This article presents a composite case study of a 27-year-old woman who attended a first contact service with right breast pain and systemic symptoms six months after undergoing a nipple piercing. She was subsequently diagnosed with non-lactational mastitis and treated successfully with antibiotics. The article reviews the anatomy of the female breast, the presentation and management of mastitis and breast abscess, and the pathogens associated with nipple piercing, including Staphylococcus aureus and non-tuberculous mycobacteria. It highlights the diagnostic challenges posed by the similarity of breast changes associated with mastitis, breast abscess and other potentially more serious conditions, including breast cancer. The article also identifies limitations in the evidence base regarding complications associated with nipple piercing and notes the lack of robust evidence informing the management of non-lactational mastitis. Further research is required to support evidence-based recommendations for diagnosis and treatment.

The Kazakh coarse-wool fat-tailed sheep is an indigenous breed of Central Asia and represents a valuable genetic resource adapted to harsh continental environmental conditions. Despite its economic importance, its genomic architecture, population structure, and the genetic basis of disease resistance remain largely unexplored. Whole-genome sequencing was performed on 20 Kazakh coarse-wool fat-tailed sheep using the Illumina NovaSeq 6000 platform. Population structure was assessed by principal component analysis (PCA) using comparative genomic data from six domestic sheep breeds and the wild ancestor Ovis orientalis. Selection signatures were identified by integrating FST, nucleotide diversity (π), and Tajima's D statistics. Variants within the PRNP and TMEM154 loci were extracted from the filtered VCF files using bcftools. PCA revealed a distinct genetic cluster of the Kazakh coarse-wool fat-tailed sheep, showing close genetic affinity to Bashibai and Hu sheep while remaining clearly differentiated from European meat and short-tailed breeds. A total of 208 candidate selective sweep regions containing 297 overlapping genes were identified. Quantitative trait loci (QTL) annotation revealed enrichment for loci associated with resistance to gastrointestinal nematodes (14 QTLs), body weight (9 QTLs), susceptibility to pneumonia (2 QTLs), clinical mastitis (2 QTLs), and susceptibility to Mycobacterium avium subsp. paratuberculosis infection (2 QTLs). Five PRNP haplotypes were identified, with 60% of animals classified as high-risk (NSP3) for scrapie susceptibility. No animals carrying the fully resistant ARR/ARR genotype were detected. At the TMEM154 locus, 95% of individuals carried the E35E genotype, which is associated with increased genetic susceptibility to Maedi-Visna. This study provides the first comprehensive genomic characterization of the Kazakh coarse-wool fat-tailed sheep, revealing its unique population structure, genomic regions under selection associated with adaptation and disease resistance, as well as genetic predisposition susceptibility-associated genotypes for scrapie and Maedi-Visna. These findings provide a valuable genomic resource for marker-assisted breeding, genetic improvement, and conservation of indigenous sheep genetic resources in Kazakhstan.

Knowledge of the probability that a dairy cow will cure of mastitis when treated with antimicrobials would facilitate informed treatment decisions. A generalised linear mixed model was created for predicting the probability of cure of cases of clinical mastitis. Cure was defined as the absence of subclinical and clinical mastitis over the subsequent two or three milk-recordings. The data described 5845 cases of mastitis on 49 U.K. commercial dairy farms between 2003 and 2005, alongside cow- and herd-level variables proposed to be associated with cure. Data were partitioned into two subsets, for training and validating the model respectively. Further model validation was performed against modern data collected between 2020 and 2026. The final model was selected through backwards variable selection, guided by Akaike's information criterion (AIC). Variables significantly associated with cure described the historical occurrence of subclinical or clinical mastitis. Elevated SCC at the two most recent tests was associated with reduced odds of cure, as was the occurrence of three or more cases of clinical mastitis in the previous lactation. The proportion of tests during the previous lactation at which the SCC was greater than 100,000 cells/ml was inversely associated with cure probability. On the validation data, probabilistic predictions were well-calibrated although balanced accuracy of classifications was modest at 0.67. The model provides calibrated predictions of cure probability without the need to identify causal pathogens; its deployment will be of value to farmers wishing to make evidence-based mastitis treatment decisions consistent with the desire to minimise antimicrobial usage.

Recent advances in sensor technologies and machine learning have increased expectations for actionable disease detection in dairy cattle, yet farm-level adoption remains limited. This conceptual review outlines an implementation trajectory from proof of concept to deployment as follows: (i) case definitions and defensible reference standards for diseases such as mastitis, lameness and respiratory disease should be biologically based; (ii) model development should consider features that drive utility and address class imbalance, ensuring that algorithms can reliably detect diseases that occur infrequently within herds; (iii) external validation should occur at the herd level, across seasons and time, and should report prevalence-aware metrics (e.g., recall and precision [positive predictive value]) rather than relying solely on accuracy or receiver operating characteristic area under the curve; and (iv) interoperability and economic viability should be sufficient to support multi-sensor use and sustainable adoption. Economic factors such as hardware and software costs, follow-up time burden, usefulness and return on investment under realistic false-positive rates will primarily determine adoption. Ethical and welfare considerations include maintaining human oversight where possible, avoiding invasive sensing and preventing over-automation. In conclusion, sensor-based systems show promise but remain constrained by inconsistent case definitions, limited cross-herd validation, interoperability gaps and uncertain economics, and many tools still blur the line between detecting animals needing treatment and ancillary predictions. With regulatory and ethical safeguards, progress will depend on reliability-focused standard operating procedures and end-user-oriented design; until then, most systems remain closer to hype than independent clinical technologies.

Triple-negative breast cancer (TNBC) remains the most aggressive form of breast cancer with a worse prognosis than other subtypes. Its high metastatic potential, limited availability of targeted therapies, and increased likelihood of being diagnosed at an advanced stage make it one of the most complex clinical challenges in breast cancer. Today, thanks to the advent of antibody-drug conjugates (ADCs), clinical outcomes have improved significantly. In this context, sacituzumab govitecan (SG) has been a true revolution. The case reported here illustrates how this therapeutic opportunity produced lasting clinical benefit in a highly complex patient with a triple neoplasm at presentation: inoperable locally advanced TNBC, operable colon cancer, and localized lung cancer. The chemorefractory patient (rapid progression of the first two lines of chemotherapy) received SG as a third-line treatment, showing a sustained response lasting more than 12 months and a significant benefit in terms of quality of life. This case highlights how a proper multidisciplinary approach can lead to a lasting clinical benefit from therapy. Deviating from the cases reported in clinical studies, it is important to underscore how, in this context, clinical choice prevailed. Given the locally advanced stage of the breast cancer, characterized by carcinomatous mastitis tending to ulceration and the high proliferation rate, compared to the localized stage of lung adenocarcinoma, a multidisciplinary decision was made to treat the more rapidly progressing disease, TNBC. In light of this, the importance of collecting clinical practice data to support the complex challenges we face daily is emphasized.

This study focuses on the development of nanocapsules loaded with Tagetes minuta essential oil (TEO-NC) for milking management in sustainable dairy systems. The nanocapsules were prepared by nanoprecipitation, and their physicochemical properties were evaluated for 60 days at 4°C and 25°C. Antimicrobial and disinfectant activities were evaluated in the presence and absence of organic matter and on contaminated surfaces, such as rubber, stainless steel and ceramics. TEO-NCs had an average particle size of 266.67 ± 18.28 nm, polydispersity index (PdI) <0.12, zeta potential of 42.70 ± 1.08 and pH of 4.09 ± 0.08. Moreover, TEO-NCs showed no visual changes, and the physicochemical changes were minimal at the two temperatures tested. The minimum inhibitory concentration of TEO-NCs against Staphylococcus aureus was 50% (v/v). The disinfectant activity of TEO-NCs (100 and 50%, v/v) was demonstrated against Pseudomonas aeruginosa in the absence of organic matter after 5, 15 and 30 min. Disinfectant activity against S. aureus was demonstrated after 15 and 30 min (100%, v/v). In the presence of organic matter, the disinfectant activity of TEO-NCs (100%, v/v) was effective only against P. aeruginosa. Similarly, disinfectant activity of TEO-NCs against P. aeruginosa was demonstrated after 15 and 30 min on the three surfaces tested. Our results highlight the potential of T. minuta essential oils, a natural product, which, when combined with nanocapsules, results in a potential alternative for teat disinfection in milking management, especially in the control of environmental mastitis.

The objective of this retrospective before-and-after trial was to evaluate the effects of a Bacillus-based direct-fed microbial (DFM) on milk yield, composition, reproductive performance, and health status of dairy cows in a single commercial dairy farm in Argentina. The dataset used in the current study comprised January 2019 until September 2025, including roughly 1,300 to 1,600 lactating Holstein cows across the study period, split into 10 pens. The data between January 2019 to September 2023 was considered as the period pre-DFM feeding (57 mo; PRE), whereas the period of DFM supplementation (B. licheniformis 809 and B. subtilis 810) considered in the analysis ranged from October 2023 to September 2025 (24 mo; POST). During this period, the Bacillus-based DFM was fed at 3 g/cow per day. Milk yield was evaluated daily and individually for all lactating cows, while milk composition was evaluated monthly for the whole-farm. Based on these results, 4% FCM and ECM yield were determined. Moreover, reproductive and health data were also collected. All data were analyzed with the GLIMMIX and GLM procedure of SAS, considering pen as the experimental unit. No treatment × season interactions were observed for any of the productive and health variables (P ≥ 0.08). Treatment group and season were associated with differences in milk yield (P ≤ 0.02). Milk yield was 1.0 kg/cow per day greater (P = 0.02) in POST cows, while 4% FCM and ECM were 3.6 and 4.1 kg/day greater (P ≤ 0.01), respectively, following the DFM supplementation. Cows in the winter and spring produced more milk (P < 0.01) than in the fall and summer. For milk composition (kg and %), positive changes were observed and associated with the DFM supplementation period (P ≤ 0.07). The occurrence of adverse heath events and mortality rate were observed to be reduced (P < 0.001) from 32.8 and 1.6% to 23.7 and 0.7% for PRE and POST periods, respectively. Among the individual adverse health outcomes, mastitis, ketosis, metritis, displaced abomasum, and pneumonia occurrence were significantly reduced in POST vs. PRE (P ≤ 0.02). Treatment group × season interactions were observed for both conception and abortion rates (P ≤ 0.03); however, the interactions were noted mainly because of the season effect, given that within the same season, no treatment differences were observed (P ≥ 0.14). No treatment effects were observed either on conception or abortion rate (P ≥ 0.31). In summary, in this retrospective before-and-after study, B. licheniformis 809 and B. subtilis 810 supplementation was associated with an improved milk production, composition, and the health of the herd, while maintaining the reproductive performance of the commercial farm in Argentina.

Mastitis-induced inflammation in dairy cows can trigger premature luteolysis and subsequent pregnancy loss; however, the molecular mechanisms underlying these effects remain poorly understood. This study aimed to characterize changes in gene expression in the corpus luteum of cows with clinical or subclinical mastitis compared with healthy cows. Multiparous pregnant cows from a commercial pasture-based dairy herd were classified into three groups: subclinical mastitis (SCM; n = 6), clinical mastitis (CLM; n = 6), and healthy controls (CON; n = 5), based on California Mastitis Test scores and somatic cell counts. Corpus luteum biopsies were collected at pregnancy diagnosis, between 28 and 42 days post-insemination, using a transvaginal ultrasound-guided technique and subjected to RNA sequencing. Differential expression analysis identified 428 differentially expressed genes (DEG) between CLM and CON cows (263 upregulated and 165 downregulated), 186 between SCM and CON cows (76 upregulated and 110 downregulated), and 391 between CLM and SCM cows (236 upregulated and 155 downregulated; FDR < 0.05). Functional enrichment of DEG between CLM and CON showed dysregulation of pathways related to immune response, cortisol, GnRH, and oxytocin signaling. These transcriptional changes may reflect an adaptive luteoprotective response aimed at maintaining luteal function under systemic inflammatory stress. Overall, the results demonstrate that mastitis, particularly in its clinical form, markedly alters the luteal transcriptome during early pregnancy, potentially affecting corpus luteum function and contributing to an increased risk of pregnancy loss.

This study evaluated the influence of postpartum disorders, parity, stage of lactation, and season on estimated enteric methane emissions in dairy cows from northwestern Spain using dairy herd improvement (DHI) data. A total of 2,440 lactating Holstein cows from 50 commercial dairy farms were included. Data from four official monthly DHI controls conducted in 2017 included daily milk yield, milk composition, parity, and postpartum disorders recorded within 21 days in milk. Methane emissions (MEE; g CH4/day) were estimated using a prediction equation based on energy-corrected milk, and methane emission intensity (MEEI) was calculated as g CH4/kg of daily milk yield. MEEI was highest in early lactation and declined as milk yield increased. Retained placenta, metritis, subclinical mastitis, and abomasal displacement were associated with higher MEEI, whereas ketosis was associated with higher milk yield and total MEE but not with MEEI. Multiparous cows had higher total MEE but lower MEEI than primiparous cows. During warm months, cows produced less milk and had lower total MEE but higher MEEI than during cold months. These findings indicate that DHI-based methane estimates can be used as practical indicators of emission efficiency in dairy herds. Differences in methane intensity were mainly associated with milk production efficiency and were influenced by health status, parity, lactation stage, and season. Improving early-lactation health and reducing seasonal stress may help lower methane emissions per unit of milk, although direct methane measurements are needed to validate these estimates. Future studies should validate these estimates using direct on-farm methane measurements.

Somatic cell count (SCC) is widely used as an indicator of milk quality and udder health in ruminants; however, its interpretation in goats remains complex due to species-specific physiology and multifactorial variability. A key challenge in goat dairy production is distinguishing physiological from pathological SCC elevation. This critical narrative review synthesizes current knowledge on physiological, genetic, environmental, and inflammatory determinants of SCC in goat milk and evaluates their implications for milk quality and udder health assessment. The evidence indicates that SCC in goats is inherently elevated due to the apocrine milk secretion mechanism and is further influenced by factors such as lactation stage, parity, environmental conditions, and management practices. Genetic effects are moderate and context-dependent, while intramammary infection remains the primary pathological driver of SCC increase. These findings demonstrate that SCC cannot be reliably interpreted using fixed threshold values. Instead, its diagnostic value depends on context-dependent evaluation. Based on the reviewed evidence, an integrated framework for SCC interpretation is proposed, enabling differentiation between physiological and pathological SCC elevation. This approach supports more accurate diagnostic decision-making and improved milk quality management in goat production systems.