**3.1 Uremic toxins**

Several studies in the past 30 years have shown that uremic patients are at great risk for disordered sleep. A study by Millman et al. (1985) noticed a slight but significant relationship between sleep apnea syndrome and azotemia. In a recent prospective study of incident HD patients followed-up for 1 year, higher dialysis efficiency was associated with fewer sleep disturbances (Unruh et al., 2006). These latter data are further supported by the results of Hanly et al. (2001), who studied patients on conventional hemodialysis (4 h three times a week) who then switched to nocturnal (intensive) dialysis (8 h for 6 or 7 days/week). A spectacular correction of sleep apnea was recorded in this small-scale study (Hanly et al., 2001). Finally, the impact of uremic toxins is highlighted by the fact that sleep disorders are more frequent and more severe in dialyzed patients compared with subjects with pre-dialysis CKD (Merlino et al., 2006).

### **3.2 Interdialytic weight gain and hypertension**

Excessive interdialytic weight gain has been associated with poor compliance and high blood pressure (Rahman et al., 2000). While the mechanism of the association between interdialytic weight gain and sleep problems is unclear, we speculate that large interdialytic weight gains result in expanded intravascular volume, which has been associated with upper airway obstruction (Chiu et al., 2006).

The length of sleep during the night before a hemodialysis session after a long interdialytic interval (3 days, the "weekend interval") is significantly shorter compared to the usual sleep length following a short interdialytic interval. This led to some speculation regarding the possible effect of interdialytic weight gain (IWG) on sleep (Bertini et al., 1999). Furthermore, patients with uncontrolled pre-dialysis systolic pressure (usually a sign of hypervolemia in these patients) experience clinically overt insomnia more frequently (De Santo et al., 2001); in patients with chronic diseases, systolic hypertension appears to be a cause of sleep disturbances (Katz & McHorney 1998; Sabbatini et al., 2002; Thase, 2005).

### **3.3 Dialysis shift**

412 Progress in Hemodialysis – From Emergent Biotechnology to Clinical Practice

Most studies assessing the effectiveness of different treatment modalities in insomniacs address short-term treatment of insomnia (Montgomery &Dennis., 2004; Morgan et al., 2003; Smith et al., 2002). Extra care and caution has to be exercised when treating insomnia in patients with renal impairment. Most hypnotics should be administered in appropriately reduced doses and interactions with the numerous medications used in the different HD populations should be considered carefully when prescribing a hypnotic to patients with renal failure (Novak et al., 2006). Surprisingly there is an almost complete lack of pharmacologic studies in renal patients suffering from insomnia. In a small randomized study using the PSQI Sabbatini et al. (2003). suggested that zaleplon improved sleep efficacy

Nonpharmacologic interventions include sleep hygiene measures relaxation therapy and biofeedback stimulus control therapy sleep restriction and cognitive behavioral therapy (Montgomery &Dennis., 2004; Morgan et al., 2003). These interventions have been shown to be beneficial in the long-term management of patients with chronic hypnotic use. Cognitive behavioral therapy for insomnia in the routine general practice setting improved sleep quality reduced hypnotic drug use and improved health-related quality of life at a favorable

A multitude of causes including anemia, blood urea levels, plasma creatinine levels, parathyroid hormone (PTH) concentrations, increased blood pressure, quality of life, and illness intrusiveness may contribute to sleep disturbances in patients on maintenance hemodialysis (Hanly et al., 2003; Iliescu et al., 2004; Sabbatini et al.,2003). Furthermore, there is a positive correlation between sleep disturbances and increased morbidity and mortality related to cardiovascular disease and infectious complications, the 2 major causes of death in hemodialysis patients (De Santo et al., 2005). Sleep disorders in hemodialysis patients is a multifactorial complaint, stemming from Uremic toxins and dialysis procedure, other

Several studies in the past 30 years have shown that uremic patients are at great risk for disordered sleep. A study by Millman et al. (1985) noticed a slight but significant relationship between sleep apnea syndrome and azotemia. In a recent prospective study of incident HD patients followed-up for 1 year, higher dialysis efficiency was associated with fewer sleep disturbances (Unruh et al., 2006). These latter data are further supported by the results of Hanly et al. (2001), who studied patients on conventional hemodialysis (4 h three times a week) who then switched to nocturnal (intensive) dialysis (8 h for 6 or 7 days/week). A spectacular correction of sleep apnea was recorded in this small-scale study (Hanly et al., 2001). Finally, the impact of uremic toxins is highlighted by the fact that sleep disorders are more frequent and more severe in dialyzed patients compared with

Excessive interdialytic weight gain has been associated with poor compliance and high blood pressure (Rahman et al., 2000). While the mechanism of the association between

**3. Related factors with sleep problems in hemodialysis patients**

medical problems, psychiatric or psychosocial background.

subjects with pre-dialysis CKD (Merlino et al., 2006).

**3.2 Interdialytic weight gain and hypertension**

in maintenance hemodialysis patients.

cost in chronic insomniacs.

**3.1 Uremic toxins**

The dialysis shift has several effects on patients with ESRD. Morning-shift HD patients experience more insomnia (Sabbatini et al., 2002), but also have longer survival times (Bliwise., 2001) than patients on other dialysis shifts. In according to another study (Merlino et al., 2006), for patients undergoing HD in the morning shift, the risk of subclinical insomnia is up to 18 times higher than for those who have their dialysis session in the afternoon. However, morning-shift HD patients have higher intradialytic sleepiness, which is associated with more decreased body temperature during HD, than patients on other dialysis shifts (Parker et al., 2003).

### **3.4 Dialysis vintage**

The longer the dialysis vintage, the more significant is the prevalence of sleep quality disorders (Veiga et al., 1997). In a study by De Santo et al., (2005), patients with subclinical or clinical sleep disorders had double the dialysis vintage of those without sleep complaints; in patients with a medium dialytic age of 75 months, the prevalence of sleep disorders is close to 80%. These data are not surprising, taking into account the accumulation of comorbidites, including peripheral neuropathy and CVD, whilst on dialysis on long-term. In a polysomnographic study by Tatomir et al.,(2007) in patients hemodialyzed for more than 10 years, all patients has disturbed sleep with frequent awakening and reduced sleep efficiency. One-half of these patients had sleep-related breathing disorders, i.e. sleep apnea syndrome.

### **3.5 Restless legs syndrome**

Restless legs syndrome (RLS) usually becomes apparent during resting and may significantly interfere with sleep. An association between RLS and insomnia in patients on maintenance hemodialysis has been suggested already by a few papers (Musci et al., 2005; Sabbatini et al., 2002; Walker et al., 1995). Moreover, RLS is associated with a low quality of sleep and lower quality of life.

### **3.6 Other medical factors**

Other medical factors include the pain and discomfort caused by illnesses such as arthritis, cardiovascular disease, chronic obstructive pulmonary disease (COPD), cerebrovascular disease, neurological disorders, asthma, headaches, and more. The prognosis of patients with chronic uremia is influenced by the presence of comorbidities, mainly by

Sleep in Patients with ESRD Undergoing Hemodialysis 415

ESRD patients compared to the general population. Moreover, according to recent data from the CHOICE incident dialysis population, use of benziodiazepines is associated with altered sleep quality during the first year of dialysis. Although this study was unable to distinguish between cause and effect, more effective dialysis and cognitive behavioral therapy have been suggested in patients with sleep disorders in need of sleep-inducing medication (Unruh et al., 2006). Moreover, many antidepressants actually cause paradoxical restlessness, therefore systematic administration of these drugs should be subjected to close

Alcohol is widely used as a sleep aid. Although it does shorten sleep latency, it also causes sleep fragmentation, decreased REM, REM rebound and early morning awakenings. The use of alcohol combined with hypnotics may exacerbate sleep difficulties even more (Rosenthal, 1998). Alcohol and (particularly) tobacco abuse is highly associated with increased prevalence of sleep disorders in ESRD (Merlino et al., 2006). Caffeine and other stimulants such as nicotine have been shown to increase sleep latency and sleep fragmentation, and to decrease total sleep time (Rosenthal, 1998). Current smoking is also related with decreases in sleep quality during the first year of dialysis therapy in incident patients (Unruh et al., 2006). Caffeine intake appears to have no significant impact on insomnia in ESRD (Sabbatini

Sleep quality is an important clinical construct for two major reasons. First, complaints about sleep quality are common; epidemiological surveys indicate that 15-35 % of the adult population complain of frequent sleep quality disturbance, such as difficulty falling asleep or difficulty maintaining sleep. Second, poor sleep quality can be an important symptom of

Sleep quality" is sometimes used to refer to a collection of sleep measures including total sleep time (TST), sleep onset latency (SOL), degree of fragmentation, total wake time, sleep efficiency, and sometimes sleep disruptive events such as spontaneous arousals or apnea. The widely employed Pittsburgh Sleep Quality Index (PSQI), for example, provides a measure of global sleep quality based on a respondent's retrospective appraisal (past month) of an array of sleep measures, including sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction (Krystal & Edinger, 2008). Sleep quality is also sometimes inferred from a collection of objective indices taken from polysomnography (PSG). PSG is the most valid and accurate way to assess sleep. Measures derived from PSG include: (a) total sleep time, (b) sleep efficiency (ratio of time spent asleep/time in bed), (c) sleep latency (time to fall asleep after lights out), (d) amount of wake time during sleep periods (waking after sleep onset, WASO), (e)

Among these objective indices are measures such as sleep onset latency, total sleep time, wake time after sleep onset, sleep efficiency, and number of awakenings that correspond to like measures taken from various available self-report instruments (e.g., sleep diaries, PSQI, etc.) (Buysse et al., 2006). However, PSG also provides a number of measures that reflect the architecture of sleep such as the percentage or temporal amounts of stage 1 sleep, stage 2 sleep, slow wave sleep or rapid eye movement (REM) sleep. Despite having no self-report analogues, these latter measures also have been employed by some as indices of sleep

clinical follow-up, which should be easy to accomplish in hemodialysis patients.

**4. Sleep quality and evaluation of sleep in hemodialysis patients**

number of awakenings, and (f) amount of each sleep stage ( Landis al., 2002).

many sleep and medical disorders (Buysse et al., 1989).

quality (Krystal & Edinger, 2008).

et al., 2002).

cardiovascular disease (CVD), which causes roughly 50% of deaths (Covic et al., 2006). A study by Mucsi et al. (2004), demonstrated that comorbidities are independent predictors of sleep disturbances in patients on maintenance dialysis. According to another study, the average Charlson Comorbidity Index (CCI) in patients without sleep disorders was 4.10, while scores in patients with subclinical and clinically overt sleep disorders were 6.10 and 6.81, respectively (De Santo et al.,2005). This highly significant association in the Italian HD population was maintained regardless of age. To conclude so far, any in-depth research on the quality of sleep in renal patients must consider the magnitude of comorbidities.


**4.2 Resistant varieties**

The use of cultivars that are resistant to disease has always been one of the most effective and economical ways of managing diseases in many crops, including mango. However, in mango, several studies have shown that most cultivars that are commercially available to farmers are susceptible to the disease. Nevertheless, some experiments in controlled environments have reported that there are some few genotypes with some considerable levels of resistance to the mango anthracnose disease (**Table 1**). These traits can be harnessed in future breeding intervention to development some potential cultivars with higher resistance to the disease [26].


*3.1. Structure and Morphological Characterization*

MOFs can be readily converted to metal-oxide composites, which take advantage of their original morphology and porosity. The as-prepared HKUST-1, HKUST-1/Fe3O4, and HKUST-1/Fe3O4/CMF composite samples were then calcinated to obtain the nanoporous metal-oxide particles, generating nanoporous CuO, CuO/Fe3O4, and carbon-doped CuO/Fe3O4 composite catalysts, respectively. Pyrolysis/thermolysis of HKUST-1/Fe3O4/CMF composites led to the formation of porous carbon-doped CuO/Fe3O4 composites.

The size and the morphology of the synthesized materials were investigated using a transmission electron microscope (TEM) and scanning electron microscope (SEM). Figure 1 shows the SEM and TEM images of HKUST-1/Fe3O4/CMF composites and carbon-doped CuO/Fe3O4 composites after calcination in air. HKUST-1 crystals and Fe3O4 nanoparticles were uniformly loaded onto the surface of CMF (Figure 1). The nanocellulose MOF-derived porous carbon-doped CuO/Fe3O4 composite was then obtained through a calcination process under air at 500 ◦C for 5 h. The Brunauer–Emmett–Teller surface area (SBET) of the carbon-doped CuO/Fe3O4 sample was 38.7 m2/g, which was much higher than that of the CuO/Fe3O4 sample (0.042 m2/g) obtained from the calcination of HKUST-1/Fe3O4. The resultant carbon-doped CuO/Fe3O4 composite inherited largely the original porous morphology of HKUST-1. Based on the mapping images, the conclusion was that both Cu and Fe elements dispersed well in the carbon-doped CuO/Fe3O4 composite catalyst.

XPS analysis was employed to investigate the elemental composition on the surface of different composites. For the XPS spectrum of carbon-doped CuO/Fe3O4 composite catalyst (Figure 2a), the main peaks were C*1s*, O*1s*, Fe*2p*, and Cu*2p*, centered at around 285 eV, 530 eV, 720 eV, and 930 eV, respectively. The C*1s* XPS pattern of the sample is shown in Figure 2b. The spectrum contains two peaks at 285 eV and 288.5 eV, which may be attributed to carbon (*sp2* hybridization) in the sample, and the Cu–O–C bonds or Fe–O–C bonds, respectively. Figure 2c shows the Cu*2p* core-level XPS spectrum of the composite catalyst. The Cu*2p1* and Cu*2p3* binding energies for the composite catalyst were 952.8 and 932.7 eV, respectively, indicating the presence of CuO in the composite catalyst. Similar results were reported in the literature [38,39]. The Fe*2p3* and Fe*2p1* binding energies (Figure 2d) for the composite catalyst were 710.7 and 725.4 eV, respectively, which agrees with published results [35,40], confirming the presence of Fe3O4 in the composite catalyst.

**Figure 1.** SEM images of (**a**) HKUST-1/Fe3O4/cellulose microfibril (CMF) composites and (**b**) carbon-doped CuO/Fe3O4 composite catalysts after calcination; TEM images of (**c**) carbon-doped CuO/Fe3O4 composite catalysts; (**d**–**f**) energy-dispersive X-ray spectroscopy (EDX) mapping of carbon-doped CuO/Fe3O4 composite catalysts.

**Figure 2.** (**a**) High-resolution X-ray photoelectron spectroscopy (XPS) survey spectra of the carbon-doped CuO/Fe3O4 composite catalysts. (**b**) High-resolution XPS scans over C*1s* peaks of the carbon-doped CuO/Fe3O4 composite catalysts. (**c**) High-resolution XPS scans over Cu*2p* peaks of carbon-doped CuO/Fe3O4 composite catalysts. (**d**) High-resolution XPS scans over Fe*2p* peaks of carbon-doped CuO/Fe3O4 composite catalysts.

The crystalline nature and the composition of the as-synthesized products were characterized using PXRD. The crystalline phases of CuO, such as (110), (11-1), (111), (20-2), (020), (202), (11-3), (31-1), and (220) are shown in Figure 3a, which are consistent with those reported in the literature [41,42]. These results support the conclusion that HKUST-1 was transformed into CuO via calcination under the present conditions. In the XRD pattern of carbon-doped CuO/Fe3O4 composite, the diffraction peaks and relative intensities were indexed to Fe3O4 (JCPD NO. 19-0629) and CuO (JCPD NO. 05-0661). This indicates that CuO and Fe3O4 were both obtained via calcining HKUST-1/Fe3O4/CMF composites under air. No other C-related impurity peaks were detected; a similar result was reported in the previous research [43].

**Figure 3.** (**a**) Powder X-ray diffraction (PXRD) patterns of carbon-doped Fe3O4/CuO composite catalyst. (**b**) Magnetization curve at 300 K of carbon-doped Fe3O4/CuO composite catalyst.

The magnetic behavior of the carbon-doped Fe3O4/CuO composite catalyst sample was evaluated at 300 K. Its magnetization saturation value (Ms) was 15.1 emu·g−1, suggesting a good magnetic property (Figure 3b). Thus, the magnetic carbon-doped Fe3O4/CuO composite catalyst could be readily separated from the reaction system using a magnet for the recycling process.


3.2.2. Machine Learning Methods

Multivariate modeling based on four common algorithms in agricultural and environmental modeling, including RFR, SVM, ANN and PLSR, was used to model the RCF. In the first scenario, all reflective band-based spectral indices were used as independent variables. In the second scenario, in addition to spectral indices based on reflective bands, VV and VH band information was also used in the modeling process. Each RFR, SVM, ANN and PLSR algorithm was calibrated based on training data (96 samples for corn, 38 samples for wheat and 84 samples for soybean). Then the efficiency of each of these algorithms in estimating the RCF fraction using test data (53 samples for corn, 20 samples for wheat and 43 samples for soybean) was evaluated.

PLSR breaks down both dependent and independent variables into a number of major components. PLSR is a two-line calibration algorithm that converts a large number of correlated linear variables into several non-correlated variables based on data compression. Hence, this algorithm can solve the challenges of high correlation between variables and overfitting in the modeling process [38].

In recent years, artificial neural networks have been widely used to estimate various environmental variables based on satellite data [39,40]. In this study, a back-propagation ANN was used to model the residue. This algorithm consists of input, hidden and output layers. Sigmoid and linear functions were used for activation in hidden and output nodes, respectively. To calibrate synaptic coefficients, the Levenberg–Marquardt minimization algorithm was used [41]. The number of nodes in the hidden layers varied between 4 to 8. To optimize the structural parameters of the ANN algorithm for the network, we changed the momentum coefficient and learning rate from 0.1 to 1.0 with a step of 0.05. The number of nodes in the hidden layer varied from 3 to 7. Mean squared error was used as a measure of the performance threshold and the determination of a network with optimal structure in receipt fraction modeling. The optimal network was selected in terms of mean absolute error between validation and predictions data.

The SVR model is a widely used algorithm for solving nonlinear problems [42]. In the SVR method, n-dimensional input variables are transferred to the new feature space with higher dimensions using the core functions and, as a result, optimal separator super planes are developed [43]. In this study, different Gaussian, linear, nonlinear quadratic, cubic, etc., kernels were evaluated, and finally the Gaussian kernel was selected and used as a function in the receipt fraction estimation process. The optimal values of box constraint, kernel scale and epsilon were set to 909, 857 and 0.04, respectively.

RFR is an ensemble-learning algorithm that combines a large set of decision trees to improve the accuracy of estimating a variable [44]. RFR has several advantages in modeling environmental variables, including (1) low sensitivity to noise and over-fitting, and (2) the use of a large number of quantitative and qualitative variables in the modeling process [45,46]. To implement this algorithm, two parameters, the number of trees and the number of attributes, must be set. The number of trees varied from 30 to 300 with step size 30 and the number of trees 150 was selected as the optimal value.

The optimal model for each of these four algorithms was selected to estimate the RCF and the mean absolute error between the validation data and the predictions.


*Article* **Occurrence of Zearalenone and Its Metabolites in the Blood of High-Yielding Dairy Cows at Selected Collection Sites in Various Disease States**

**Wojciech Bara ´nski 1 , Magdalena Gaj˛ecka 2,\* , Łukasz Zielonka 2 , Magdalena Mróz 2 , Ewa Onyszek 3 , Katarzyna E. Przybyłowicz 4 , Arkadiusz Nowicki 1 , Andrzej Babuchowski 3 and Maciej T. Gaj˛ecki 2**

**Abstract:** Zearalenone (ZEN) and its metabolites, alpha-zearalenol (α-ZEL) and beta-zearalenol (*β*-ZEL), are ubiquitous in plant materials used as feed components in dairy cattle diets. The aim of this study was to confirm the occurrence of ZEN and its selected metabolites in blood samples collected from different sites in the hepatic portal system (posthepatic–external jugular vein EJV; prehepatic–abdominal subcutaneous vein ASV and median caudal vein MCV) of dairy cows diagnosed with mastitis, ovarian cysts and pyometra. The presence of mycotoxins in the blood plasma was determined with the use of combined separation methods involving immunoaffinity columns, a liquid chromatography system and a mass spectrometry system. The parent compound was detected in all samples collected from diseased cows, whereas α-ZEL and β-ZEL were not identified in any samples, or their concentrations were below the limit of detection (LOD). Zearalenone levels were highest in cows with pyometra, where the percentage share of average ZEN concentrations reached 44%. Blood sampling sites were arranged in the following ascending order based on ZEN concentrations: EJV (10.53 pg/mL, 44.07% of the samples collected from this site), ASV (14.20 pg/mL, 49.59% of the samples) and MCV (26.67 pg/mL, 67.35% of the samples). The results of the study indicate that blood samples for toxicological analyses should be collected from the MCV (prehepatic vessel) of clinically healthy cows and/or cows with subclinical ZEN mycotoxicosis. This sampling site increases the probability of correct diagnosis of subclinical ZEN mycotoxicosis.

**Keywords:** zearalenone; mastitis; ovarian cysts; pyometra; hepatic portal system; dairy cows

**Key Contribution:** Zearalenone metabolites were not detected in the blood plasma of cows with healthy ruminal microbiota. When the rumen contents containing ZEN reach the intestines, the mycotoxin is transported to the liver by the prehepatic circulation.

### **1. Introduction**

Zearalenone (ZEN) is a resorcinic acid lactone produced by fungi of the genus *Fusarium*, which, due to its structural similarity to 17β-estradiol and affinity to estrogen receptors [1], is classified as mycoestrogens. In mammals, ZEN binds to estrogen receptors that display tropism for female reproductive cells. Zearalenone poses a health risk for dairy

**Citation:** Bara ´nski, W.; Gaj˛ecka, M.; Zielonka, Ł.; Mróz, M.; Onyszek, E.; Przybyłowicz, K.E.; Nowicki, A.; Babuchowski, A.; Gaj˛ecki, M.T. Occurrence of Zearalenone and Its Metabolites in the Blood of High-Yielding Dairy Cows at Selected Collection Sites in Various Disease States. *Toxins* **2021**, *13*, 446. https:// doi.org/10.3390/toxins13070446

Received: 4 May 2021 Accepted: 25 June 2021 Published: 28 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

cows because it is highly stable in contaminated animal feedstuffs and difficult to degrade through heating and other physical treatments [2].

High-yielding dairy cows are highly susceptible to metabolic diseases such as milk fever, ketosis and rumen acidosis, which are frequently accompanied by subclinical and clinical symptoms of udder infection and decreased reproductive performance. Metabolic disorders are most frequently observed in three critical periods [3]: (i) dry period, (ii) parturition, and (iii) first 100 days of lactation. These periods are characterized by an increased risk of udder inflammation and aseptic diffuse inflammation of the laminar corium, which are indicative of compromised innate immunity, decreased acquired immunity to infectious factors [4], and higher susceptibility to undesirable substances, including secondary metabolites of molds such as ZEN and its metabolites.

In dairy cows, susceptibility to mycotoxins is largely determined by the degree to which these substances are eliminated by ruminal microbiota before they are assimilated by the body [5]. Very few mycotoxins are resistant to microbial detoxification in the rumen, and they cause typical symptoms of poisoning with toxic metabolites. Silage and other stored feedstuffs can contain mycotoxins that possess antibacterial properties and modify ruminal microbiota. These compounds can compromise the detoxification capacity of rumen microorganisms. The rumen contents contaminated with mycotoxins may be absorbed in the duodenum, leading to certain mycotoxin concentrations in dairy cows. The clinical symptoms of mycotoxin poisoning are generally non-specific, and they include metabolic and hormonal disorders accompanied by inflammatory states caused by a weakened immune response [4]. During transition periods, cows are particularly susceptible to mycotoxicosis because the presence of molds and/or mycotoxins in cattle diets further deepens the negative energy balance [3].

In dairy cows, the risk of exposure to ZEN can be assessed directly by identifying mycotoxins in the feed matrix [6], or indirectly by analyzing the respective biomarkers in biological fluids and tissues. Feed matrices are widely applied to evaluate the risk [7] resulting from e.g., the presence of ZEN and its metabolites. However, this approach has several limitations. The feed production process and the health status of animals may affect the bioavailability of ZEN and its metabolites in feed and, consequently, increase the risk of exposure in dairy cows. In addition, mycotoxins are not evenly distributed in the feed matrix, and feed intake is difficult to estimate accurately. Since ZEN is transmitted by numerous vectors (green fodder, roughage and concentrated fodder or water), the levels of this mycotoxin and its metabolites can be more reliably quantified in blood samples (indirect analysis).

Our observations show that during clinical activities, field veterinarians need to be aware of some simple information about mycotoxins: (i) mycotoxins are very often found in small amounts in the plant material used for feed production; (ii) not every laboratory has equipment for detecting the presence of very small amounts (these may be values below the sensitivity of the method); (iii) mycotoxin contamination may occur during the vegetation of feed crops, and the mycotoxins are then evenly distributed throughout the plant material; (iv) the feed may become contaminated with mycotoxins during the storage of the final product (concentrated feed) and then the distribution of eg ZEN may be pinpointed in the feed; (v) the accumulation in the macro-organism of low doses of mycotoxins (below the NOAEL value) taken in the feed takes a very long time, making it impossible to trace the mycotoxin vector; and (iv) ZEN is mycoestrogen and its presence in the mammalian body is immediately noted in the form of endocrine system dysfunction and the resulting perturbations in estrogen-dependent tissues.

In cattle, mycotoxicoses are diagnosed based on the presence of clinical symptoms of infertility or hormonal disorders during exposure to high ZEN doses. However, the influence of very low, measurable concentrations of ZEN on the health status of dairy cows has never been investigated. Such mycotoxin concentrations are frequently encountered in plant materials that are used in the production of feed for dairy cattle [6–8].

Mycotoxins exert various effects on the health status of animals exposed to their different doses [9,10]. The symptoms and health consequences (toxicological) of exposure to high doses of most mycotoxins have been relatively well researched and described [10]. Prolonged exposure to low monotonic doses of mycotoxins is usually well tolerated by monogastric animals [11], which suggests that these compounds can meet the animals' life needs, or exert protective [8,12–15] or therapeutic effects [16]. In ruminants, exposure and digestion processes are similar to those observed in monogastric animals when the rumen contents pass into the intestines. Therefore, the clinical presentation of ZEN mycotoxicosis is also similar [14].

During exposure to low mycotoxin doses, the dose-response relationship has been also undermined by the low dose hypothesis. The above applies particularly to hormonally active chemical compounds [17], including mycoestrogens such as ZEN and its metabolites which disrupt the functioning of the hormonal system, even when ingested in small quantities [18]. This ambiguous dose-response relationship does not justify direct analyses or meta-analyses of the risk (clinical symptoms or the results of laboratory analyses) associated with low dose stimulation and high dose inhibition, which is consistent with the hormesis paradigm [19]. The concept of the lowest identifiable dose which produces counter-intuitive effects is becoming increasingly popular in biomedical sciences [20]. The relevant mechanisms should be investigated to support rational decision-making [8,21]. Such decisions involve the selection of blood sampling sites which are most adequate for assessing the risk of mycotoxin exposure in dairy cows. The main routes of blood inflow and outflow to/from the liver should be examined taking into account the availability of blood vessels and the extent of natural (hepatic) detoxification. Topographic anatomy of anastomoses between the portal venous system (*vena portae*) with the major veins [22], including the external jugular vein (EJV, *v. jugularis externa-*posthepatic), the abdominal subcutaneous vein (ASV, *v. epigastrica cranialis superficialis*-prehepatic) and the median caudal vein (MCV, *v. caudalis mediana*-prehepatic) should be analyzed to confirm the reliability and consistency of the results [4].

The aim of the study was to confirm the occurrence of ZEN and its selected metabolites in the blood of high-yielding dairy cows and selected collection sites (external jugular vein, abdominal subcutaneous vein and median caudal vein) in various disease states (Mastitis, ovarian cysts and Pyometra), in natural conditions.

#### **2. Results**

#### *2.1. Clinical Observations*

Clinical signs of ZEN mycotoxicosis were not observed during the experiment (such as reduced growth rate and milk yield, and causes significant economic cost to the dairy industry). However, cows may have been exposed to natural sources of ZEN in plant materials. Cattle diets probably contained very small amounts of the mycotoxin that approximated the minimal anticipated biological effect level (MABEL). Zearalenone transmission vector/vectors could not be identified with full certainty.

#### *2.2. Concentrations of Zearalenone in Peripheral Blood*

Alpha-ZEL and β-ZEL were not detected in blood samples, or their concentrations were below the limit of detection (LOD).

Highly significant (*p* ≤ 0.01) differences in ZEN concentrations (Figure 1) were observed only in blood samples collected from the MCV. These differences were noted between cows with pyometra compared to blood samples from cows with mastitis (difference of 18.20 pg/mL), cows with ovarian cysts (difference of 21.21 pg/mL) or asymptomatic cows (difference of 26.23 pg/mL). Regardless of the sampling site, ZEN levels were highest in cows displaying clinical symptoms of pyometra. In cows with mastitis and pyometra, ZEN concentrations were lowest in blood sampled from the EJV (5.70 pg/mL and 10.53 pg/mL, respectively) and highest in samples collected from the MCV (8.47 pg/mL

and 26.67 pg/mL, respectively. For ovarian cysts, the trend was the opposite between collection sites (from 7.63 pg/mL at EJV to 5.46 pg/mL at MCV). pg/mL and 26.67 pg/mL, respectively. For ovarian cysts, the trend was the opposite between collection sites (from 7.63 pg/mL at EJV to 5.46 pg/mL at MCV). tween collection sites (from 7.63 pg/mL at EJV to 5.46 pg/mL at MCV).

pg/mL and 26.67 pg/mL, respectively. For ovarian cysts, the trend was the opposite be-

*Toxins* **2021**, *13*, x FOR PEER REVIEW 4 of 13

**Figure 1.** Mean values ( *x* ) and standard deviation (SD) of ZEN concentrations (pg/mL) in peripheral blood sampled from different sites: (1) external jugular vein (EJV, *v. jugularis externa-*posthepatic); (2) abdominal subcutaneous vein (ASV, *v. epigastrica cranialis superficialis*-prehepatic); (3) median caudal vein (MCV, *v. caudalis mediana*-prehepatic) of cows diagnosed with mastitis, ovarian cysts and pyometra, and asymptomatic cows. Limit of detection (LOD) > values below the **Figure 1.** Mean values (*x*) and standard deviation (SD) of ZEN concentrations (pg/mL) in peripheral blood sampled from different sites: (1) external jugular vein (EJV, *v. jugularis externa-*posthepatic); (2) abdominal subcutaneous vein (ASV, *v. epigastrica cranialis superficialis*-prehepatic); (3) median caudal vein (MCV, *v. caudalis mediana*-prehepatic) of cows diagnosed with mastitis, ovarian cysts and pyometra, and asymptomatic cows. Limit of detection (LOD) > values below the limit of detection were regarded as equal to 0. Differences were regarded as statistically significant at \*\* *p* ≤ 0.01. **Figure 1.** Mean values ( *x* ) and standard deviation (SD) of ZEN concentrations (pg/mL) in peripheral blood sampled from different sites: (1) external jugular vein (EJV, *v. jugularis externa-*posthepatic); (2) abdominal subcutaneous vein (ASV, *v. epigastrica cranialis superficialis*-prehepatic); (3) median caudal vein (MCV, *v. caudalis mediana*-prehepatic) of cows diagnosed with mastitis, ovarian cysts and pyometra, and asymptomatic cows. Limit of detection (LOD) > values below the limit of detection were regarded as equal to 0. Differences were regarded as statistically significant at \*\* *p* ≤ 0.01.

limit of detection were regarded as equal to 0. Differences were regarded as statistically significant at \*\* *p* ≤ 0.01.

(Figure 2).

**40**

At the same time, it was found that the mean highest ZEN value (number of samples: the sum of the ZEN concentration values in these samples) was obtained in samples taken from median caudal vein (which accounted for 57%), compared to the other sampling sites At the same time, it was found that the mean highest ZEN value (number of samples: the sum of the ZEN concentration values in these samples) was obtained in samples taken from median caudal vein (which accounted for 57%), compared to the other sampling sites (Figure 2). At the same time, it was found that the mean highest ZEN value (number of samples: the sum of the ZEN concentration values in these samples) was obtained in samples taken from median caudal vein (which accounted for 57%), compared to the other sampling sites (Figure 2).

**Mastitis Ovarian cysts Pyometra Asymptomatic**

**Figure 2.** Percentage share of the average ZEN concentrations in blood sampled from cows diagnosed with mastitis, ovar-**Mastitis Ovarian cysts Pyometra Asymptomatic Figure 2.** Percentage share of the average ZEN concentrations in blood sampled from cows diagnosed with mastitis, ovarian cysts and pyometra and from asymptomatic cows in the monitored herd. **Figure 2.** Percentage share of the average ZEN concentrations in blood sampled from cows diagnosed with mastitis, ovarian cysts and pyometra and from asymptomatic cows in the monitored herd.

ian cysts and pyometra and from asymptomatic cows in the monitored herd. A comparison of ZEN concentrations in samples of peripheral blood (Figure 3) collected from diseased cows revealed significant differences (*p* ≤ 0.05) only in the blood of cows with pyometra. The difference between the samples collected from the EJV and the A comparison of ZEN concentrations in samples of peripheral blood (Figure 3) collected from diseased cows revealed significant differences (*p* ≤ 0.05) only in the blood of cows with pyometra. The difference between the samples collected from the EJV and the A comparison of ZEN concentrations in samples of peripheral blood (Figure 3) collected from diseased cows revealed significant differences (*p* ≤ 0.05) only in the blood of cows with pyometra. The difference between the samples collected from the EJV and the MCV was determined at 16.16 pg/mL. In cows diagnosed with mastitis and ovarian cysts,

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*Toxins* **2021**, *13*, x FOR PEER REVIEW 5 of 13

to those noted in cows with pyometra.

*Toxins* **2021**, *13*, x FOR PEER REVIEW 5 of 13

ZEN concentrations were highly similar (no significant differences) and very low relative to those noted in cows with pyometra. ZEN concentrations were highly similar (no significant differences) and very low relative to those noted in cows with pyometra.

MCV was determined at 16.16 pg/mL. In cows diagnosed with mastitis and ovarian cysts,

MCV was determined at 16.16 pg/mL. In cows diagnosed with mastitis and ovarian cysts, ZEN concentrations were highly similar (no significant differences) and very low relative

**Figure 3.** Mean values ( *x* ) and standard deviation (SD) of ZEN concentrations (pg/mL) in peripheral blood of cows collected in various disease states (Mastitis; Ovarian cysts; Pyometra; Asymptomatic) at different collection sites [external jugular vein (EJV-*v. jugularis externa*-posthepatic); abdominal subcutaneous vein (ASV-*v. epigastrica cranialis superficialis*prehepatic); oraz median caudal vein (MCV-*v. caudalis mediana*-prehepatic)]. Limits of detection (LOD) > values below the **Figure 3.** Mean values (*x*) and standard deviation (SD) of ZEN concentrations (pg/mL) in peripheral blood of cows collected in various disease states (Mastitis; Ovarian cysts; Pyometra; Asymptomatic) at different collection sites [external jugular vein (EJV-*v. jugularis externa*-posthepatic); abdominal subcutaneous vein (ASV-*v. epigastrica cranialis superficialis*-prehepatic); oraz median caudal vein (MCV-*v. caudalis mediana*-prehepatic)]. Limits of detection (LOD) > values below the limit of detection were regarded as equal to 0. Statistically significant difference was determined at \* *p* ≤ 0.05. **Figure 3.** Mean values ( *x* ) and standard deviation (SD) of ZEN concentrations (pg/mL) in peripheral blood of cows collected in various disease states (Mastitis; Ovarian cysts; Pyometra; Asymptomatic) at different collection sites [external jugular vein (EJV-*v. jugularis externa*-posthepatic); abdominal subcutaneous vein (ASV-*v. epigastrica cranialis superficialis*prehepatic); oraz median caudal vein (MCV-*v. caudalis mediana*-prehepatic)]. Limits of detection (LOD) > values below the limit of detection were regarded as equal to 0. Statistically significant difference was determined at \* *p* ≤ 0.05.

limit of detection were regarded as equal to 0. Statistically significant difference was determined at \* *p* ≤ 0.05.

The data presented in Figure 3; Figure 4 show that the highest values of the ZEN level were recorded in Pyometra cows, where the percentage share of the mean values The data presented in Figure 3; Figure 4 show that the highest values of the ZEN level were recorded in Pyometra cows, where the percentage share of the mean values of ZEN concentrations was 44.08%. The data presented in Figure 3; Figure 4 show that the highest values of the ZEN level were recorded in Pyometra cows, where the percentage share of the mean values of ZEN concentrations was 44.08%.

herd of dairy cows.

herd of dairy cows.

**Figure 4.** Percentage share of average ZEN concentrations in blood samples collected from different sites (*external jugular*  **EJV ASV MCV Figure 4.** Percentage share of average ZEN concentrations in blood samples collected from different sites (*external jugular vein*-EJV-posthepatic; *abdominal subcutaneous vein*-ASV-prehepatic; *median caudal vein*-MCV-prehepatic) in the monitored **Figure 4.** Percentage share of average ZEN concentrations in blood samples collected from different sites (*external jugular vein*-EJV-posthepatic; *abdominal subcutaneous vein*-ASV-prehepatic; *median caudal vein*-MCV-prehepatic) in the monitored herd of dairy cows.

*vein*-EJV-posthepatic; *abdominal subcutaneous vein*-ASV-prehepatic; *median caudal vein*-MCV-prehepatic) in the monitored The share of individual blood sampling sites in the values of the ZEN level indicates an upward trend, starting with *external jugular vein* (10.53 ng/mL, which constituted The share of individual blood sampling sites in the values of the ZEN level indicates an upward trend, starting with *external jugular vein* (10.53 ng/mL, which constituted The share of individual blood sampling sites in the values of the ZEN level indicates an upward trend, starting with *external jugular vein* (10.53 ng/mL, which constituted 44.13% in this collection site), through the obtained values in *abdominal subcutaneous vein* (14.20 ng/mL, which was 51.39% at this point of samples), and ending with the concentra-

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tion values obtained in blood samples collected in *median caudal vein* (26.67 ng/mL, which constituted 65.68% at this point of collection).

#### **3. Discussion**

It is generally believed that ruminants are less susceptible to the harmful effects of mycotoxins than monogastric animals because some mycotoxins are degraded by ruminal microbiota [23]. Despite the above, mycotoxins can induce subclinical conditions in dairy cows [24].

The results of the present study should be interpreted in view of the following observations: (i) the feed administered to dairy cows was probably contaminated with very low doses of ZEN [5]; (ii) this is the first study of the type; therefore, the present findings cannot be compared with published data and have to be interpreted by extrapolation.

According to Fushimi et al. [25], very low levels of ZEN in feed do not affect reproductive performance, but they affect anti-Müllerian hormone levels in the blood [26,27] which play an important role in folliculogenesis and are most highly expressed in the granulosa cells of preantral follicles, mostly in the antral stage [28,29]. These hormones inhibit follicle stimulating hormone (FSH)-induced growth and development of the remaining primary follicles and the selection of the dominant follicle [30–32].

In cows, *β*-ZEL is identified more frequently than *α*-ZEL in the intestinal contents contaminated with low doses of ZEN, which implies that detoxification processes are predominant during exposure to very low ZEN doses. It should also be noted that: (i) ZEN could be utilized as a substrate that regulates (inversely) the expression of genes encoding HSDs (Hydroxysteroid Dehydrogenases) which act of molecular switches for the modulation of steroid hormone prereceptors [33–35]; (ii) enterohepatic recirculation occurs before ZEN and its metabolites are biotransformed and eliminated; and (iii) in vitro studies have demonstrated that ZEN metabolites are detected within 15 min to 1 h after ingestion, which indicates that this mycotoxin is rapidly metabolized by ruminal microbiota [5,23]. In the present study, the latter observation was confirmed in asymptomatic cows.

These hypotheses (alone or in combination) could explain the differences in the concentrations of ZEN and its metabolites in the peripheral blood of dairy cows exposed to low doses of the mycotoxin. However, these differences could be also attributed to unknown factors that cause rumen inflammations, thus increasing the risk of absorption of toxic compounds, such as lipopolysaccharides (major risk factors) and/or mycotoxins, through the rumen wall [23]. According to Dänicke et al. [4,36], low ruminal pH (which compromises the buffering capacity of the rumen, [23]) inhibits the biotransformation of parent compounds such as ZEN and metabolite synthesis, which could explain the absence of ZEN metabolites in the examined blood samples (Figures 1 and 2). The cited authors also argued that in ruminants, ZEN (parent compound) can be transported to the postruminal digestive tract at unchanged levels and cause mycotoxicosis [37] or digestive disorders contributing to feed-borne diseases such as mastitis, ovarian cysts and pyometra [3]. The above could be explained by a higher rumen passage rate which decreases ruminal pH, thus decreasing the proportion of Gram-positive bacteria and increasing the population of Gram-negative bacteria. These processes occur mainly during the transition period which is accompanied by vast changes in the metabolic processes of cows due to energy, mineral and vitamin deficiencies.

In the present study, ZEN and its metabolites were not detected in asymptomatic cows (Figures 1 and 3), which could be attributed to the rapid biotransformation of mycotoxins by ruminal microbiota or very low concentrations of ZEN in unspecified feed transmission vectors [6]. Similar results were reported in vitro by Debevere et al. [23] who observed that the biotransformation of ZEN to *α*-ZEL and *β*-ZEL (only in the rumen) is highly limited at normal ruminal pH.

Zearalenone metabolites were not detected in diseased cows (Figures 1 and 3). However, trace amounts of the parent compound were identified in the blood, probably because ZEN is not completely metabolized in the rumen. This mycotoxin is transported to successive intestinal segments in unmodified form, and it reaches peripheral organs with the blood and enters the prehepatic circulation that ends in the portal vein (*vena portae*) which supplies blood to the liver [4,22]. The evaluated herd was probably exposed to ZEN for a long period of time or continuously. In laboratory analyses, ZEN is very often detected in maize silage, green fodder and hay [6,37], which indicates that this mycotoxin is present in feed throughout the year. The present results point to dysfunctions of ruminal microbiota or the liver, a unique immunological site that protects the body against mycotoxins. These defense mechanisms can participate in the development of tolerance [38] or initiate a separate immune response [4].

Subclinical disease states caused by mycotoxins should be analyzed in greater detail in high-yielding dairy cows. Disease states can be also caused by saprophytic or conditionally pathogenic microorganisms (bacterial lipopolysaccharides) which contribute to comorbidities, including mycotoxicosis [23]. The etiological factors of disease include an increase in the rumen passage rate caused by increased concentrate intake or the postnatal period (as a result of the physiological loss of the fetus), as well as metabolic disorders such as subacute rumen acidosis resulting from changes ("shift") in ruminal microbiota [39]. Mycotoxins are less effectively detoxified in the rumen, and they are transported to the intestinal lumen [5]. Zearalenone reaches the intestines and unpaired organs in the abdominal cavity, and it is carried by the prehepatic circulation to the hepatic portal vein and the liver. As a result, substances absorbed from the digestive tract can be more accurately controlled [22].

The above observations were confirmed by ZEN levels in blood samples collected from the MCV of cows with pyometra. These samples were characterized by the highest concentrations of ZEN (Figures 2–4). After ingestion, ZEN reaches the proximal and, subsequently, distal segments of the intestines, and it is transported to the liver by prehepatic vessels where it detoxifies from undesirable or dangerous substances as part of the functional circulation. The described hypothesis was also validated by the percentage share of average ZEN concentrations in blood samples collected from diseased cows. In blood sampled from cows with pyometra, the above parameter was determined at 57% (Figure 2), whereas in the remaining (posthepatic) sampling sites, ZEN concentrations (Figures 1 and 3) and their percentage share (Figures 2 and 4) were much lower because the analyzed mycotoxin had already been detoxified in the liver.

#### **4. Materials and Methods**

All experimental procedures were consistent with Polish regulations defining the conditions and methods of animal experimentation (opinion No. 01/2010/D issued on 21 December, 2016-by the Local Ethics Committee for Animal Experimentation of the University of Warmia and Mazury in Olsztyn, Poland).

#### *4.1. Experimental Animals and Feed*

The animals were kept in a barn with access to pasture. Blood for toxicological analyses was sampled from cows clinically diagnosed with mastitis (9 animals), ovarian cysts (6 animals), pyometra (5 animals) and from 9 asymptomatic cows (with no clinical symptoms).

The research lasted one year, and it involved 150 dairy cows that were free of clinical symptoms of ZEN mycotoxicosis. The milk yield (herd average) at the end of the last farming year was 9700 L per lactation 305 days (there was an upward trend). All cows are fed the fodder at the bunk feeding based on the average yield of the production group.

There are 3 production groups in the herd: (1)—the most efficient (approximately the first 150 days of lactation); (2)—from day 151 to the end of lactation; (3)—dry cows. The tested dairy cows health problems occurred only in Group 1. Groups 1 and 2 receive an additional amount of total mixed ration (TMR) at the feeding station, depending on their individual performance. Group 3 only receives feed at the bunk feeding. Total mixed ration for dairy cows for all animals in the barn was supplied by the same producer. TMR was administered twice a day, at 6:00 a.m. and 5:00 p.m., in a powdery form. The TMR composition declared by the manufacturer is presented in Table 1.

**Table 1.** Declared total mixed ration for caws.

1 Composition of the vitamin-mineral supplements per kg: vitamin A—17,500.00 IU; vitamin D3—5000.00 IU; vitamin E (alpha-tocopherol)—100 mg; B3 (niacin)—400 mg; biotin—400.00 µg; iron (iron sulfate) —110 mg; manganese (manganese sulfate)—125 mg; zinc (zinc sulfate)—125 mg; copper (CuSO4·5H2O)—20 mg; vitamin iodine (potassium iodide)—1.8 mg; selenium (sodium selenate)—0.35 mg; Seldox antioxidatum (BHA-E320, BHT-E321, Ethoxyquin E324)—0.95 mg; flavoring substances—0.5 g.

In addition to the presented TMR composition, the manufacturer also declared the share of components in TMRs for dairy cows, as shown in Table 2.

**Table 2.** Declared total analytical components in total mixed ration for caws.

The approximate chemical composition of the silage to dairy cows (Table 3) was determined using the NIRS™ DS2500 F Feed Analyzer (FOSS, Hillerød, Denmark) which is a monochromatic NIR reflectance and transflectance analyzer with scanning range of 850–2500 nm.

**Table 3.** The results of the silage analysis in g/kg dry metter.

**Table 3.** *Cont.*

#### *4.2. Blood Sampling*

In each cow, blood was sampled from three sites in the hepatic portal system [22]: (1) external jugular vein (EJV, *v. jugularis externa*-posthepatic); (2) abdominal subcutaneous vein (ASV, *v. epigastrica cranialis superficialis*-prehepatic); (3) median caudal vein (MCV, *v. caudalis mediana*-prehepatic). Blood samples of 10 mL each were collected from each site into vials containing 0.5 mL of heparin solution. The samples were centrifuged at 3000 rpm for 20 min at a temperature of 4 ◦C. The separated plasma was stored at −18 ◦C until sample analysis for the presence of ZEN (according to the schedule of the monitoring program).

## *4.3. Extraction Recovery*

The standard addition (fortification) method was used to evaluate the recovery in this study. Non-contaminated sample matrix (blood serum) were enriched by three mycotoxins (ZEN, α-ZEL, β-ZEL) at low (5 pg/mL) medium (10 pg/mL) and high (20 pg/mL) concentrations and pretreated using the methodology outlined in Section 4.4. After analysis, the extraction recovery (ER) method was calculated as: ER = A/B × 100; where A is the slope of the fortified sample after extraction and B is the slope of the fortified sample before extraction. Recovery after extraction from fortified samples ranged from 90% to 102%, suggesting that the pretreatment method met the requirement for mycotoxin determination.

#### *4.4. Mycotoxin Extraction*

Zearalenone, α-ZEL and β-ZEL were extracted from the blood plasma with the use of immunoaffinity columns (Zearala-TestTM Zearalenone Testing System, G1012, VICAM, Watertown, MA, USA). All extraction procedures were conducted in accordance with the manufacturers' instructions. The eluates were placed in a water bath with a temperature of 50 ◦C, and the solvent was evaporated in a stream of nitrogen. Dry residues were combined with 0.5 mL of 99.8% methanol to dissolve the mycotoxins. The procedure were monitored with the use of external standards (Cayman Chemical 1180 East Ellsworth Road Ann Arbor, Michigan 48108 USA, ZEN-catalog number 11353; Batch 0593470-1; *α*-ZEN-catalog number 16549; Batch 0585633-2; *β*-ZEN-catalog number 19460; Batch 0604066-7), and the results were validated by mass spectrometry.

#### *4.5. Chromatographic Analysis of ZEN and Its Metabolites*

The concentrations of ZEN and its metabolites, α-ZEL and β-ZEL, were determined by the Institute of Dairy Industry Innovation in Mr ˛agowo. Zearalenone and its metabolites were quantified in the blood plasma with the use of combined separation methods involving immunoaffinity columns (Zearala-TestTM Zearalenone Testing System, G1012, VICAM, Watertown, MA, USA), Agilent 1260 liquid chromatography (LC) system, and a mass spectrometry (MS, Agilent 6470) system. The prepared samples will be analyzed with the use of the Zorbax rapid resolution chromatographic column (2.1 × 50 mm; 1.8 micron Agilent Eclipse Plus C18) in gradient mode. The mobile phase will contain 0.1% (*v*/*v*) formic acid in water (solvent A) and 0.1% (*v*/*v*) formic acid in acetonitrile (solvent B). Gradient conditions will be as follows: initially, 20% B that increases to 100% B in 4.0 min and back to 20% B in 0.1 min.

Mycotoxin concentrations were determined with an external standard and were expressed in ppt (pg/mL). Matrix-matched calibration standards were applied in the quantification process to eliminate matrix effects that can decrease sensitivity. Calibration standards were dissolved in matrix samples based on the procedure that was used to prepare the remaining samples. A signal-to-noise ratio of 3:1 will be used to estimate the limits of detection (LOD) for ZEN, α-ZEL and β-ZEL. The LOQ will be estimated as the triple LOD value.

#### *4.6. Mass Spectrometric Conditions*

The mass spectrometer was operate with ESI in the negative ion mode. The MS/MS parameters were opimized for each compoud. The linearity was tested by a calibration curve including six levels. Table 4 shows the optimized analysis conditions for the mycotoxins tested.

**Table 4.** Optimized conditions for mycotoxins tested.

#### *4.7. Statistical Analysis*

Statistical analyses were performed by the Department of Discrete Mathematics and Theoretical Computer Science, Faculty of Mathematics and Computer Science of the University of Warmia and Mazury in Olsztyn. Plasma concentrations of ZEN and its metabolites were determined in asymptomatic cows and in three groups of experimental cows: (1) with clinically diagnosed mastitis, (2) with clinically diagnosed ovarian cysts, and (3) with clinically diagnosed pyometra. In each cow, blood was sampled from three different sites. The results were expressed as means (*x*) with standard deviation (SD). In both cases, the differences between mean values were determined by one-way ANOVA. If significant differences were noted between groups, the differences between paired means were determined in Tukey's multiple comparison test. If all values were below LOD (mean and variance equal zero) in any group, the values in the remaining groups were analyzed by one-way ANOVA (if the number of the remaining groups was higher than two), and the means in these groups were compared against zero by Student's t-test. Differences between groups were determined by Student's t-test. The results were regarded as highly significant at *p* < 0.01 (\*\*) and as significant at 0.01 < *p* < 0.05 (\*). Data were processed statistically in Statistica v.13 (TIBCO Software Inc., Silicon Valley, CA, USA, 2017).

#### **5. Conclusions**

The results of this study suggest that blood samples for toxicological analyses should be collected from the MCV (prehepatic vessel) of clinically healthy cows and/or cows displaying subclinical symptoms of disease (ZEN mycotoxicosis). Blood sampled from the MCV improves the reliability of diagnosis of subclinical ZEN mycotoxicosis.

**Author Contributions:** W.B., M.G., Ł.Z. and M.T.G. designed the experiments. Data were analyzed and interpreted by W.B., M.G, M.M., E.O. and K.E.P. The manuscript was drafted and critically read by W.B., M.G., Ł.Z., A.N., A.B. and M.T.G. The manuscript was revised, read and approved by all authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** The project was financially supported by the Minister of Science and Higher Education under the program entitled "Regional Initiative of Excellence" for the years 2019-2022 (project No. 010/RID/2018/19; amount of funding PLN 12,000,000).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**