VF AFK Virškinimo fiziologijos ir patologijos centras (13.03.02)

Rumen microbial fermentation plays a central role in nutrient utilization and milk production in dairy cows. This study evaluated the effects of supplementation with exogenous fibrolytic enzymes, alone or in combination with live yeast on rumen microbiota, fermentation characteristics, nitrogen-related metabolites, and production performance during the transition from outdoor grazing to indoor housing. Thirty Lithuanian Red dairy cows were assigned to control (CTR), enzyme (E), or enzyme plus yeast (YE) treatments across outdoor (OD) and transit (T) periods, while nine cows (three per group) were selected for rumen and microbiota analysis. Rumen bacterial communities were characterized using 16S rRNA gene sequencing, and functional parameters were evaluated using linear mixed-effects models. Supplementation resulted in selective changes in several bacterial genera, including Blautia spp., WPS-2, Ruminococcus spp., Erysipelotrichaceae UCG-009, Sharpea spp., uncultured Bacteroidales, and Prevotellaceae UCG-003, and was associated with alterations in fermentation patterns, particularly propionate concentration, and in nitrogen metabolism, including putrescine dynamics. The transition period significantly influenced microbial diversity and total bacterial abundance across treatments. Cows in the YE group maintained higher milk yield during the transition period. Overall, dietary supplementation modulated specific rumen metabolic responses and contributed to production stability without causing large-scale changes in overall microbial structure.

Background. Bee pollen (BP) – a natural product gathered by honeybees from plants. It is recognized for its rich chemical composition. The nutritional value of BP components can be limited by the storage conditions [1]. To ensure product stability, different preservation methods (drying, freezing) are applied. Aim. To compare the amino acid, FAD and AMP composition in dried and frozen BP samples. Methods. BP was collected from an apiary in Talkoniai, Pasvalys district. One portion was dried at +28°C (day 1) and at +35°C (day 2), whereas the rest of the portions were frozen at -20°C and -80°C. Aqueous extracts were prepared. The amino acid, FAD and AMP profile was evaluated using UHPLC– ESI-MS/MS method. Statistical analysis was performed using Excel (Microsoft, USA) and SPSS softwares (IBM, USA). Results. The total free amino acid concentration was 267.97±14.60 µg/g DW in dried samples, whereas values of 256.58±11.92 µg/g at −20 °C and 243.82±15.60 µg/g at −80°C were determined in frozen samples, p>0.05. Among the 16 detected amino acids, the highest concentration was observed for proline (66.35 ± 1.78 µg/g in dried samples, 68.14±2.20 µg/g in -20°C frozen samples and 63.22±2.82 µg/g in samples stored at -80 °C) whereas tyrosine exhibited the lowest (1.02±0.19 µg/g, 1.08±0.31 µg/g, and 0.85±0.14 µg/g for dried, −20°C, and −80°C samples, respectively). FAD was detected in all samples – 4.67±0.95 µg/g in dried samples, 5.52±0.49 µg/g in samples stored at −20°C and 4.45±0.19 µg/g in samples stored at −80°C, p>0.05. AMP was detected only in frozen samples – 5.17±0.37 µg/g at −20°C and 9.85±0.41 µg/g at −80°C, p<0.05. Conclusion. Dried samples exhibited slightly higher total free amino acid content. The absence of AMP in dried samples indicates possible degradation during processing. FAD appeared to be more stable and remained in dried samples as well as in samples stored at −20°C and −80°C.

Digital technologies that continuously quantify animal behavior, physiology, and production offer significant potential for the early identification of health and welfare disorders of dairy cows. In this study, a multimodal artificial intelligence (AI) framework is proposed for real-time health monitoring of dairy cows through the integration of physiological, behavioral, production, and thermal imaging data, targeting veterinarian-confirmed udder, leg, and hoof infections. Predictions are generated at the cow-day level by aggregating multimodal measurements collected during daily milking events. The dataset comprised 88 lactating cows, including veterinarian-confirmed udder, leg, and hoof infections grouped under a single ‘sick’ label. To prevent information leakage, model evaluation was performed using a cow-level data split, ensuring that data from the same animal did not appear in both training and testing sets. The system is designed to detect early deviations from normal health trajectories prior to the appearance of overt clinical symptoms. All measurements, with the exception of the intra-ruminal bolus sensor, were obtained non-invasively within a commercial dairy farm equipped with automated milking and monitoring infrastructure. A key novelty of this work is the simultaneous integration of data from three independent sources: an automated milking system, a thermal imaging camera, and an intra-ruminal bolus sensor. A hybrid deep learning architecture is introduced that combines the core components of established models, including U-Net, O-Net, and ResNet, to exploit their complementary strengths for the analysis of dairy cow health states. The proposed multimodal approach achieved an overall accuracy of 91.62% and an AUC of 0.94 and improved classification performance by up to 3% compared with single-modality models, demonstrating enhanced robustness and sensitivity to early-stage disease.

Abstract- Contemporary technological solutions provide significant opportunities not only to optimize dairy production on farms, but also to monitor cows physiological parameters and the overall health status of the herd. One of the most advanced animal monitoring approaches is the application of thermography. This non-invasive method enables the detection of early changes in surface temperature, particularly for identifying inflammatory processes in the mammary gland and limbs. The integration of infrared thermography (IRT)technology into the milking parlor, together with the development of machine learning algorithms (MLA), could facilitate the monitoring and early detection of limb disorders and initial signs of mastitis in cows. Early identification of subclinical disease, before the onset of clinical symptoms such as lameness, would allow timely preventive interventions, thereby reducing treatment expenses and economic losses. Lameness is one of the most prevalent and economically significant health problems affecting dairy farms worldwide. It has been shown that 55% of lactations are associated with lameness-related health issues, while 15% are linked to mastitis or uterine infections. The aim of this study was to evaluate the effectiveness of IRT method in the diagnosis of bovine limb and udder diseases, with the goal of establishing a foundation for the development of MLA. For this study, a FLIR T640 thermal camera (FLIR Systems, USA) was used: Upon entering the milking parlor, thermographic images of the front and hind limbs and of the udder (with all teats visible) were captured (ambient temperature 15°C). Image analysis was performed using the Flir Tools software. The cows milk was tested by express diagnostic method before milking using the CMT (California Mastitis Test). During milking, milk samples were collected for the determination of lactose, fat, urea, protein concentrations, and somatic cell count. After milking, clinical examinations of all limbs were conducted to assess claw pathologies. Data analysis revealed that in the region of the articulationesinterphalangeaedistales, the surface temperature of clinically healthy front limbs was 18.06 +- 1.8 C whereas limbs with confirmed pathology exhibited a temperature of 27.31 +- 3.8 C - an increase of 9.25 C (p 1 < 0.001 ). In the hind limbs, the surface temperature of clinically healthy limbs was 19.58 = 2.62 C while limbs with pathology reached 29.77 +- 2.5 C - an increase of 10.19 C (p < 0.001). Analysis of teat surface temperature showed that healthy teats exhibited a temperature of 25.82 +- 2.43 C whereas showing signs of subclinical mastitis (confirmed by CMT) reached 30.42 +- 0.92 C an increase of 4.6 C (p < 0.001). Spearman's correlation analysis indicated that clinically confirmed signs of inflammation in the hind limbs were associated with increased teat temperatures left front (LF) (rs = 0.21, p<0.01), right front (RF) (rs = 0.22 p < 0.01), left rear (LR) (rs = 0.22, p < 0.01), and right rear (RR) (rs = 0.24 , p < 0.001). However, clinically confirmed signs of inflammation in the for climbs were not associated with the temperature of all four teats (p > 0.05) . Additionally, increased teat surface temperature was negatively associated with milk protein and urea content (rs = -0.18 to -0.28, p < 0.05 ), irrespective of temperature changes detected in the front and hind limbs. The results of this dataset for the development of machine learning algorithms capable of identifying associations between limb and udder diseases in dairy cows and to predict their impact on herd health.

The dairy sector continually seeks technological advancements for sustainability and efficiency. Modern technologies offer significant opportunities to optimize production, enhance animal welfare, and promptly identify health issues [1]. Intensive livestock farming, with large populations, inherently increases disease risk and complicates individual monitoring [2]. Thus, advanced animal monitoring methods are becoming essential for efficient farm operations and maintaining high animal welfare standards [3]. Early disease diagnosis, precise physiological monitoring, and detailed behavioral analysis are crucial. These approaches mitigate economic losses from illness and reduced productivity, promoting more responsible farming [4]. Growing public awareness of animal welfare and food safety further encourages farms to adopt innovations that boost production while aligning with ethical considerations. Continuous oversight of animal health and welfare helps optimize feed use, minimize medication (especially antibiotic) reliance [5], and improve dairy product quality. Extensive research was conducted at the Lithuanian University of Health Sciences Practical Testing Center, housing over 100 dairy cows and two DeLaval milking robots. This research focused on developing and validating innovative animal monitoring systems. The primary aim was to integrate and apply cutting-edge technologies: thermography, remote pulsometric monitoring, exhaled breath analysis, and visual behavior analysis, specifically for dairy cow health and welfare assessment. The overarching objective was to create a comprehensive system that, by seamlessly combining various data streams and leveraging artificial intelligence (AI), could provide real-time, actionable information to farm managers [11]. This system would facilitate informed decision-making and enable prompt responses to herd issues. Each of the four distinct experiments addressed a specific problem, testing novel data collection. The synergistic integration of these experiments led to a sophisticated, multifaceted platform for animal health and welfare assessment, readily applicable in practical farming, equipping farmers with reliable tools for efficient and responsible herd management [12]. [...].

The invention belongs to the field of agriculture, specifically, to veterinary medicine. The system is intended for the excitation of mechanical vibrations in the udders of cows, through the teats, thereby activating the blood flow, which prevents the inflammatory processes occurring in the udder - cow mastitis. The system comprises at least one teat cup on which a vibration stimulating actuator is rigidly mounted on the body, by means of a holder ring, which is connected to a controller, controlled through a vacuum delivery to teat cup transducer. The system preferably comprises four individual vibrostimulation elements, each mounted on four individual teat cups. All vibrostimulation actuators are connected to a single controller, controlled via the vacuum delivery to teat cup transducer.

Cranial cruciate ligament rupture (CrCLR) is a common stifle joint pathology among dogs, leading to osteoarthritis and painfulness. Non-surgical treatment options often represent the usage of non-steroidal antiinflammatory drugs for 14 days (NSAIDs), but autologous conditioned plasma (ACP) shows promising results in managing various orthopaedic conditions, decreasing inflammation, and improving the clinical outcome in dogs. This study aimed to determine the differences in MMP-3 serum levels and the clinical outcomes between differently treated cranial cruciate rupture cases. For this purpose, we used two different treatment methods for managing canine cranial cruciate ligament rupture (minimally invasive ACP injection or oral NSAIDs), and evaluated the clinical outcomes, indicating the quality of life, and the MMP-3 serum levels over a period of 28 days. The findings of this investigation indicate that ACP has better efficacy than two weeks of NSAIDs in inflammation reduction, clinical outcome improvement, and the allowance of a longer duration of activity after 28 days.

Brown adipose tissue (BAT) has received renewed interest as a potential treatment target for obesity and comorbidities due to its thermogenic activity capacity and contribution to energy expenditure. Murine models are among the commonest preclinical models for studying human disease, including BAT studies. C57BL/6 and BALB/c are the two most commonly used mouse strains, however, different strains manifest significant behavioural differences, levels of sociability, emotionality, exercise response, respond differently to severe spinal cord injury, show differences in susceptibility to dietinduced obesity and insulin resistance, etc. To gain a broader understanding of the peculiarities, this study aimed to investigate the effect of age, strain and gender on interscapular BAT temperature changes and to analyse some blood biochemical and behavioural parameters in the aforementioned strains. The age of the mice (5–8 weeks) did not influence the interscapular BAT area temperature. The mean temperatures of the maximum (Tmax) and the average temperatures (Tisoth) in the BALB/c mice were by 0.46 °C (P<0.001) and by 0.37 °C (P<0.001) higher than in the C57BL/6 mice. In addition, the gender of the mice also modulated the BAT area temperature, causing an increment of 0.29 °C (P<0.001) and 0.21 °C (P<0.05) in Tmax and Tisoth temperatures of males, respectively. The difference in mean glucose, cholesterol and triglyceride levels between the BALB/c and C57BL/6 strains was not substantial. Behavioural analyses disclosed a statistically insignificant distribution of grooming activities in male BALB/c and C57BL/6 mice and significant (P<0.001) differences in female mice. Collectively, our findings revealed that infrared thermography can be successfully used to measure interscapular BAT temperature in mice in vivo, and that mouse strain and gender can alter BAT measurements.

The degradation of zearalenone (ZEN) in the rumen of dairy cows is influenced by rumen pH, which is a key factor affecting this process. The aim of this study was to investigate the variation of ZEN in interaction with other mycotoxins at different ruminal pH environments (physiological (pH 6.5) and acidic (pH 5.5)) using an in vitro rumen model. Rumen fluid was collected from the caudoventral part of the rumen of cows using a pharyngeal–esophageal probe. To determine the changes in different mycotoxins (ZEN; AFLB1; DON; T-2) in the rumen of cows, a model rumen system was used, and mycotoxins concentrations were detected by HPLC. The study found that at pH 6.5, ZEN alone and in combination with other mycotoxins (DON; T-2; AFLB1) significantly (p < 0.05) reduced ZEN levels compared to the rumen environment at pH 5.5. It was observed that α-zearalenol (α-ZEL) and β-zearalenol (β-ZEL) concentrations were generally higher at a rumen pH of 6.5 compared to pH 5.5, averaging 47.09 µg/L and 35.23 µg/L, respectively. Additionally, the frequency of detection for both α-ZEL and β-ZEL was greater at pH 6.5 than at pH 5.5. A comparison of α-ZEL concentrations in rumen samples at pH 5.5 showed a 20% increase from the 6th to the 9th hour of the test, while β-ZEL levels remained unchanged over the same period.

Metritis affects 5–20% of cows after parturition, negatively impacting animal welfare and the profitability of dairy farms, increasing culling rates and costs, and decreasing productivity and reproduction rates. This study compared the results of a comprehensive biochemical panel consisting of 25 salivary and 31 serum analytes between healthy cows (n = 16) and cows with metritis (n = 12). Descriptive parameters such as depression, rectal temperature, body condition score (BCS), heart rate, respiratory rate, mucous color, ruminal motility, vaginal discharge, milk production, and complete hematology analyses were also assessed for comparative purposes. The biochemistry analytes comprised five analytes related to stress, five to inflammation, five to oxidative status, and nineteen to general metabolism. The two-way ANOVA analysis revealed that, in saliva, eight biomarkers (lipase, adenosine deaminase (ADA), haptoglobin (Hp), total proteins, g-glutamyl transferase (gGT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and creatine kinase (CK)) were significant higher in cows with metritis. In serum, eight biomarkers (ADA, Hp, serum amyloid A (SAA), fibrinogen, ferritin, AOPPs/albumin ratio, non-esterified fatty acids (NEFAs), and bilirubin) were significantly higher in cows with metritis, whereas six (total esterase (TEA), albumin, urea, lactate, phosphorus, and calcium) were lower. Of the total number of 23 biomarkers that were measured in both saliva and serum, significant positive correlations between the two biofluids were found for six of them (Hp, FRAP, CUPRAC, AOPPs, urea, and phosphorus). Urea showed an R = 0.7, and the correlations of the other analytes were weak (R < 0.4). In conclusion, cows with metritis exhibited differences in biomarkers of stress, inflammation, cellular immune system, and general metabolism in both salivary and serum biochemistry profiles. These changes were of different magnitudes in the two biofluids. In addition, with the exception of ADA and Hp, the analytes that showed changes in the saliva and serum profiles of cows affected by metritis were different. Overall, this report opens a new window for the use of saliva as potential source of biomarkers in cows with metritis.