Antimicrobial resistance (AMR) poses an imminent threat to global public health, driven in part by the widespread use of antimicrobials in both humans and animals. Within the dairy cattle industry, Gram-negative coliforms such as Escherichia coli and Klebsiella pneumoniae stand out as major causative agents of clinical mastitis. These same bacterial species are frequently associated with severe infections in humans, including bloodstream and urinary tract infections, and contribute significantly to the alarming surge in antimicrobial-resistant bacterial infections worldwide. Additionally, mastitis-causing coliforms often carry AMR genes akin to those found in hospital-acquired strains, notably the extended-spectrum beta-lactamase genes. This raises concerns regarding the potential transmission of resistant bacteria and AMR from mastitis cases in dairy cattle to humans. In this narrative review, we explore the distinctive characteristics of antimicrobial-resistant E. coli and Klebsiella spp. strains implicated in clinical mastitis and human infections. We focus on the molecular mechanisms underlying AMR in these bacterial populations and critically evaluate the potential for interspecies transmission. Despite some degree of similarity observed in sequence types and mobile genetic elements between strains found in humans and cows, the existing literature does not provide conclusive evidence to assert that coliforms responsible for mastitis in cows pose a direct threat to human health. Finally, we also scrutinize the existing literature, identifying gaps and limitations, and propose avenues for future research to address these pressing challenges comprehensively.

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.

Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. “Mastitis” and “machine learning” were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as “sensors” and “mastitis detection”, also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area.

This review summarized the significant results from Brazilian studies published in peer-reviewed scientific papers about the effect of bovine subclinical mastitis (SM) on economic performance and milk production. Different approaches were considered for (i) disease detection (indirect measurement of somatic cell count (SCC) and directly using microbiological culture) and (ii) milk sampling strategy (mammary quarters, composite cow samples, and bulk milk tank). Globally, bovine mastitis is the most common disease of dairy herds, and the subclinical presentation is the most frequent. Dairy farmers usually underestimate the economic losses associated with SM because no visual changes in milk and quarters, udder, and systemic symptoms are observed. SM reduces milk yield and quality, reducing dairy herds’ profitability. The estimation of losses depends on the causative pathogen, the lactation stage, and the parity of affected cows. Thus, estimating the economic caused by SM in milk production and economic performance in dairy herds can be used to decide which mastitis control strategies to adopt. Mastitis control involves adopting specific measures associated with the characteristics of each herd, the period of the highest frequency of cases, the transmission form, and the profile of the pathogens involved in cases of intramammary infection. Thus, using individual SCC, the microbiological identification of pathogens causing SM, adopting efficient drying-off protocols, and other management practices are essential for mastitis control, improved milk quality, and greater profitability of dairy herds.

Staphylococcus aureus is a mastitis pathogen that compromises cow health and causes significant economic losses in the dairy industry. High antimicrobial resistance and biofilm formation by S. aureus limit the efficacy of conventional treatments. This study evaluated the potential of polyhexamethylene biguanide nanoparticles (PHMB NPs) against mastitis-causing S. aureus. PHMB NPs showed low toxicity to bovine mammary epithelial cells (MAC-T cells) at concentrations up to four times higher than the minimum inhibitory concentration (1 µg/mL) against S. aureus. In Experiment 1, PHMB NPs significantly reduced biofilm formation by S. aureus by 50% at concentrations ≥1 µg/mL, though they showed limited efficacy against preformed biofilms. In Experiment 2, using an excised teat model, PHMB NPs reduced S. aureus concentrations by 37.57% compared to conventional disinfectants (chlorhexidine gluconate, povidone–iodine, and sodium dichloroisocyanurate), though limited by short contact time. These findings highlight the potential of PHMB NPs for the control of S. aureus growth and biofilm formation.