification by MALDI-TOF mass spectrometry. 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, Staphylococcus haemolyticus, Staphylococcus simulans, Staphylococcus epidermidis, Staphylococcus sciuri (now Mammaliicoccus sciuri), Staphylococcus agnetis/Staphylococcus hyicus, Staphylococcus borealis, and Staphylococcus 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 Staphylococcus borealis, a significant seasonality effect for our study\'s most prevalent NASM. This study showed that Staphylococcus chromogenes remains the most frequent (43%) NASM species identified from bovine CM, followed by Staphylococcus haemolyticus (18%), and Staphylococcus simulans (12%).

\"India is the world\'s leading producer of milk\" and demands a non-invasive diagnostic tool like infrared thermography (IRT) to identify the costliest production disease, 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 Sahiwal cows during different seasons to identify subclinical (SCM) and clinical mastitis (CM) cases using the Darvi DTL007 camera. A total of 24-69 lactating Sahiwal cows were screened out using IRT regularly throughout the year. The intramammary infection status was further assessed using the CMT. The receiver operating characteristic analysis was carried out to develop the current study\'s cut-off for various thermographic parameters. The incidence for SCM and CM ranged from 26.47 to 38.75% and 17.83-22.79%, respectively during different seasons in Sahiwal udder quarters. The thermogram analysis revealed a significant difference (p < 0.01) in the mean values of the udder and teat surface temperature of Sahiwal cows between healthy, SCM, and CM during different seasons. The mean values of udder skin surface temperature (USST) during different seasons ranged between 29.07 and 36.91 °C, 31.51 to 37.88 °C and 32.42 to 38.79 °C among healthy, SCM, and CM-affected quarters, and correspondingly, the mean values of teat skin surface temperature (TSST) were 28.28 to 36.77 °C, 30.68 to 37.88 °C and 31.70 to 38.73 °C, respectively. Further results revealed an increase (p < 0.01) in the mean values of USST during winter, summer, rainy, and autumn as 2.44, 3.35; 0.97, 1.88; 1.06, 1.83; 1.29, 2.39 °C and TSST as 2.4, 3.42; 1.11, 1.96; 1.21, 2.19, 1.3, 2.4 °C of SCM, CM-affected quarters to healthy quarters, respectively, in Sahiwal cows. Thermograms showed a strong positive correlation with the CMT scores of SCM, CM cases, and healthy samples. Henceforth, irrespective of the seasons studied in the present work, IRT is an efficient, supportive tool for the early identification of subclinical mastitis.