[Investigations of a controlled, decision tree based procedure for Selective Dry Cow Treatment in Bavarian dairy farms]. / Untersuchungen zu einem kontrollierten, entscheidungsbaumbasierten Verfahren des Selektiven Trockenstellens in Bayerischen Milchviehbetrieben.

OBJECTIVE: Four parameters of a decision tree for Selective Dry Cow Treatment (SDCT), examined in a previous study, were analyzed regarding their efficacy in detecting cows for dry cow treatment (DCT, use of intramammary antimicrobials). This study set out to review wether all parameters (somatic cell count [SCC≥ 200 000 SC/ml 3 months' milk yield recordings prior dry off (DO)], clinical mastitis history during lactation [≥1 CM], culturing [14d prior DO, detection of major pathogens] and California-Mastitis-Test [CMT, > rate 1/+ at DO]) are necessary for accurate decision making, whether there are possible alternatives to replace culturing, and whether a simplified model could replace the decision tree. MATERIAL AND METHODS: Records of 18 Bavarian dairy farms from June 2015 to August 2017 were processed. Data analysis was carried out by means of descriptive statistics, as well as employing a binary cost sensitive classification tree and logit-models. For statistical analyses the outcomes of the full 4-parameter decision tree were taken as ground truth. RESULTS: 848 drying off procedures in 739 dairy cows (CDO) were included. SCC and CMT selected 88.1%, in combination with CM 95.6% of the cows that received DCT (n=494). Without culturing, 22 (4.4%) with major pathogens (8x Staphylococcus [S.] aureus) infected CDO would have been misclassified as not needing DCT. The average of geometric mean SCC (within 100 d prior DO) for CDO with negative results in culturing was<100 000 SC/ml milk, 100 000-150 000 SC/ml for CDO infected with minor pathogens, and ≥ 150 000 SC/ml for CDO infected with major pathogens (excluding S.aureus). Using SCC during lactation (at least 1x > 200 000 SC/ml) and positive CMT to select CDO for DCT, contrary to the decision tree, 37 CDO (4.4%) would have been treated "incorrectly without" and 43 CDO (5.1%) "unnecessarily with" DCT. Modifications were identified, such as SCC<131 000 SC/ml within 100 d prior to DO for detecting CDO with no growth or minor pathogens in culturing. The best model for grading CDO for or against DCT (CDO without CM and SCC<200 000 SC/ml [last 3 months prior DO]) had metrics of AUC=0.74, Accuracy=0.778, balanced Accuracy=0.63, Sensitivity=0.92 and Specificity=0.33. CONCLUSIONS: Combining the decision tree's parameters SCC, CMT and CM renders suitable selection criteria under the conditions of this study. When omitting culturing, lower thresholds for SCC should be considered for each farm individually to select CDO for DCT. Nonetheless, the most accurate model could not replace the full decision tree.

Development of a prototypic, field-usable diagnostic tool for the detection of gram-positive cocci-induced mastitis in cattle.

BACKGROUND: Bovine mastitis is one of the most widespread diseases affecting cattle, leading to significant losses for the dairy industry. Currently, the so-called gold standard in mastitis diagnosis involves determining the somatic cell count (SCC). Apart from a number of advantages, this method has one serious flaw: It does not identify the etiological factor causing a particular infection, making it impossible to introduce targeted antimicrobial therapy. This can contribute to multidrug-resistance in bacterial species. The diagnostic market lacks a test that has the advantages of SCC and also recognizes the species of pathogen causing the inflammation. Therefore, the aim of our study was to develop a lateral flow immunoassay (LFIA) based on elongation factor Tu for identifying most prevalent Gram-positive cocci responsible for causing mastitis including Streptococcus uberis, Streptococcus agalactiae and Staphylococcus aureus. RESULTS: As a result, we showed that the assay for S. uberis detection demonstrated a specificity of 89.02%, a sensitivity of 43.59%, and an accuracy of 80.3%. In turn, the second variant - assay for Gram-positive cocci reached a specificity of 95.59%, a sensitivity of 43.28%, and an accuracy of 78.33%. CONCLUSIONS: Our study shows that EF-Tu is a promising target for LFIA and we have delivered evidence that further evaluation could improve test parameters and fill the gap in the mastitis diagnostics market.

Considering potential roles of selected MicroRNAs in evaluating subclinical mastitis and Milk quality in California mastitis test (+) and infected bovine milk.

This study investigates the relationships between subclinical mastitis and milk quality with selected microRNAs in cow milk. California Mastitis Test (CMT)-positive (n = 20) and negative (n = 20) samples were compared (Experiment I). Additionally, samples with CMT-positive but microbiological-negative, as well as positive for only Staphylococcus subspecies (Staph spp.) and only Streptococcus subspecies (Strep spp.) were examined (Experiment II). Four groups were formed in Experiment II: Group I (CMT and microbiological-negative) (n = 20), Group II (CMT-positive but microbiological-negative) (n = 10), Group III (Staph spp.) (n = 5), Group IV (Strep spp.) (n = 5). While electrical conductivity, somatic cell count (SCC), malondialdehyde (MDA) increased, miR-27a-3p and miR-223 upregulated and miR-125b downregulated in the CMT-positive group in Experiment I. SCC and MDA were higher in CMT-positive groups. miR-27a-3p and miR-223 upregulated in Groups III and IV. While miR-155 is upregulated, miR-125b downregulated in Group IV. Milk fat is positively correlated with miR-148a and miR-223. As miR-27a-3p positively correlated with SCC and MDA, miR-125b negatively correlated with electrical conductivity and SCC. miR-148a and MDA were positively correlated. miR-155 was correlated with fat-free dry matter, protein, lactose, and freezing point. miR-223 was positively correlated with SCC and miR-148a. Results particularly highlight miR-27a-3p and miR-223 as potential biomarkers in subclinical mastitis, especially those caused by Staph spp. and Strep spp., while miR-148a, miR-155, and miR-223 stand out in determining milk quality.

The determination of mastitis severity at 4-level using Milk physical properties: A deep learning approach via MLP and evaluation at different SCC thresholds.

Current research aims to generate an alternative model to classical methods in the determination of subclinical mastitis at 4 levels (healthy, suspicious, subclinical, and clinical). For this purpose, multilayer perceptron (MLP) artificial neural networks (ANN) was developed as test model. 5 variables from the physical properties of milk somatic cell count (SCC), electrical conductivity (EC), pH, density, and temperature at fore milking (TFM) were included in the model in the classification of mastitis. Model performance was validated on test data (%25) and compared with the multinomial logistic regression (MNLR). MLP model has shown a satisfactory performance with an accuracy of 95.14% and - 141 of AIC score better than the control model (MNLR) of 80.27% and - 133 AIC despite using higher number of parameters (104). Since the main problem is to diagnose subclinical mastitis, which does not cause any visible symptoms, it was important to distinguish between absolute subclinical (suspicious excluded positives) and absolute healthy (suspicious included positives) ones. Therefore, optimum cut-off threshold was evaluated for these two different scenarios with only variable SCC the gold standard indicator of subclinical mastitis and results were compared in the interpretation of model performance. The results show that the 5-variable MLP model exhibits a high sensitivity of 93.22% (AUC = 0.95 for healthy ones) at low cutoff thresholds as well. New studies should provide a better understanding by evaluating economics, sustainability, animal welfare and health aspects together to determine the optimal threshold value.

Milk miRNA expression in buffaloes as a potential biomarker for mastitis.

BACKGROUND: Buffaloes have the highest potential for production due to a promising gene pool that is being enhanced and upgraded. Mastitis is a significant health impediment that greatly diminishes milk yield and quality, affecting rural farmers' livelihoods. The traditional gold standard used for diagnosing mastitis or subclinical mastitis is CMT, but it has the drawback of false positive or negative results. Subclinical mastitis, if not treated promptly, can lead to mammary tumors. To address the gap in early diagnosis of subclinical mastitis in CMT-negative milk of buffaloes, we performed a retrospective analysis and evaluated the milk miRNA expression profiles as potential biomarkers. RESULTS: Thirty buffalo milk samples based on clinical signs and CMT were divided into normal, subclinical, and clinical mastitis. SCC evaluation showed significant differences between the groups. The data analysis demonstrated that the elevation of miR-146a and miR-383 differed substantially between normal, subclinical, and clinical mastitis milk of buffaloes with 100% sensitivity and specificity. The relationship of SCC with miR-146a and miR-383 in normal/healthy and subclinical mastitis was positively correlated. CONCLUSION: The overexpression of miR-146a and miR-383 is associated with inflammation. It can be a valuable prognostic and most sensitive biomarker for early mastitis detection in buffaloes with SCC below 2 lakhs and CMT-ve, enhancing the accuracy of subclinical mastitis diagnosis.

Biomarker and proteome analysis of milk from dairy cows with clinical mastitis: Determining the effect of different bacterial pathogens on the response to infection.

Antimicrobial usage (AMU) could be reduced by differentiating the causative bacteria in cases of clinical mastitis (CM) as either Gram-positive or Gram-negative bacteria or identifying whether the case is culture-negative (no growth, NG) mastitis. Immunoassays for biomarker analysis and a Tandem Mass Tag (TMT) proteomic investigation were employed to identify differences between samples of milk from cows with CM caused by different bacteria. A total of 94 milk samples were collected from cows diagnosed with CM across seven farms in Scotland, categorized by severity as mild (score 1), moderate (score 2), or severe (score 3). Bovine haptoglobin (Hp), milk amyloid A (MAA), C-reactive protein (CRP), lactoferrin (LF), α-lactalbumin (LA) and cathelicidin (CATHL) were significantly higher in milk from cows with CM, regardless of culture results, than in milk from healthy cows (all P-values <0.001). Milk cathelicidin (CATHL) was evaluated using a novel ELISA technique that utilises an antibody to a peptide sequence of SSEANLYRLLELD (aa49-61) common to CATHL 1-7 isoforms. A classification tree was fitted on the six biomarkers to predict Gram-positive bacteria within mastitis severity scores 1 or 2, revealing that compared to the rest of the samples, Gram-positive samples were associated with CRP < 9.5 µg/ml and LF ≥ 325 µg/ml and MAA < 16 µg/ml. Sensitivity of the tree model was 64%, the specificity was 91%, and the overall misclassification rate was 18%. The area under the ROC curve for this tree model was 0.836 (95% bootstrap confidence interval: 0.742; 0.917). TMT proteomic analysis revealed little difference between the groups in protein abundance when the three groups (Gram-positive, Gram-negative and no growth) were compared, however when each group was compared against the entirety of the remaining samples, 28 differentially abundant protein were identified including ß-lactoglobulin and ribonuclease. Whilst further research is required to draw together and refine a suitable biomarker panel and diagnostic algorithm for differentiating Gram- positive/negative and NG CM, these results have highlighted a potential panel and diagnostic decision tree. Host-derived milk biomarkers offer significant potential to refine and reduce AMU and circumvent the many challenges associated with microbiological culture, both within the lab and on the farm, while providing the added benefit of reducing turnaround time from 14 to 16 h of microbiological culture to just 15 min with a lateral flow device (LFD).

Factors affecting N-acetyl-ß-D-glucosaminidase as an indicator for mastitis detection in dairy sheep.

The study of new indirect methods for mastitis detection is of great relevance both at the economic level of the farm and dairies, and in terms of consumer health, and animal welfare. These methods help us to monitor the disease and speed up the decision-making process on treatment of the affected animal and the destination of the milk. The main aim of this work was to study the effect of intramammary infection and other non-infectious factors on the activity of the enzyme N-acetyl-ß-D-glucosaminidase (NAGase) in milk, in order to evaluate its use as an indicator for the early diagnosis of mastitis in sheep that could be less expensive, easier to measure and a better marker of inflammation or complementary to existing methods such as somatic cell count (SCC). Seven biweekly samplings were carried out, in which NAGase activity, SCC and milk were analyzed. Glands were classified according to their sanitary status based on the results of the SCC and bacteriological analysis. Non-infectious factors such as lactation stage, parity number and milking session had a statistically significant effect on NAGase values, finding the highest NAGase values at the onset and end of the study, in infectious mastitic glands of multiparous females and at morning milking. However, among the NAGase variation factors studied, the health status of the gland was the factor that caused the highest variation in enzyme levels, with infectious mastitic glands showing higher values than healthy glands. The predictive ability of NAGase was also studied by means of several logistic regression models, with the one that included NAGase together with lactation stage and parity obtaining the best results if sensitivity is to be prioritized, or the model that included NAGase, lactation stage, parity, milking and production if specificity is to be prioritized. From the results obtained, it can be concluded that the use of NAGase as an intramammary infection detection method in sheep can be useful when non-infectious factors that cause changes in the concentration of the enzyme are also considered.

Comparison of bacteriological culture method and multiplex real-time PCR for detection of mastitis.

This study includes the evaluation of multiplex real-time PCR (rPCR) kit, which was developed to provide rapid diagnosis of mastitis infections, by working with milk samples of 2 different sources of mastitis and comparing the results with the classical bacteriological culture method (BC). A total of 273 bacteria were isolated in 226 samples (47.88%) out of 472 samples by BC. These were 139 (50.91%) Staphylococcus spp., 61 (22.34%) Streptococcus spp., 15 (5.49%) E. coli, 8 (2.93%) Enterococcus spp., 50 (18.31%) other bacteria. When we look at the multiplex rPCR results; 1052 positive were obtained for the gene regions of 14 different bacteria, 1 yeast, and 1 ß-lactamase gene examined in 472 samples. While no searched gene region was found by rPCR in 78 (16.5%) of the 472 samples studied, at least 1 gene was detected in 394 (83.5%) samples. These 1052 positive samples by rPCR were; 263 (28.43%) Staphylococcus spp., 51 (5.51%) S. aureus, 57 (6.16%) Enterococcus spp., 49 (5.29%) C. bovis, 16 (1.73%) S. dysgalactiae, 84 (9.08%) S. agalactiae, 71 (7.67%) S. uberis, 73 (7.89%) E. coli, 14 (1.51%) Prototheca spp., 39 (4.21%) T. pyogenes/P. indolicus, 5 (0.54%) S. marcescens, 15 (1.62%) K. oxytoca/pneumonia, 117 (12.64%) Mycoplasma spp., 31 (3.35%) M. bovis, 40 (4.32%) yeast, and 127 samples (26.90%) were ß-lactamase positive. When the antibiotic resistance of the isolates was evaluated, 78 (31.96%) tetracycline, 72 (29.5%) penicillin, and 60 (24.59%) clindamycin resistance were observed predominantly in Gram-positive isolates, while 6 (23.07%) tigecycline, 6 (23.07%) netilmicin, 6 (23.07%) pipercillin resistance was found in gram-negative isolates. While a bacteria and/or yeast gene was found by rPCR in 187 of 246 (76.01%) samples with no bacterial growth, a bacterium was isolated with BC in only 20 (8.84%) samples whose gene region was not found by rPCR. As a result, the multiplex rPCR system used in the diagnosis of mastitis has been found to be quite reliable as it can detect a large number of bacteria in a very short time compared to classical methods. Therefore, we advise the use of rPCR and/or culture for confirmation of clinical signs in mastitis and at routine mastitis surveillance.

Laboratory evaluation of a rapid diagnostic test for dairy mastitis.

Rapid diagnostic tests that differentiate between Gram positive, Gram negative and the absence of aerobic bacteria in milk samples from dairy cows with clinical mastitis can support antimicrobial treatment decisions and contribute to a more prudent use of antimicrobials in the dairy industry. The objective of this study was to evaluate the test characteristics of the novel rapid BACT mastitis test in discriminating causes of clinical mastitis under laboratory conditions. Test outcomes of 155 milk samples from clinical mastitis cases were incubated for 14-16 h in the BACT test and compared to results of bacteriological culture. The accuracy for detection of bacterial growth and Gram positive growth was 91 and 89%, respectively. The BACT test could provide an accurate and relatively fast decision tool for farmers to aid in antimicrobial treatment decisions in cases of clinical mastitis.

Reliability of udder infrared thermography as a non-invasive technology for early detection of sub-clinical mastitis in Sahiwal (Bos indicus) cows under semi-intensive production system.

The present study focused on Sahiwal cows, a prominent milch breed in tropical India, to correlate udder temperature with physiological markers of stress and inflammation during subclinical mastitis (SCM). The primary goal was to assess the potential of udder infrared thermography for the early detection of SCM under the semi-intensive production. Cows were categorized based on milk somatic cell counts (SCC), with healthy (H) cows having SCC <2 × 105 cells/mL and no history of mastitis, and cows with subclinical mastitis (SCM) and initial stages of clinical mastitis (CM) having quarter milk SCC of 2-5 × 105 and >5 × 105 cells/mL, respectively. Firstly, udder thermograms were analysed for udder skin surface temperature (USST), teat skin surface temperature (TSST), and teat apex temperature (TAT) using Fluke software to determine the optimal site for temperature measurement during intramammary infection. Secondly, milk samples were collected for automatic estimation of compositional changes, electrical conductivity, and pH. Thirdly, milk whey was separated for quantifying stress and inflammatory indicators, including cortisol, prolactin, and acute-phase proteins (APPs): milk amyloid A and milk haptoglobin using bovine-specific ELISA kits. Significant increases (p < 0.01) in USST, TSST, TAT, cortisol, and APPs were observed in SCM and CM compared to healthy cows, while prolactin levels decreased (p < 0.01). The correlation matrix revealed strong positive correlations of SCC with USST (r = 0.84, p < 0.01). In ROC analysis, USST demonstrated cut-off values of 37.74 and 39.58 °C, with accuracy (p < 0.05) of 98% for SCM and 95% for CM, surpassing both TAT and TSST. Therefore, the combination of these non-invasive methods increases the reliability and accuracy of infrared thermography for early detection of SCM, providing valuable insights for the development of a protocol for routine screening and udder health monitoring in indigenous dairy cows.

Performance evaluation of a rapid immunochromatographic test kit in detecting bovine mastitis-causing streptococci.

Mastitis causes significant economic losses to the dairy industry due to decreased milk production in infected cows. Identification of mastitis-causing pathogens, such as streptococci, is necessary for selecting an effective antibiotic for treating mastitis. Although bacterial cultivation is widely used for pathogen identification, it requires more than 24 hr to complete. Contrarily, Lateral flow assays are simple, rapid, and inexpensive testing procedures. In this study, the effectiveness of an immunochromatographic test kit for detecting streptococci in milk samples from cows with clinical mastitis was evaluated as an alternative to bacterial cultivation. The performance of the immunochromatographic test kit for detecting mastitis-causing pathogens was compared with that of bacterial cultivation and real-time quantitative polymerase chain reaction (qPCR). The sensitivity and specificity of the immunochromatographic test kit were 0.800 and 0.875, respectively, compared with bacterial cultivation. Additionally, the κ statistic values of the immunochromatographic test kit was 0.667, indicating substantial agreement with the results of bacterial cultivation. Statistically, sensitivity and specificity of the immunochromatographic kit and real-time qPCR did not differ significantly; thus, the immunochromatographic test kit detected mastitis-causing streptococci as effectively as real-time qPCR. Therefore, the immunochromatographic kit is a rapid, inexpensive, and simple method for detecting streptococci and contributes to the timely selection of appropriate antibiotics for treatment and promotes early recovery from mastitis.

Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: Bagging, boosting, stacking, and super-learner ensembles versus single machine learning models.

Mastitis has a substantial impact on the dairy industry across the world, causing dairy producers to suffer losses due to the reduced quality and quantity of produced milk. A further problem, related to this issue, is the excessive use of antibiotics that leads to the development of resistance in different bacterial strains. The growing consumer awareness oriented toward food safety and rational use of antibiotics has promoted the search for new methods of early identification of cows that may be at risk of developing the disease. Subclinical mastitis does not cause any visible changes to the udder or milk, and therefore it is more difficult to detect than clinical mastitis. The collection of large amounts of data related to milk performance of cows allows using machine learning (ML) methods to build models that could be used for classifying cows into healthy and at risk of subclinical mastitis. The data used for the purpose of this study included information from routine milk recording procedures. The dataset consisted of 19,856 records of 2,227 Polish Holstein-Friesian cows from 3 herds. The authors decided to use the approach of building ensemble ML models, in particular bagging, boosting, stacking, and super-learner models, and comparing them for accuracy of identification of disease-affected cows against single ML models based on the support vector machines, logistic regression, Gaussian Naive Bayes, k-nearest neighbors, and decision tree algorithms. The models were trained and evaluated based on the information recorded for herd 1 and using an 80:20 train-test split ratio according to animal ID (to avoid data leakage). The information recorded for herds 2 and 3 was only used to evaluate on unseen data models developed using the herd 1 dataset. Among the single ML models, the support vector machines model was found to be the most accurate in predicting subclinical mastitis at subsequent test day when used both for the training set (mean F1-score of 0.760) and the testing sets containing data for herds 1, 2, and 3 (F1-score of 0.778, 0.790, and 0.741 respectively). The gradient boosting model was found to be the best performing model among the ensemble ML models (F1-score of 0.762, 0.779, 0.791, and 0.723 for the training set and the testing sets, respectively). The super-learner model, featuring the most advanced design and logistic regression in the meta layer, achieved the highest mean F1-score of 0.775 during the cross validation; however, it was characterized by a slightly worse prediction accuracy of the testing sets (mean F1-score of 0.768, 0.790, and 0.693 for herds 1, 2 and 3 respectively). The study findings confirm the promising role of ensemble ML methods, which were found to be slightly superior with respect to most of the single ML models.

Basic concepts, recent advances, and future perspectives in the diagnosis of bovine mastitis.

Mastitis is one of the most widespread infectious diseases that adversely affects the profitability of the dairy industry worldwide. Accurate diagnosis and identification of pathogens early to cull infected animals and minimize the spread of infection in herds is critical for improving treatment effects and dairy farm welfare. The major pathogens causing mastitis and pathogenesis are assessed first. The most recent and advanced strategies for detecting mastitis, including genomics and proteomics approaches, are then evaluated . Finally, the advantages and disadvantages of each technique, potential research directions, and future perspectives are reported. This review provides a theoretical basis to help veterinarians select the most sensitive, specific, and cost-effective approach for detecting bovine mastitis early.

[Haptoglobin as an indicator for diseases during early lactation of dairy cows, with particular consideration of udder health]. / Haptoglobin als Indikator für Erkrankungen in der Frühlaktation von Milchkühen unter besonderer Berücksichtigung der Eutergesundheit.

s an acute-phase protein Haptoglobin (HP) is part of the non-specific immune response and represents a strong indicator for inflammatory conditions in cattle. The purpose of this article is to provide an overview of previous study results on serum and milk HP related to diseases in early lactation with special consideration of udder health. During inflammatory diseases of the reproductive tract, metabolism and musculoskeletal system, HP increases in the serum and may serve as a non-specific indicator for diseases during early lactation. Threshold values are available for the differentiation of healthy from diseased animals. A correlation exists between HP in blood and milk. The HP concentration in milk is not only influenced by systemic disorders, as the udder epithelium is also independently capable of synthesizing HP in case of an infection. In mastitis, HP concentration may be used to estimate the severity of the disease. In addition, HP may provide certain suspicions regarding the causative pathogen. Threshold values for milk HP are available for the differentiation of healthy individuals from subclinically resp. clinically affected animals.

Development of Loop-Mediated Isothermal Amplification for the Detection of Prototheca bovis Directly from Milk Samples of Dairy Cattle.

Prototheca bovis is an algal emerging pathogen in dairy farms causing refractory protothecal mastitis with increasing incidence worldwide and significant economic impact. P. bovis infects cows throughout the lactation cycle, including dry periods, and can persist in the udder and the environment for a long time. Since P. bovis does not respond to treatments with antibiotics, the suggested sanitary measure to restrict the spread is culling infected animals. A point-of-care test for early detection of the causative agent is critically needed to guide farm management and the appropriate treatment of mastitis. Loop-mediated isothermal amplification (LAMP) is a highly specific molecular method, time-saving, cost-effective and easy to perform in limited settings. This study aimed to develop a LAMP assay for P. bovis detection directly from milk samples; it was employed in conjunction with a commercial DNA extraction kit which was previously used to extract DNA from milk specimens containing microbes. The LAMP assay detected P. bovis DNA within 1 h in milk samples spiked with P. bovis at a concentration of 50 cells/µL, enabling on-farm disease monitoring and decision-making based on a reliable diagnosis. The LAMP method will contribute to the accurate and rapid identification of P. bovis in asymptomatic or recurrent mastitis cases and consequently aid the implementation of targeted control measures and the reduction of losses in milk production.

Genetic identification of Staphylococcus aureus isolates from cultured milk samples of bovine mastitis using isothermal amplification with CRISPR/Cas12a-based molecular assay.

Bovine mastitis, a common and costly disease in dairy cattle, is primarily caused by Staphylococcus aureus. Timely and accurate detection of this pathogen is crucial for effective disease management. In this study, we developed and validated a novel molecular diagnostic assay based on the CRISPR/Cas12a system coupled with Recombinase Polymerase Amplification (RPA) and Loop-Mediated Isothermal Amplification (LAMP). We utilized specific primers targeting the nucleotide sequences of the S.aureus genes of interest, such as nuc and sea. RPA/LAMP reactions were performed under optimized conditions, and the resulting products were subsequently subjected to CRISPR/Cas12a detection. The CRISPR/Cas12a assay successfully detected the target nuc and sea genes, with a limit of detection of 104 and 102 gene copies per reaction, respectively. All 13 S.aureus clinical isolates were identified by RPA-CRISPR/Cas12a assay. The total reaction time is approximately 1 h. The assay demonstrated high sensitivity for the detection of S.aureus in both laboratory and clinical samples.

Exploring the sources of variation of electrical conductivity and total and differential somatic cell count in Italian Mediterranean buffaloes.

In the buffalo dairy sector, a huge effort is still needed to improve mastitis prevention, detection, and management. Electrical conductivity (EC) and total somatic cell count (SCC) are well-known indirect indicators of mastitis. Differential somatic cell count (DSCC), which represents the proportion of neutrophils and lymphocytes on the total SCC, is instead a novel phenotype collected in the dairy cattle sector in the last lustrum. As little is known about this novel trait in dairy buffalo, in the present study we explored the nongenetic factors affecting DSCC, as well as EC and total somatic cell score (SCS), in the Italian Mediterranean buffalo. The data set used for the analysis included 14,571 test-day (TD) records of 1,501 animals from 6 herds, and climatic information of the sampling locations. The original data were filtered to exclude animals with less than 3 TD per lactation and, for the investigated traits, outliers beyond 4 standard deviations. In the statistical model we included the fixed effects of herd (6 classes), days in milk (DIM; 10 classes of 30 d, with the last being an open class until 360 d), parity (6 classes, from 1 to 6+), year-season of calving (11 classes, from summer 2019 to winter 2021/2022), year-season of sampling (9 classes, from spring 2020 to spring 2022), production level (4 classes based on quartiles of average milk yield by herd), and temperature-humidity index (THI; 4 classes based on quartiles, calculated using the average temperature and relative humidity of the 5 d before sampling). Average EC, SCS, and DSCC vary across herds. Considering DIM, greater EC values were observed at the beginning and the end of lactation; SCS was slightly lower, but DSCC was greater around the lactation peak. Increased EC, SCS, and DSCC levels with increasing parity were reported. Year-season calving and year-season sampling only slightly affected the variation of the investigated traits. Milk of high-producing buffaloes was characterized by lower EC and SCS mean values, nevertheless it had slightly greater DSCC percentages. Buffaloes grouped in the highest THI classes (classes 3 and 4) showed, on average, greater EC, SCS, and DSCC in comparison to the lower classes, especially to class 2. Results of the present study represent a preliminary as well as necessary step for the possible future inclusion of EC, SCS, or DSCC in breeding programs aimed to improve mastitis resistance in dairy buffaloes.

Seasonal assessment of mastitis using thermogram analysis in Sahiwal cows.

"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.

Milk as diagnostic fluid for udder health management.

BACKGROUND: Mastitis is the major disease affecting milk production of dairy cattle, and milk is an obvious substrate for the detection of both the inflammation and its causative infectious agents at quarter, cow, or herd levels. In this review, we examine the use of milk to detect inflammation based on somatic cell count (SCC) and other biomarkers, and for the detection of mastitis pathogens through culture-based and culture-free methods. FINDINGS: The use of SCC at a cow or bulk milk level to guide udder health management in lactation is well-established, and SCC is increasingly used to guide selective dry cow treatment. Other markers of inflammation include electrical conductivity, which is used commercially, and markers of disease severity such as acute phase proteins but are not pathogen-specific. Some pathogen-specific markers based on humoral immune responses are available, but their value in udder health management is largely untested. Commercial pathogen detection is based on culture or polymerase chain reaction, with other tests, for example, loop-mediated isothermal amplification or 16S microbiome analysis still at the research or development stage. Matrix-assisted laser desorption ionisation time of flight (MALDI-ToF) is increasingly used for the identification of cultured organisms whilst application directly to milk needs further development. Details of test sensitivity, specificity, and use of the various technologies may differ between quarter, cow, and bulk milk applications. CONCLUSIONS: There is a growing array of diagnostic assays that can be used to detect markers of inflammation or infection in milk. The value of some of these methods in on-farm udder health improvement programs is yet to be demonstrated whilst methods with proven value may be underutilised.

Serum metabolome differences associated with subclinical intramammary infection caused by Streptococcus agalactiae and Prototheca spp. in multiparous dairy cows.

Mastitis is one of the most significant diseases in dairy cows and causes several economic losses. Somatic cell count (SCC) is often used as an indirect diagnostic tool for mastitis, especially for subclinical mastitis (SCM) where no symptoms or signs can be detected. Streptococcus agalactiae is one of the main causes of contagious mastitis, and Prototheca spp. is an alga-inducing environmental mastitis that is not always correlated with increased milk SCC. The aim of this study was to evaluate the changes in the metabolomic profile of blood in relation to subclinical intramammary infection (IMI) in dairy cows. In addition, differences resulting from the etiologic agent causing mastitis were also considered. Forty Holstein-Friesian dairy cows in mid and late lactation were enrolled in this cross-sectional design study. Based on the bacteriological examination of milk, the animals were divided into 3 groups: group CTR (control group; n = 16), group A (affected by SCM with IMI caused by Strep. agalactiae; n = 17), and group P (affected by SCM with IMI caused by Prototheca spp.; n = 7). Blood samples from the jugular vein were collected in tubes containing clot activator; the serum aliquot was stored until metabolomic analysis by 1H-nuclear magnetic resonance spectroscopy. Statistical analysis was conducted by fitting a linear model with the group as the fixed effect and SCC as the covariate. Forty-two metabolites were identified, and among them 10 were significantly different among groups. Groups A and P showed greater levels of His and lactose and lower levels of acetate, Asn, and dimethylamine compared with group CTR. Group A showed high levels of Val, and group P showed high levels of Cit and methylguanidine, as well as lower levels of 3-hydroxybutyrate, acetone, allantoin, carnitine, citrate, and ethanol. These metabolites were related to ruminal fermentations, energy metabolism, urea synthesis and metabolism, immune and inflammatory response, and mammary gland permeability. These results suggest systemic involvement with subclinical IMI and that the metabolic profile of animals with SCM undergoes changes related to the etiological agent of mastitis.