Mastitis is an important and prevalent infectious disease in dairy herds that imposes high economic losses annually to the animal industry. Numerous infectious agents, including bacteria, fungi and viruses, can cause mastitis, with Staphylococcus spp. and Streptococcus spp. as prevalent causative agents. Mastitis is typically classified as clinical or subclinical, which impacts lactose and somatic cell count (SCC) volumes in milk. In this cross-sectional study, 108 Holstein Friesian cows from three industrial farms in Iran were sampled; after exclusion of five samples for culture contamination, a total of 427 milk samples were analysed. Samples with SCC > 200,000 cells/mL were considered positive for subclinical mastitis. Overall, 238/427 samples (55.7%) exceeded this SCC cut-off. Staphylococcus aureus and Klebsiella spp. were the most frequent isolates among culture-positive samples. Antibiotic susceptibility testing was performed on 11 representative isolates (selected to cover the main species and herds). A significant negative association between milk lactose and log-SCC was observed (p < 0.05). These results indicate lactose reduction as a useful indirect indicator of subclinical mastitis and document the bacterial profile of industrial Iranian farms.

Genetic parameters for traits derived from measures of electrical conductivity collected by automatic milking systems and their correlations with clinical mastitis in Holstein cattle.

The large amount of data collected through automatic milking systems (AMS) may be used for early detection of intramammary infections and become instrumental for monitoring udder health in dairy herds. Machine learning (ML) techniques can aid in improving diagnostic test properties of current indicators of subclinical mastitis (SCM). In this study, we present novel customized ML models for predicting SCM from AMS data. We show how results from several diagnostic tests can be incorporated into ML model training by explicitly accounting for their sensitivity and specificity. The underlying infection status was modeled as a latent variable derived from bacteriological culture (BC) and polymerase chain reaction (PCR) results on milk samples. Model performance was evaluated using a customized log-likelihood (CLL) function, addressing uncertainty in prediction target, and compared with traditional metrics using simulated data. Our study demonstrates that incorporating prior knowledge of sensitivity and specificity of the tests directly into the likelihood function during model training enables reliable ML even in scenarios with an imperfect target variable. The customized models achieved the highest CLL scores on real data and demonstrated significantly better calibration on simulated data. At the same time, all models showed similarly near-perfect area under the curve (AUC) on simulated data. Further validation across herds is needed, but our approach shows promise for robust SCM prediction from AMS data using ML. The framework is applicable to other scenarios in veterinary epidemiology with imperfectly measured outcomes.

The study evaluated the impact of clinical and subclinical mastitis in 656 Holstein-Czech Fleckvieh crossbred cows on feeding time, rumination, feeding-to-rumination ratio, and events of increased activity. Statistical analysis was performed in SAS 9.4. using the GLM procedure. It also assessed the feeding to rumination ratio as a potential predictor of subclinical or clinical mastitis. Significant behavioral changes were observed before onset of mastitis. Feeding duration peaked six days before symptoms (255.06 ± 5.21 min) for subclinical mastitis and nine days prior to clinical mastitis (235.53 ± 10.21 min). This is possible to confirmed also by significant changes in events of increased activity were also observed before disease onset. Rumination was highest on the day of clinical mastitis detection (491.24 ± 10.63) but lowest the day before detection (403.77 ± 5.43 min) for subclinical mastitis, indicating possible stress-related changes. The feeding-to-rumination ratio fluctuated, peaking at 0.57 ± 0.01 five days before subclinical mastitis and at 0.80 ± 0.02 for clinical mastitis, potentially reflecting metabolic and health status changes. At the onset of mastitis, the ratio declined to 0.44 for clinical cases and 0.50 for subclinical cases. Cows with subclinical mastitis exhibited longer feeding time and more events of increased activity, whereas those with clinical mastitis showed increased rumination. These differences highlight distinct behavioral responses to mastitis types. The study suggests that monitoring the feeding-to-rumination ratio and its trends over time could serve as an early indicator for mastitis detection.

Mastitis, an inflammation of mammary tissue caused by infection, physical injury, or chemical irritation, is one of the most economically significant diseases in the global dairy industry. Both acute and chronic forms of mastitis lead to an increase in somatic cell counts (SCCs) in milk and a significantly reduced milk yield. Farmers' knowledge and awareness of mastitis play a crucial role for the preventive and therapeutic measures to manage the disease. This study aims to assess the efficacy of mastitis control practices adopted by dairy farmers in Austria by a survey-based approach. Data on milk yield and milk components were obtained from the Austrian National Milk Recording System, whereas farm management practices were documented by direct surveys conducted with dairy farmers. The study found that farm management practices related to milking, udder health, hygiene management, disease control, and nutrition significantly impact milk yield and the frequency of SCC exceeding 200,000/mL. These findings highlight the critical influence of management strategies during the dry period, nutrition, and milking practices on both milk production and mastitis indicators. Based on these results, it is strongly recommended that farmers receive training on effective strategies to improve milk yield and control mastitis.

Concentrations of serum amyloid A and haptoglobin in mare blood and milk as indicators of subclinical mastitis.

Caprine mastitis is a common and costly disease in dairy goats, especially in Egypt, where ≈ 4.2 million goats are raised. Early detection is critical as the subclinical form is the most common. Procalcitonin is a sepsis‐associated prohormone that rises in bacterial infections. It is unfortunately, goats haven’t been extensively investigated. In this study, we measured Procalcitonin in both serum and milk along with somatic cell count (SCC) and calcium in fifty adult female lactating Egyptian Nubian goats, with an average body weight of (45± 5) kg and an age range of 6–7 years which were grouped into four: control (n=8), critical (n=5), subclinical (n=8), and clinical mastitis (n=29). Our results indicated an increase in vital signs such as temperature, pulse, and respiratory rate in the mastitis group. In addition to the mean corpuscular volume, white blood cell counts, monocytes, and eosinophils were elevated in the clinical group, and monocytes in the subclinical group. There was a decrease in red blood cell, and packed cell volume (PCV) in the subclinical group and in the RBC, hemoglobin, PCV, and platelet in the clinical group. The critical group showed a significant drop in the platelet count. Results showed a stepwise rise in both serum and milk procalcitonin with increasing mastitis severity, while SCC is highly elevated in the clinical cases. We conclude that measuring Procalcitonin levels in serum or milk may contribute in the early detection of critical and subclinical mastitis and aid in the prognosis of severe mastitis in Nubian goats.

HighlightsWhat are the main findings?An AI-driven thermal imaging system, synchronized with robotic milking, enables continuous, non-invasive monitoring of udder health in Italian Mediterranean buffalo.A SegFormer-based neural network accurately segments the udder and extracts the maximum skin temperature, showing a significant correlation with somatic cell count (SCC).What are the implications of the main findings?The system allows for the early detection of subclinical mastitis, enabling timely veterinary intervention before clinical signs appear.This method supports precision livestock farming by reducing stress, avoiding unnecessary antibiotic use, and improving milk quality and farm sustainability.Mastitis is a significant challenge in the buffalo industry, affecting both milk production and animal health and resulting in economic losses. This study presents the first fully automated AI-driven thermal imaging system integrated with robotic milking, specifically developed for the real-time, non-invasive monitoring of udder health in Italian Mediterranean buffalo. Unlike traditional approaches, the system leverages the synchronized acquisition of thermal images during milking and compensates for environmental variables through a calibrated weather station. A transformer-based neural network (SegFormer) segments the udder area, enabling the extraction of maximum udder skin surface temperature (USST), which is significantly correlated with somatic cell count (SCC). Initial trials demonstrate the feasibility of this approach in operational farm environments, paving the way for scalable, precision diagnostics of subclinical mastitis. This work represents a critical step toward intelligent, automated systems for early detection and intervention, improving animal welfare and reducing antibiotic use.

This study investigated polymorphisms in the genes CYP3A4, CYP2R1, and TLR5, and their associations with liver fluke (Fasciola hepatica) and rumen fluke (Calicophoron / Paramphistomum spp.) infections as well as with milk somatic cell count (SCC) as an indicator for mastitis in Holstein Friesian dairy cows. DNA sequencing of the genes' exons, 5'-, 3'-untranslated regions (UTR), introns, and flanking regions of 24 cows revealed 442 variants (388 SNPs and 54 InDels) including 116 previously unknown variants. We detected three novel non-synonymous variants leading to the derived amino acid exchanges, i.e. CYP3A4 p.Gly197Ser, CYP3A4 p.Ile388Val, and CYP2R1 p.Val11Ala. The newly identified SNP 25:36589922 T > C (ss11846100002) is positioned in the splice site of CYP3A4, but showed no impact on the binding score of the splice enzymes. The CYP2R1 and TLR5 genes presented 11 SNPs in the 5'- and 3'-UTR, partly influencing transcription factor binding or microRNA target sites. Associations between polymorphisms and constructed haplotypes with infection traits were analysed via (generalized) linear mixed models including further potential confounders. In total, 109 variants in CYP3A4, 37 variants in CYP2R1, and 18 variants in TLR5 were significantly associated with F. hepatica and rumen fluke infections, and with SCC. The CYP2R1 and TLR5 variants were mostly linked to SCC, indicating the genes' roles in immune responses to bacterial infections. Haplotype analysis revealed significant associations between specific CYP3A4 haplotypes and F. hepatica worm count and faecal egg counts. This study revealed significant insights into gene polymorphisms related to vitamin D metabolism and immune response, which seem to play a role in helminth and udder infections.

Subclinical mastitis is a major concern in dairy production, leading to economic losses and antibiotic dependence, which poses risks of milk residue contamination. As a natural alternative, orange jasmine (Murraya paniculata) leaves, rich in flavonoids, exhibit antimicrobial and anti-inflammatory properties. This study investigated the effects of incorporating date pulp and orange jasmine leaf flour (OLF) into the diets of lactating Ettawa crossbred goats on lactation performance and subclinical mastitis indicators. A randomized block design with a 2&times;2+1 factorial arrangement was applied using 15 primiparous lactating goats distributed across: control diet (35% forage + 65% concentrate), 10% date pulp with or without 1% OLF, and 20% date pulp with or without 1% OLF. Results showed that dry matter intake decreased significantly (p &lt; 0.05) compared with control, while dry matter digestibility was influenced by the interaction between date pulp and OLF but remained above the physiological threshold for ruminants. Milk yield and most milk components were not significantly affected, although fat content increased (p &lt; 0.05) with OLF supplementation. Hematological parameters and leukocyte differentiation showed no significant differences among treatments, remaining within normal physiological ranges. Notably, the combination of 20% date pulp and 1% OLF resulted in the largest reduction in somatic cell count (up to 68.75%) and lower neutrophil-to-lymphocyte ratio, reflecting improved udder health and reduced systemic inflammation. In conclusion, dietary supplementation with 20% date pulp and 1% OLF represents a promising strategy to enhance udder health and mitigate subclinical mastitis in lactating goats without compromising production performance.

The objectives of the study were to determine somatic cell count (SCC) and evaluate the presence of pathogens (IMI - intramammary infection) in late lactation (LL), followed by the start (colostrum, CL) and approximate peak (established lactation, EL) of the next lactation, as well as to assess the possible transmission of IMI from lactation to lactation. The study was performed on a dairy farm in northern Slovakia. A total of 489 half udder milk samples (242, 80 and 167 in LL, CL and EL, respectively) were collected. Pathogens were identified using MALDI-TOF MS and PathoProof (the latter only in LL). SCC was determined only in LL and EL. Samples were divided according to SCC in four groups from lowest (SCC1<500×103 cells mL-1) to highest (SCC4≥2000×103 cells mL-1). SCC was higher in LL than in EL. The prevalence of pathogens identified using MALDI-TOF MS was 16.5, 38.8 and 12.6% in LL, CL and EL, respectively. Non-aureus staphylococci and mammaliicocci (NASM) were the most common isolated pathogens in goat milk and colostrum. Staphylococcus (S.) caprae and S. epidermidis species tended to cause persistent IMI in the next lactation. The identification of pathogens using PathoProof was higher than with MALDI-TOF MS. Of all the pathogens (n=262) identified using PathoProof, the most common were Staphylococcus spp. (86.7%) of which 65.8% exhibited the β-lactamase gene. Additionally, Escherichia coli (4.2%), S. aureus (2.7%), Enterococcus spp. (2.3%), Streptococcus uberis (1.9%), Mycoplasma spp., Protetheca spp. (0.8% each), Arconabacterium pyogenes/Peptoniphilus indolicus and yeast (0.4% each) were also detected using PathoProof. Better identification of pathogen presence in samples with high SCC could contribute to the discussion about SCC as an indicator of subclinical mastitis in goats.

The main objective of the research is to generate an alternative approach to classical techniques in the prediction of the somatic cell count (SCC), which is the gold standard indicator of subclinical mastitis. This approach involves using the physical properties of milk such as density, the temperature at fore milking (TFM), pH, and electrical conductivity (EC) with a feed-forward backpropagation multilayer perceptron (MLP) artificial neural networks (ANN) model, which is one of the widely used machine learning techniques. The performance of the model was assessed by test with cross-validation on data that was not introduced to the model before and compared to the classical linear model (multiple linear regression) as the control model. The findings showed that the model has satisfactory results in terms of loss and performance criteria (R2=0.95, RMSE=0.01; AIC=-338). The test model (ANN) had a higher performance (AIC=-338) than the control model (AIC=-240) created with the classical linear model despite using more parameters (81). Using big data from automated milking information—like estrus cycle, lactation stage, and milk yield—on supercomputers can improve the accuracy of performance assessments in dairy farming.

The social environment experienced by livestock can have implications for their health, welfare, and subsequently, their productivity. Changes in the dairy industry have led to larger herd sizes and altered management of cows, which has impacted their social environment. Studies have shown that mixing of animals can lead to social instability of groups and expansion of herds can lead to high stocking densities resulting in social stress and negative effects on production. Yet few studies have assessed the putative impact of positive cow-cow interactions, such as proximity to preferred herd mates and engaging in grooming, on milk production and udder health. To address this, we used cattle proximity as a proxy for affiliative interactions between cows in three dairy herds in south-west England over one week study periods. We created proximity networks of dairy cows and measured cow-cow associations according to milk yield, somatic cell count (SCC; an indicator of mastitis), parity (number of lactations in the cow’s lifetime), and lactation stage (grouped by days in milk for current lactation). We then assessed associations between social factors and production and health measures (milk yield and SCC). In all three herds, cows interacted more with cows in the same parity, suggesting early social bonding may be evident later in life and that grouping animals in terms of parity might encourage affiliative interactions. Cows did not associate according to milk yield, SCC, or lactation stage. There was no significant association between milk production or SCC and the total time spent in social contact with other cows, the mean time spent with the four closest herd mates, or the number of closest herd mates of the same parity. We suggest that further research on positive social environments for dairy cattle is warranted in the interests of improving welfare and enabling a more robust assessment of the putative effects on production and health parameters

This pilot study suggests that SCC at a level of (greater than or equal to) 250,000 cells mL-1, as used in the dairy industry, is a suitable index to identify asymptomatic subclinical mastitis in lactating women since it reflects a significant increase in the inflammatory response compared to milk samples from healthy women. Using this index should aid studies into the short- and long-term consequences of subclinical mastitis for mother and infant.

One of the most serious diseases affecting dairy cattle, causing significant losses both in breeding and economy, is mastitis, an inflammation of the mammary gland. Due to the economic importance of this issue, many research teams are striving to develop an easy-to-apply and, most importantly, effective method to prevent mastitis. The use of traditional methods for mastitis detecting and treating, as well as improvement in hygienic conditions, have not yielded the expected results in combating this disease combating. Currently, the main task is to find the tools that would allow for the rapid detection of mastitis and the improvement of udder health in cows while maintaining high milk production, which is essential for the profitability of dairy cattle farming. Accurate and rapid diagnostic tools, with the simultaneous capability of identifying pathogens, may help to reduce losses. Sufficient sensitivity and specificity for tests are required to minimize the number of false-positive and false-negative cases. Efforts are also being made to determine the optimal threshold value for detecting the disease at its earliest possible stage. The estimation of somatic cell count (SCC) as a phenotypic indicator of mastitis is widely used. A more precise parameter for accurately describing udder health is the differential somatic cell count (DSCC). The well-known California Mastitis Test (CMT) is an inexpensive, easy, and rapid method for mastitis detection useful on farms. The latest diagnostic methods for mastitis utilize tests based on the activity of N-acetyl-β-d-glucosaminidase (NAGase) or lactate dehydrogenase (LDH) as well as the determination of acute phase proteins (APPs) in blood serum and milk (such as haptoglobin, serum amyloid A, fibrinogen, and ceruloplasmin). Research also focuses on the genomic improvement of mastitis resistance in successive generations, and for this purpose, many quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs) have been identified. In recent years, immunotherapy has become an increasingly common area of research, including vaccinations, T/B cell immunotherapy, RNA immunotherapy, epigenetic immunotherapy, stem cell therapy, and native secretory factors. An important aspect of the control of mastitis is the implementation of strategies that focus primarily on preventing the disease through appropriate breeding and farm management practices. In the forthcoming years, a significant challenge will be the development of universal diagnostic and therapeutic strategies that can be effectively implemented as alternatives to antibiotic therapy. Future research should prioritize the advancement of preventive and therapeutic techniques, such as immunotherapies, bacteriocins, herbal therapy, and nanoparticle technology.

Udder conformation is directly related to milk yield, cow health, workability, and welfare. Automatic milking systems (AMS, also known as milking robots) have become popular worldwide, and the number of dairy farms adopting these systems has increased considerably over the past years. In each milking visit, AMS record the location of the 4 teats as Cartesian coordinates in an xyz plan, which can then be used to derive udder conformation traits. Because AMS generate a large amount of data for individual cows per milking visit, they can contribute to an accurate assessment of important traits such as udder conformation without the addition of human classifier errors (in subjective scoring systems). Therefore, the primary objectives of this study were to estimate genomic-based genetic parameters for udder conformation traits derived from AMS records in North American Holstein cattle and to assess the genetic correlation between the derived traits for evaluating the feasibility of multitrait genomic selection for breeding cows that are more suitable for milking in AMS. The Cartesian teat coordinates measured during each milking visit were collected by 36 milking robots in 4,480 Holstein cows from 2017 to 2021, resulting in 5,317,488 records. A total of 4,118 of these Holstein cows were also genotyped for 57,600 SNPs. Five udder conformation traits were derived: udder balance (UB, mm), udder depth (UD, mm), front teat distance (FTD, mm), rear teat distance (RTD, mm), and distance front-rear (DFR, mm). In addition, 2 traits directly related to cow productivity in the system were added to the study: daily milk yield (DY) and milk electroconductivity (EC; as an indicator of mastitis). Variance components and genetic parameters for UB, UD, FTD, RTD, DFR, DY, and EC were estimated based on repeatability animal models. The estimates of heritability (± SE) for UB, UD, FTD, RTD, DFR, DY, and EC were 0.41 ± 0.02, 0.79 ± 0.01, 0.53 ± 0.02, 0.40 ± 0.02, 0.65 ± 0.02, 0.20 ± 0.02, and 0.46 ± 0.02, respectively. The repeatability estimates (± SE) for UB, UD, FTD, RTD, and DFR were 0.82 ± 0.01, 0.93 ± 0.01, 0.87 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. The strongest genetic correlations were observed between FTD and RTD (0.54 ± 0.03), UD and DFR (-0.47 ± 0.03), DFR and FTD (0.32 ± 0.03), and UD and FTD (-0.31 ± 0.03). These results suggest that udder conformation traits derived from Cartesian coordinates from AMS are moderately to highly heritable. Furthermore, the moderate genetic correlations between these traits should be considered when developing selection subindexes. The most relevant genetic correlations between traits related to cow milk productivity and udder conformation traits were between UD and EC (-0.25 ± 0.03) and between DFR and DY (0.30 ± 0.04), in which both genetic correlations are favorable. These findings will contribute to the design of genomic selection schemes for improving udder conformation in North American Holstein cattle, especially in precision dairy farms.

Simple SummarySubclinical mastitis is mostly caused by pathogenic infections, including bacteria, viruses, and fungi, with bacterial infections being the most serious. SCC is a relatively accurate indicator of the severity of mastitis and is widely used as a diagnostic indicator of mastitis in dairy cows. The purpose of this investigation is to explore the relationship between SCC, diversity in the microbiome, and SCM. Recent studies have suggested that SCM-related pathogens stay in isolation identification and drug resistance, and the relative lack of SCM is related to the change in microbiome diversity. Therefore, we aim to investigate the association between dysbacteriosis and the development of SCM using 16Sr RNA high-throughput sequencing and molecular diagnostic techniques to analyze the structure and abundance of microbiota in the mammary microenvironment in different SCC ranges. The roles of Staphylococcus spp. and Streptococcus spp., as well as Enterobacter spp., in various udder quarters and their relationship with inflammation and mastitis have been established. Bacillus spp. has also been associated with the development of mastitis. Among them, Pseudomonas, Serratia, and Acinetobacter can be used as landmark genera for the transition from the subclinical to the clinical state of bovine mastitis.Subclinical mastitis is a common disease that threatens the welfare and health of dairy cows and causes huge economic losses. Somatic cell count (SCC) is the most suitable indirect index used to evaluate the degree of mastitis. To explore the relationship between SCC, diversity in the microbiome, and subclinical mastitis, we performed next-generation sequencing of the 16S rRNA gene of cow’s milk with different SCC ranges. The data obtained showed that the microbiota was rich and coordinated with SCC below 2 × 105. SCC above 2 × 105 showed a decrease in the diversity of microbial genera. When SCC was below 2 × 105, the phylum Actinobacteriota accounted for the most. When SCC was between 2 × 105 and 5 × 105, Firmicutes accounted for the most, and when SCC exceeded 5 × 105, Firmicutes and Proteobacteria accounted for the most. Pathogenic genera such as Streptococcus spp. were absent, while SCC above 2 × 105 showed a decrease in the diversity of microbial genera. SCC was positively correlated with the percentage of Romboutsia, Turicibacter, and Paeniclostridium and negatively correlated with the percentage of Staphylococcus, Psychrobacter, Aerococcus, and Streptococcus. Romboutsia decreased 6.19 times after the SCC exceeded 2 × 105; the SCC increased exponentially from 2 × 105 to 5 × 105 and above 1 × 106 in Psychrobacter. Analysis of the microbiota of the different SCC ranges suggests that the development of mastitis may not only be a primary infection but may also be the result of dysbiosis in the mammary gland.

In the present study, thermal images of the short milking tube of the milking machine representing four udder quarters independently attached to a milking animal, along with pre-milking and post-milking udder and teat thermograms, were taken using a hand-held digital infrared thermal camera (DarviDTL007) during morning milking of lactating Murrah buffaloes (n = 132) to assess the mastitis status. California mastitis test (CMT) and somatic cell count (SCC) of milk samples were carried out to screen the udder quarters as healthy, subclinical (SCM), and clinical mastitis (CM). The thermograms revealed an increase (p < 0.05) of 2.19 and 3.72ºC in the mean values of short milking tube (SMT) surface temperature among SCM and CM quarters compared to healthy quarters, respectively. The mean values of udder skin surface temperature (USST) for pre-milking, milking, and post-milking of SCM and CM compared to healthy quarters showed an increase (p < 0.05) of 2.17, 1.96, and 1.61ºC and 3.11, 2.88, and 2.73ºC, respectively. Similarly, compared to healthy quarters, the mean values of teat skin surface temperature (TSST) for pre-milking and post-milking of SCM and CM showed an increase (p < 0.05) of 2.12 and 1.66ºC and 3.07 and 2.45ºC, respectively. Also, CMT and SCC results showed a strong positive correlation (r = 0.68-0.91, p < 0.01) with all the thermographic parameters. Thus, thermograms of SMT alone can be used as an efficient detection tool in assessing SCM among Murrah buffaloes.

Mastitis is an inflammatory disease in dairy cows, causing economic losses and reducing animal welfare. In order to contribute for the discovery of early and noninvasive indicators, our objective was to determine the effects of a lipopolysaccharide (LPS) challenge on the microRNA profile (miRNome) of milk fat, using microarray analyses in cows. Cows were fed a lactation diet at ad libitum intake (n = 6). At 27 ± 3 days in milk, cows were injected with 50 µg of LPS Escherichia coli in one healthy rear mammary quarter. Milk samples were collected just before LPS challenge (LPS−) and 6.5 h after LPS challenge (LPS +) from the same cows. Microarray analysis was performed using customized 8 × 60 K ruminant miRNA microarrays to compare LPS− to LPS + miRNome. In silico functional analyses were performed using OmicsNet and Mienturnet software. MiRNome comparison between LPS− and LPS + identified 37 differentially abundant miRNAs (q-value ≤ 0.05). The predicted target genes of the 37 differentially abundant miRNAs are mostly involved in cell life including apoptosis, cell cycle, proliferation and differentiation and in gene expression processes. MiRNome analyses suggest that miRNAs profile is related to the inflammation response of the mammary gland. In conclusion, we demonstrated that milk fat might be an easy and rapid source of miRNAs that are potential indicators of early mastitis in cows.

Abstract The objective was to evaluate the potential for the use of a thermal imaging system for the identification of subclinical mastitis in dairy cows. Multiparous Holstein dairy cows (n = 131) within 150 days in milk (DIM; range 65 to 155) were enrolled in the study at a commercial dairy farm in central Texas. Milk samples (10 mL) were collected from each quarter separately and evaluated for somatic cell counts (SCC; n*1000), fat (%), protein (%), lactose (%), SNF (%), BHBA (mM/L), acetone (mM/L), and milk urea nitrogen (MUN; mg/dL). A minimum of four thermal images per cow representing all four quarters were captured using a handheld infrared camera (Fluke TiS 75+) from approximately one-meter distance from the cow. All images were taken inside the milking parlor and after the morning milking during February 2023 to minimize the impact of daily ambient temperatures. Skin temperatures from each quarter (maximum, minimum, average) were extracted using Fluke Connect software. A total of 524 quarter-level milk samples and corresponding udder images were obtained, and 10 images were discarded from analyses due to the lack of appropriate resolution and focus. To prevent collinearity, a correlation matrix was developed using PROC CORR in SAS and variables with significant correlations were removed from the model. Subclinical mastitis was defined at the quarter level when SCC &amp;gt; 250,000. Statistical analyses were performed using mixed linear regression models (PROC MIXED) with cow and DIM as random effects in the model, and logistic regression model (PROC LOGISTIC) was fitted to evaluate the probability of the disease based on temperature and milk variables. Maximum and average udder temperatures did not demonstrate a significant relationship with subclinical mastitis status whereas minimum udder temperature was greater in sick quarters (29.6 ± 0.52 vs 27.5 ± 0.45; P = 0.003). Increased minimum udder temperature was also associated with BHBA (P &amp;lt; 0.0001), acetone (P &amp;lt; 0.0004), and MUN (P &amp;lt; 0.0007) concentrations in milk. Cows with subclinical mastitis had decreased BHBA (0.14 ± 0.037 vs 0.28 ± 0.031; P = 0.0036) and acetone (0.017 ± 0.003 vs 0.044 ± 0.003; P &amp;lt; 0.001). Odds of subclinical mastitis were associated with MUN values in milk (OR 0.94; CI 0.89 - 0.98) but not with any udder surface temperature variables. We conclude that an increase in the minimum temperature of the udder could be used as an indicator of subclinical mastitis but studies with larger datasets and using advanced image analysis techniques are warranted to validate these results.