Introduction

Modern science continues to search for approaches to improve health (Karpov et al., 2021; Zavalishina et al., 2022) and increase the viability of the mammalian organism (Zavalishina, 2018b; Kočović et al., 2021). For this purpose, direct observations are carried out (Zavalishina, 2018c; Zavalishina et al., 2021), and various schemes of experimental work are set up (Vorobyeva et al., 2018; Kulikov et al., 2020). At the same time, despite all the efforts made by science and practice, the Fasciola hepatica parasite is still common and very common in cattle. The situation is complicated by the fact that, as a rule, fascioliasis in cattle is mostly asymptomatic (Zavalishina, 2018d; Yıldız et al., 2021), but it causes serious physiological changes that lead to significant economic losses (Torgerson & Claxton, 1999). Despite this fact, few studies related to the impact of the disease on productivity levels have been performed on this parasitic disease (Vercruysse & Claerebout, 2001; Drljača et al., 2020). At the same time, in some cases, there were questions about the scheme or design of the study (Burden et al., 1978; Dargie, 1987).

The presence of F. hepatica in the body, as a rule, is determined using scatological methods. At the same time, in recent years, specific tests have been increasingly used to determine its presence, which is gradually replacing scatological research methods. Newer tests are associated with the detection of specific fasciola antigens in blood and feces (Leclipteux et al., 1998; Mezo et al., 2004) or the detection of the presence of specific antibodies to fasciola in the blood (Farrell et al., 1981; Salimi-Bejestani et al., 2005b; Korobov & Glamazdin, 2010) and in milk (Boulard et al., 1985; Reichel et al., 2005; Salimi-Bejestani et al., 2005a). The available reagent kits for antibodies to fasciolae have already been sufficiently developed and allow detecting the presence of the parasite with an unmistakable accuracy, which makes it possible to judge the degree of its spread. Given the impossibility of the complete eradication of F. hepatica, it is important to direct diagnostic control to reduce its frequency of occurrence and prevalence, in which the invasion would have a minimal impact on the overall lactation capacity of the dairy herd of cows (Vercruysse & Claerebout, 2001; Mai et al., 2022).

Previously, the prevalence of invasion in the amount of 30 different flukes with a prevalence in the herd of 25% of the total population of the herd was supposed to be considered as threshold values ​​for optimum productivity (Vercruysse & Claerebout, 2001; Roest Henk et al., 2021). However, these figures either cannot be determined prior to slaughter or are based on questionable information. Recording the amount of highly specific antibodies to the parasite present in marketed milk is now considered a valid method of tracking the overall level of parasite infestation in a dairy herd. Also, this method is recognized as reliable for the diagnosis of parasites in the herd, where the level of infestation of cows causes a decrease in productivity (Sanchez & Dohoo, 2002; Charlier et al., 2005a). Scientists have elucidated the relationship between the content of antibodies specific to fasciola in milk and the number of antibodies in the blood of lactating cows (Salimi-Bejestani et al., 2005a; Sloan et al., 2021).

The purpose of the study: is to find out the relationship between the number of antibodies specific to fasciola in milk and indicators reflecting the productivity of dairy cows in the Moscow region of Russia to assess possible economic losses.

Materials and Methods

The study was conducted in dairy farms of the Moscow region of Russia with a total number of livestock of more than 2000 dairy cows. Milk samples were taken from the milk collection containers. All taken milk samples were delivered to the laboratory of the Department of Veterinary Medicine of the Moscow State University of Food Production in Moscow within 6 hours after milking. After delivery to the laboratory, all milk samples were subjected to centrifugation (16000 g for 5 minutes). Fat was removed from them, the supernatant was obtained and frozen at -20°C until the scheduled analyzes were performed. All selected milk samples were tested using F. hepatica ELISA and O. ostertagi ELISA.

Cows that gave milk, samples of which were taken for analysis, were kept in farms in the Moscow region of Russia. They were purebred for the Black-and-White breed or had different bloodlines for the Black-and-White and Holstein breeds. In general, the cows that gave milk for sampling had 2-4 lactations. The study was completed in March 2021.

After defrosting all milk samples, they were examined using 2 indirect ELISAs to detect the content of specific antibodies capable of reacting with Fasciola hepatica and Ostertagia ostertagi antigens.

The amount of antibodies capable of reacting with F. hepatica was recorded by the traditional method by Salimi Bejestani et al. (2005b). For this, 96-well microplates with a flat bottom were used. They were loaded per well with 0.5 mg/ml excretory and secretory (ES) antigen in 0.05 M carbonate-bicarbonate buffer (pH 9.6). An overnight incubation followed by washing the well six times with phosphate-buffered saline containing 0.05% tween 80 (PBST). Then blocking was done with 100 ml of 2% skimmed milk powder (Marvel, Premier Beverages, Stafford, UK) in PBST (SMP/PBST) and the incubation process was carried out for 1 hour at 37.0°C. The plates used in the work were traditionally washed and undiluted samples of the studied milk (100 ml) were added to their wells. Evaluated serum samples, considered as negative and positive controls, were diluted at 1:800 in SMP/PBST and added (100 ml/well) to six wells of each plate. Tablets are traditionally incubated and washed.

Rabbit anti-bovine IgG conjugated with horseradish peroxidase (Jackson Immunoresearch Laboratories) was used. It was diluted 1/6000 in SMP/PBST and added to wells per 100 ml. The substrate solution consisted of 0.1% OPD (orthophenylenediamine) in citric acid/phosphate buffer (pH 5.0) with 1/2000 H₂O₂ and was applied to wells at a rate of 100 ml. The plates thus prepared were incubated at room temperature in the absence of light for 10 min. Blocking of the reaction was carried out by introducing 50 ml of 2.5 M HCl and then the optical density was evaluated at a wavelength of 492 nm. The amount of antibodies was expressed as the ratio of the detected optical density (ODRf) according to the formula ODRf = (OD NC)/(PC NC) where OD is the value of the optical density of the assessed image, and NC and PC OD are negative and positive levels. The sensitivity of this enzyme immunoassay makes it possible to detect infection in milk in a herd of more than 25% of cows with an accuracy of 95% (95% CI 89–100%) and 80% (95% CI 66–94%), respectively.

It has been noted that the ES antigen is unable to bind to O. ostertagi (Salimi-Bejestani et al., 2005a; Salimi-Bejestani et al., 2005b). Anti-O. ostertagi antibodies were quantified using a known ELISA method (Charlier et al., 2005b). The ELISA used crude antigen from mature O. ostertagi. The result of this test is reported as an optical density ratio (ODRo). The expected 95% scatter difference between two ODRo values ​​of a particular sample using separate plates and on different days reaches 0.16 (Charlier et al., 2005b) The adult worm crude antigen interacts very well with other infections of the gastrointestinal tract, including Cooperia spp. (Keus et al., 1981) and with other worms, including F. hepatica (Eysker & Ploeger, 2000).

Differences in ODR values ​​were noted between dairy farms for which production situation data were and were not obtained by applying a two-tailed Mann-Whitney U-test (a = 0.05) for ODRf and a two-tailed t-test (a = 0.05) for ODRo. The level of Spearman's rank correlation between ODRf and ODRo was determined.

The initial relationship of ODRf with the number of economically significant indicators (the amount of milk produced, the percentage of protein in milk, the fat content of milk, and the time between the onset of calving) was estimated using linear regression models. The dairy herd was considered as a whole, and the significance of the parameters was assessed using two-tailed F-tests (a=0.05). The average indicators of the amount of milk received, the protein content in milk, and the level of the fat content of milk over the past year before taking milk samples for the study were taken into account and their relationship with ODRf was traced. Several covariates were introduced into the model as independent variables because they were economically important factors: mean number of lactating cows, mean number of lactations, mean duration of lactation, mean logarithm [(number of somatic cells/1000)/mL], and ratio miscarriages to the number of calving for the whole year. Since a negative correlation is known between the value of the average annual milk yield and % of its fat content (apparently due to the effect of dilution of milk), milk yield was considered as a covariant in the course of studying the relationship of ODRf with % of milk fat. The effect of simultaneous parasitism in the body of the cow F. hepatica and gastrointestinal nematodes on the amount of milk received was considered an independent factor in the model that evaluates the relationship between ODRf and the volume of milk received and reveals the relationship between ODRf and ODRo. The relationship between the number of ODRf and the time between calving was determined using a model that included covariates in the form of the distribution of calving, the bulk of cows in the herd, the average annual number of productive animals in the herd, and the number of lactations in cows. The adequacy of the models was determined by evaluating the quadratic terms for any independent variable. The normality of the individual considered models was determined using standard plots of residuals and plots of probability compared to the predicted values.

Results and Discussion

The results of the assessment of antibody levels in relation to ODRf and ODRo are shown in Table 1. The values ​​of the ODRf content were shifted to the right side, while the distribution of ODRo values ​​showed a normal character (Figure 1). The numbers of ODRf and ODRo were lower in holdings reporting performance data than in herds for which such information was available. The differences found were not high, but statistically significant (ODRf: p<0.001; ODRo: p=0.003). A positive correlation was found between the level of ODRf and the amount of ODRo (R=0.35; p<0.001).

Table 1. General information on the levels of antibodies to F. hepatica (ODRf) and O. ostertagi (ODRo) in milk samples taken from tanks, in October 2021 in the Moscow region of Russia

The overall performance statistics collected in the year prior to sampling are summarized in Table 2.

Table 2. Performance characteristics at the herd level from April 2020 to March 2021

A significant negative relationship was noted between the value of ODRf and the volume of average annual milk produced (Table 3). The increase in ODRf within the interquartile interval was associated with a decrease in the volume of milk supplied during the year by 0.8 kg/(cow day) (p=0.002), which is associated with a decrease in the volume of milk supplied per year by 3%.

It was not possible to find a statistically significant relationship between the level of ODRf and the percentage of protein in milk [slope = 0.013; 95% CI = (0.035; 0.007); p = 0.24]. An increase in ODRf within the interquartile range was associated with a 0.07% drop in milk fat [slope = 0.095; 95% CI = (0.145; 0.046); p<0.001], and when the range between calvings increases by 4.6 days [slope = 7.6; 95% CI = (0.78; 14.3); p=0.04].

When looking for a relationship between ODRf and ODRo on the one hand, and with milk yield on the other hand, the negative relationship for ODRo [slope index = 4.47; 95% CI = (6.41; 2.61); p<0.001]. An unexpressed negative relationship was found for the ODRf level [slope index = 0.64; 95% CI = (1.32; 0.09), p=0.07]. At the same time, there was no significant relationship between the levels of ODRf and ODRo in relation to their influence on the level of milk yield (p=0.69).

Table 3. Linear regression model data to find the relationship between ODRf in milk and average annual milk yield