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.

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.

The aim of this cross-sectional study was to investigate associated factors of the severity of clinical mastitis (CM). Milk samples of 249 cases of CM were microbiologically examined, of which 27.2% were mild, 38.5% moderate, and 34.3% severe mastitis. The samples were incubated aerobically and anaerobically to investigate the role of aerobic and anaerobic microorganisms. In addition, the pathogen shedding was quantitatively examined, and animal individual data, outside temperature and relative humidity, were collected to determine associated factors for the severity of CM. The pathogen isolated the most was Escherichia coli (35.2%), followed by Streptococcus spp. (16.4%). Non-aureus staphylococci (NaS) (15.4%) and other pathogens (e.g., Staphylococcus aureus, coryneforms) (15.4%) were the pathogens that were isolated the most for mild mastitis. Moderate mastitis was mostly caused by E. coli (38%). E. coli was also the most common pathogen in severe mastitis (50.6%), followed by Streptococcus spp. (16.4%), and Klebsiella spp. (10.3%). Obligate anaerobes (Clostridium spp.) were isolated in one case (0.4%) of moderate mastitis. The mortality rate (deceased or culled due to the mastitis in the following two weeks) was 34.5% for severe mastitis, 21.7% for moderate mastitis, and 4.4% for mild mastitis. The overall mortality rate of CM was 21.1%. The pathogen shedding (back logarithmized) was highest for severe mastitis (55,000 cfu/mL) and E. coli (91,200 cfu/mL). High pathogen shedding, low previous somatic cell count (SCC) before mastitis, high outside temperature, and high humidity were associated with severe courses of mastitis.

UNASSIGNED: Streptococci are the major etiology in mastitis in dairy cattle, a cause of huge economic losses in the dairy industries. This study was aimed to determine the diversity of Streptococcus spp. isolated from clinical mastitis of cattle reared in Bangladesh.
UNASSIGNED: A total of 843 lactating cattle reared in four prominent dairy farms and one dairy community were purposively included in this study where 80 cattle were positive to clinical mastitis (CM) based on gross changes in the udder (redness, swelling, and sensitive udder) and/or milk (flakes and/or clots). Milk samples were collected from all the eighty cattle with clinical mastitis (CCM) and twenty five apparently healthy cattle (AHC). Samples were enriched in Luria Bertani broth (LB) and one hundred microliter of the enrichment culture was spread onto selective media for the isolation of Staphylococcus spp., Streptococcus spp., Enterococcus spp., Escherichia coli and Corynebacterium spp., the major pathogen associated with mastitis. Isolates recovered from culture were further confirmed by species specific PCR.
UNASSIGNED: Out of 105 samples examined 56.2% (59/105), 17.14% (18/105), 9.52% (10/105) and 22.9% (24/105) samples were positive for Staphylococcus, Streptococcus, Enterococcus faecalis and E. coli, respectively. This study was then directed to the determination of diversity of Streptococcus spp. through the sequencing of 16S rRNA. A total of eighteen of the samples from CCM (22.5%) but none from the AHC were positive for Streptococcus spp. by cultural and molecular examination. Sequencing and phylogenetic analysis of 16S rRNA identified 55.6, 33.3, 5.6 and 5.6% of the Streptococcus isolates as Streptococcus uberis, Streptococcus agalactiae, Streptococcus hyovaginalis and Streptococcus urinalis, respectively. Considering the high prevalence and worldwide increasing trend of S. uberis in mastitis, in-depth molecular characterization of S. uberis was performed through whole genome sequencing. Five of the S. uberis strain isolated in this study were subjected to WGS and on analysis two novel ST types of S. uberis were identified, indicating the presence of at least two different genotypes of S. uberis in the study areas. On virulence profiling, all the isolates harbored at least 35 virulence and putative virulence genes probably associated with intramammary infection (IMI) indicating all the S. uberis isolated in this study are potential mastitis pathogen. Overall findings suggest that Streptococcus encountered in bovine mastitis is diverse and S. uberis might be predominantly associated with CM in the study areas. The S. uberis genome carries an array of putative virulence factors that need to be investigated genotypically and phenotypically to identify a specific trait governing the virulence and fitness of this bacterium. Moreover, the genomic information could be used for the development of new genomic tools for virulence gene profiling of S. uberis.

Anemoside B4 has a good curative effect on cows with CM; however, its impact on their metabolic profiles is unclear. Based on similar somatic cell counts and clinical symptoms, nine healthy dairy cows and nine cows with CM were selected, respectively. Blood samples were collected from cows with mastitis on the day of diagnosis. Cows with mastitis were injected with anemoside B4 (0.05 mL/kg, once daily) for three consecutive days, and healthy cows were injected with the same volume of normal saline. Subsequently, blood samples were collected. The plasma metabolic profiles were analyzed using untargeted mass spectrometry, and the concentrations of interleukin (IL)-1β, IL-6, and tumor necrosis factor-α (TNF-α) in serum were evaluated via ELISA. The cows with CM showed increased concentrations of IL-1β, IL-6, and TNF-α (p < 0.05). After treatment with anemoside B4, the concentrations of IL-1β, IL-6, and TNF-α were significantly decreased (p < 0.01). Untargeted metabolomics analysis showed that choline, glycocholic acid, PC (18:0/18:1), 20-HETE, PGF3α, and oleic acid were upregulated in cows with CM. After treatment with anemoside B4, the concentrations of PC (16:0/16:0), PC (18:0/18:1), linoleic acid, eicosapentaenoic acid, phosphorylcholine, and glycerophosphocholine were downregulated, while the LysoPC (14:0), LysoPC (18:0), LysoPC (18:1), and cis-9-palmitoleic acid were upregulated. This study indicated that anemoside B4 alleviated the inflammatory response in cows with CM mainly by regulating lipid metabolism.

UNASSIGNED: Bovine mastitis is one of the most common and prevalent diseases affecting dairy cattle worldwide. It adversely affects the quality and quantity of milk production and leads to a significant economic loss for the farmers.
UNASSIGNED: This article aimed to estimate the prevalence of clinical mastitis (CM) infection in mainland China using a systematic review and meta-analysis. The research reports published during 1983-2022 in English or Chinese from databases (PubMed, Google Scholar, Cochrane Library, Science Direct, Web of Science, VIP Database for Chinese Technical Periodicals (VIP), Chinese National Knowledge Infrastructure (CNKI), and Wan Fang database) were identified after reviewing the relevant scientific literature. Based on our inclusion criteria, this study analyzed the prevalence of CM in 47 published studies prevalence extracted the total number of cattle infected with CM from the available studies, allowing us to estimate the prevalence of CM infection among these in mainland China.
UNASSIGNED: The pooled prevalence with the 95% CI for the clinical mastitis was 10% (95% CI: 9.00, 12.00). The majority of CM was associated with lactation, parity, and age, with higher prevalence observed in late lactation 15% (95% CI: 11.00, 18.00) and mid-lactation 10% (95% CI: 6.00, 13.00) in comparison to early lactation 8% (95% CI: 5.00, 10.00). The incidence of CM increased significantly with the increase of parity and age, and the highest incidence rates were 19% (95% CI: 15.00, 23.00) and 16% (95% CI: 12.00, 19.00) at parity and age ≥7, respectively. Among the seasons, the highest prevalence of CM infection was found in autumn 9% (95% CI: 2.00, 17.00). Interestingly, no significant effects were evident regarding the influence of quarter on the prevalence of CM.
UNASSIGNED: Thus, estimating the prevalence of CM among cattle in mainland China. through meta-analysis can provide adequate measures to control CM, reduce economic losses, and prevent the spread and transmission of CM in Chinese herds.

Compost-bedded pack barns (CB) are receiving increasing attention as a housing system that can potentially improve the welfare of dairy cows. This study characterized the frequency and profile of pathogens isolated from clinical (CM) and subclinical (SCM) mastitis in dairy cows housed in CB. It evaluated the association between mastitis occurrence and bedding characteristics in CB systems. Over six months, seven dairy herds were visited monthly for milk and bedding sample collections. Milk samples from mastitis cases were submitted to microbiological identification by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF MS). Bedding samples were submitted to physical-chemical (pH, organic matter, moisture, and carbon to nitrogen ratio) and microbiological counting (total bacterial counts, coliforms, streptococci, and staphylococci) analyses. Regression analysis was used to determine the association between mastitis occurrence and CB characteristics. Our results showed that Escherichia coli and environmental streptococci were the most frequently isolated pathogens from CM cases, while Staphylococcus chromogenes and contagious pathogens (Staphylococcus aureus and Streptococcus agalactiae) were the most commonly isolated from SCM cases. Bedding moisture content was positively associated with the incidence of CM. The bedding carbon to nitrogen ratio was negatively associated with the incidence of SCM, and the bedding total bacteria counts tended to be associated with the incidence of SCM. Bedding counts of coliforms positively associated with the prevalence of SCM. Our results can support decision-makers in the dairy industry seeking strategies for bedding management and mastitis control.