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. 2019 Oct 7;52(2):777–791. doi: 10.1007/s11250-019-02069-9

Prevalence and risk factors for agents causing diarrhea (Coronavirus, Rotavirus, Cryptosporidium spp., Eimeria spp., and nematodes helminthes) according to age in dairy calves from Brazil

Leonardo Bueno Cruvinel 1, Henderson Ayres 2, Dina María Beltrán Zapa 1, João Eduardo Nicaretta 1, Luiz Fellipe Monteiro Couto 1, Luciana Maffini Heller 1, Thiago Souza Azeredo Bastos 1, Breno Cayeiro Cruz 3, Vando Edésio Soares 4, Weslen Fabricio Teixeira 1, Juliana Silva de Oliveira 5, Juliana Tomazi Fritzen 5, Amauri Alcindo Alfieri 5, Roberta Lemos Freire 5, Welber Daniel Zanetti Lopes 1,6,✉
PMCID: PMC7089087  PMID: 31591674

Abstract

The present study attempted to verify the prevalence of and risk factors for diarrhea-causing agents in dairy calves from Brazil. Additionally, ages with a higher risk of occurrence for each agent were verified by means of the receiver operating characteristic (ROC) curve. The collections were performed on 39 farms, belonging to 29 municipalities located in eight states of Brazil. It was possible to conclude that the prevalence of Coronavirus, Rotavirus, Cryptosporidium spp., Eimeria spp., and nematodes was 7.20% (95% CI 4.54–9.78), 6.37% (95% CI 3.85–8.89), 51.52% (95% CI 45.26–55.57), 3.46% (95% CI 2.24–4.67), and 3.46% (95% CI 2.24–4.67), respectively. Ages with higher probabilities of occurrence of these diseases in calves were < 10, > 8, > 6, > 37, and > 36 days, respectively. Diarrhea occurred more significantly (P < 0.0001) in animals less than 21 days old and mainly on those receiving milk through automatic feeders (P < 0.001). Cryptosporidium spp. were a risk factor for the occurrence of Rotavirus, and vice versa (P = 0.0039) and presented a positive correlation with Coronavirus (P = 0.0089). Calves that drink water from rivers, streams, and ponds had a higher chance of being infected by Eimeria spp. (P < 0.0001), as well as developing infection by nematodes (P < 0.0001). The results found in this study highlight the importance of studying the agents of diarrhea together, once they act as coinfection where the losses triggered for the owners will involve some of these agents simultaneously.

Keywords: Coronavirus, Cryptosporidium spp., Diarrhea, Eimeria spp., Nematodes, Rotavirus

Introduction

Among the main hindrances to bovine production, gastrointestinal illnesses stand out, especially in relation to younger animals. The impacts of these diseases on production usually consist of delayed growth, diarrhea, and even mortality in some cases (Felippelli et al. 2014; Cruvinel et al. 2018a). Among all major agents causing gastrointestinal symptoms in young bovines, viral (Coronavirus and Rotavirus) and parasitic (Cryptosporidium spp., Eimeria spp., and gastrointestinal nematodes) agents are notable.

Rotavirus is usually associated with Coronavirus, and both agents are one of the main enteropathogens responsible for neonatal diarrhea syndrome in calves (Coura et al. 2015; Hayashi et al. 2017; Almeida et al. 2018; Bok et al. 2018; Fritzen et al. 2019).

Cryptosporidium is a protozoan that possesses a direct cycle and is monoxenous and affects bovines, mainly animals aged between 1 and 3 weeks (Murakoshi et al. 2013; Thomson et al. 2017; Masatani et al. 2018). Eimeria is another protozoan that contains thirteen species that can parasitize intestinal cells of cattle (Lucas et al. 2014; Passafaro et al. 2015) with the potential to cause losses of productivity in herds around the world, E. bovis and E. zuernii, depending on animal category and degree of infection (Taubert et al. 2008; Bruhn et al. 2011). Regarding gastrointestinal nematodes, Souza et al. (2008) described that the main impact of infection on livestock production is delayed growth and even mortality, especially young animals.

All previously mentioned agents have been described in an isolated manner in most clinical cases diagnosed; however, on most farms, it is possible that more than one agent may be involved simultaneously. This possibility may prevent researchers and animal owners from achieving a better understanding of the damage caused collectively by these agents in milk-feeding calves aged only a few days, which motivated this research. For this reason, the present study aimed to verify the prevalence and risk factors for agents causing diarrhea (Coronavirus, Rotavirus, Cryptosporidium spp., Eimeria spp., and gastrointestinal nematodes) in dairy calves from Brazil. In addition, the age with higher risk for occurrence of each agent was verified utilizing the receiver operating characteristic (ROC) curve.

Materials and methods

Selection of farms and animals and collection of fecal samples

The present study was conducted between December 2016 and March 2017 on farms belonging located in the states of Rio Grande do Sul, Paraná, Santa Catarina, Rio de Janeiro, São Paulo, Minas Gerais, Goiás, and Ceará that have a focus on milk production (Fig. 1). These states were chosen because they represent 80% of Brazil’s total milk production (IBGE 2017). Initially, 872 farms located in these states were contacted, and only 195 did not administer any vaccine or specific drug against the five agents involved in this study. Among these 195 farms, 39 farms (20%) belonging to 29 municipalities were randomly chosen for conducting the study. In some states, such as Minas Gerais, several farms were selected, while in others, such as Goiás, only one or two were selected. On these 39 farms, fecal samples were collected from 868 female calves. Samples were obtained from animals aged between 1 and 135 days, all of which were consuming milk. All these samples were collected directly from the rectum of animals and, after individual identification (using the animal’s ID number), were stored in isothermal containers filled with ice. The samples were subsequently sent to the Veterinary Parasitology Center of the Veterinary and Animal Husbandry School, Federal University of Goiás (Centro de Parasitologia Veterinária da Escola de Veterinária e Zootecnia da Universidade Federal de Goiás, EVZ/UFG), for processing.

Fig. 1.

Fig. 1

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Spatial distribution of the 39 farms belonging to 29 municipalities located in eight states of Brazil analyzed in this study

Processing of samples

Coronavirus and Rotavirus

For Coronavirus analysis, fecal suspensions at 10–20% (w/v) were prepared, and nucleic acid extraction was performed as described by Alfieri et al. (2006). The RNA of enteric viruses in fecal samples was investigated by molecular techniques, such as reverse transcription-polymerase chain reaction (RT-PCR) and semi-nested PCR (SN-PCR), for partial amplification of the BCoV N gene (Takiuchi et al. 2006), bovine RVAVP4 and VP7 genes (Gentsch et al. 1992; Gouvea et al. 1990), bovine rotavirus B (RVB) NSP2 gene (Gouvea et al. 1991), and bovine rotavirus C (RVC) VP6 gene (Alfieri et al. 1999). The RT-PCR was performed using the oligonucleotide primers upstream 5′-GCCGATCAGTCCGACCAATC-3′(nt79–98) and downstream 5′-AGAATGTCAGCCGGGGTAT-3′(nt 467–485) that amplify a 407 base pair (bp) fragment of the N gene of BCoV. The technique was carried out as described by Tsunemitsu et al. (1999). The reverse transcriptase reaction was conducted as follows. In the tube, 10 μl of RNA sample was added to 2 μl of the downstream primer (50 pmol). The tube was incubated at 100 °C for 2 min and then quenched on ice for 5 min. Subsequently, 4 μl of 5 × RT buffer [250 mM tris-HCl (pH 8.3), 375 mM KCl, 15 mM MgCl2], 1 μl of 0.1 M dithiothreitol, 2 μl of 10 mM dNTPs, 0.5 μl of RNAsin (Promega Corporation), and 0.5 μl of AMV RT (Promega Corporation) were added and incubated at 37 °C for 60 min. Then 10 μl of the RT reaction samples was added to 40 μl of the PCR mixture. The PCR mixture consisted of 5 μl of 10 × buffer [100 mM Tris-HCl (pH 8.3), 500 mM KCl, 15 mM MgCl2, 0.01% gelatin], 1 μl of 10 mM dNTPs, 0.5 μl of the upstream primer (50 pmol), 0.5 μl of the downstream primer (50 pmol), 32.5 μl of water, and 0.5 μl of Taq polymerase (Promega Corporation) (5 U/μl). The mixture was overlaid with mineral oil and then subjected to 5 min of preheating at 94 °C, 35 cycles of 1 min at 94 °C, 1 min at 58 °C, 2 min at 72 °C, and a final 7-min incubation at 72 °C. Amplified products were analyzed by electrophoresis on a 2% agarose gel in TBE buffer, pH 8.4 (89 mM Tris; 89 mM boric acid; 2 mM EDTA), stained with ethidium bromide (0.5 μg/ml), and visualized under UV light.

Regarding Rotavirus, nucleic acid extraction from fecal samples was performed using the phenol/chloroform/isoamyl alcohol (Malik et al. 2012; Silva et al. 2012) and silica/guanidinium isothiocyanate nucleic acid extraction methods (Alfieri et al. 2006). Samples were screened for RVA by silver staining after polyacrylamide gel electrophoresis (PAGE) (Herring et al. 1982) modified by Pereira et al. (1983).

In the present study, only samples from bovines less than 21 days old were analyzed for the presence of Coronavirus and Rotavirus. Therefore, from 868 collected samples, 361 were evaluated for the presence of Coronavirus and Rotavirus. This protocol was limited due to costs and was also based on the results obtained from previous studies conducted by Jerez et al. (2002), and Takiuchi et al. (2006), all of which emphasized the importance of these agents in calves up to 3 weeks old.

Cryptosporidium spp.

Each sample was homogenized, filtered with the aid of plastic sieves, and submitted to centrifuge washing with deionized water with 0.02% Tween 20. Later, the purification of samples was performed according to the methodology by Meloni and Thompson (1996), as follows: 32 ml of solution containing the fecal samples, diluted in deionized water/Tween 20, was added to a 50-ml tube together with 8 ml of ether. Next, tubes were agitated vigorously in a vortex for 30 s and then centrifuged at 2000g for 8 min.

After purification, the samples were washed twice by centrifugation with the water/Tween 20 solution, and the final pellet was resuspended in phosphate buffered saline solution (PBS). Obtained sediments were washed twice through centrifugation, utilizing distilled water/Tween 20 diluted in phosphate buffered saline solution (PBS). The presence of Cryptosporidium spp. oocysts was determined through a negative coloration technique using malachite green (Elliot et al. 1999). Approximately 15 μl of the sample and 15 μl of malachite green were added and homogenized over a microscope slide, and assembly was finished with a cover slip. Oocyst research was performed with the aid of light microscopy at magnifications of × 400 and × 1000.

Similar to Coronavirus and Rotavirus, only samples belonging to animals aged 21 days or less were analyzed for the presence of Cryptosporidium spp. Therefore, from all 868 collected samples, 361 were analyzed for the presence of the aforementioned parasite. This experimental protocol was based on previous studies performed by Garcia and Lima (1994), Langoni et al. (2004), Cardoso et al. (2008), Feitosa et al. (2008), and Meireles (2010), in which the authors demonstrated that this disease occurs most frequently and causes the most severe symptoms in milk-feeding calves aged less than 3 weeks.

Eimeria spp. and gastrointestinal nematodes

From each of the 868 fecal samples collected, an aliquot was taken for the quantification of Eimeria spp. oocysts per gram (OoPG) of feces and nematodes eggs per gram (EPG) of feces (Gordon and Whitlock 1939, as modified by Ueno and Gonçalves 1998). Each oocyst or egg found corresponded to 50 OoPG or EPG, respectively.

For each farm, all samples that presented OoPG counts of greater than or equal to 50 were placed together in pools. In this case, 5 g of feces was used from each animal to form the pools. For example, in farm X, if there were 100 bovines with OoPG counts of greater than or equal to 50, 5 g of each of these 100 animals was used to compose the pool. On farm Y, if there were 20 animals with OoPG counts of greater than or equal to 50, 5 g of each of these 20 animals was used to compose the pool. These pools were then processed using the method of centrifugal flotation in sugar solution. Later, samples were filtered using sieves with folded gauze. A 2% potassium dichromate (K2Cr2O7) solution was added to the results from this filtration with a volume-by-volume approach, and this mixture was kept at 24 °C for 14 days under oxygenation (using aquarium oxygenator pumps) to stimulate oocyst sporulation under laboratory conditions.

Oocysts were recovered by centrifugation in a 60% saturated sugar solution, and approximately 100 oocysts per pool from each farm were identified under an optical microscope coupled to a computerized system (LAS, Leica®). This process of recovering 100 oocysts per pool for the identification of Eimeria species was performed in triplicate. Differentiation among species was performed according to the phenotypic characteristics of oocysts, such as color, presence or absence of micropyle, and length and width (Daugschies and Najdrowski 2005).

Data analysis

Values regarding the prevalence (%) of diarrhea, Coronavirus, Rotavirus, Cryptosporidium spp., Eimeria spp., and gastrointestinal nematodes were classified in increasing order for municipalities and states. For the smallest prevalence, an odds ratio (OR) of one was attributed, with the remaining ORs being calculated in relation to this value using the Z test to verify significance (P ≤ 0.05).

Risk factors (RF) associated with the occurrence of each agent and diarrhea were associated with sex, race, age, breeding system (isolated housing or tied in chains; paddocks; compost barn), milk offer (bucket/bottle; direct from cows; automatic feeders), weaning age, herd size, if different categories (ages) are kept together, if different animal species are kept together, water source (artesian well; rivers, lakes, and ponds), location where animals are fed (through; directly on the ground), if herd reposition is through acquisition of animals from other farms (if yes, do they quarantine animals?).

Frequency of occurrences of diarrhea, Coronavirus, Rotavirus, Cryptosporidium spp., Eimeria spp., and nematodes was computed inside each age level, for which posterior correlations were determined using the Spearman post-correlation.

All data manipulation procedures were performed on the Epi Info software, version 7.1.5.2 (WHO, 2015), version 12 (StatSoft, Inc. 2014), and Microsoft Excel® 2016.

The ROC curve was used to establish for each animal age (in days) with the highest combined sensitivity and specificity for the occurrence and detection of the respective agents, as well as diarrhea (Galpasoro and Fernandez 1998).

Results

Coronavirus, Rotavirus, Cryptosporidium spp., and diarrhea

Of the 361 fecal samples examined, 26 (7.2%, CI 95% 4.54–9.87) were positive for Coronavirus, 23 (6.37%, CI 95% 3.85–8.89) were positive for Rotavirus, and 182 (50.41%, CI 95% 45.26–55.57) were positive for Cryptosporidium spp. (Table 1).

Table 1.

Analysis of association between the citys of different States of Brazil, referring to the prevalence of different agensts diagnosed in the dairy calves

State City Agent Total Negative Positive Prevalence Odds ratio
Value 95 % CI z statistic Significance level
MG Baependi Coronavirus 2 2 0 0.00%
MG Cruzalia 3 3 0 0.00%
MG Curvelo 15 15 0 0.00%
MG Pará de Minas 18 18 0 0.00%
MG Pouso Alto 6 6 0 0.00%
MG Prata 12 12 0 0.00%
MG Sete Lagoas 11 11 0 0.00%
MG Três Corações 5 5 0 0.00%
PR Ampere 2 2 0 0.00%
PR Chopinho 6 6 0 0.00%
PR Francisco Beltrão 1 1 0 0.00%
PR Marechal Candido Rondon 2 2 0 0.00%
RJ Barra Mansa 7 7 0 0.00%
RS Saldanha Marinho 5 5 0 0.00%
SC Xanxerê 14 14 0 0.00%
SP Areias 17 17 0 0.00%
MG Uberlandia 42 41 1 2.38% 1.0000
MG Paraopeba 27 26 1 3.70% 1.5769 0.0945 to 26.3258 0.317 0.7511
GO Morrinhos 22 21 1 4.55% 1.9524 0.1151 to 33.1132 0.463 0.6432
MG Inhauma 56 51 5 8.93% 4.0196 0.4425 to 36.5097 1.236 0.2165
PR Nova Cantu 9 8 1 11.11% 5.1250 0.5280 to 49.7441 1.409 0.1588
CE Umirim 14 12 2 14.29% 6.8333 0.5273 to 88.5525 1.470 0.1415
RS Capão do Leão 14 12 2 14.29% 6.8333 0.8226 to 56.7613 1.779 0.0752
CE Limoeiro do Norte 19 16 3 15.79% 7.6875 1.1053 to 53.4674 2.061 0.0393
RS Condor 5 4 1 20.00% 10.2500 0.8293 to 126.6924 1.814 0.0697
PR Palmital 9 7 2 22.22% 11.7143 0.7900 to 173.7121 1.789 0.0737
MG Virgínia 4 3 1 25.00% 13.6667 0.8691 to 214.9208 1.860 0.0629
RS Almirante do Sul 3 2 1 33.33% 20.5000 0.7567 to 555.3489 1.794 0.0727
MG Itutinga 11 6 5 45.45% 34.1667 2.3476 to 497.2636 2.585 0.0097
Total - 361 335 26 7.20% - - - - -
GO Morrinhos Rotavirus 22 22 0 0.00%
MG Baependi 2 2 0 0.00%
MG Cruzalia 3 3 0 0.00%
MG Curvelo 15 15 0 0.00%
MG Itutinga 11 11 0 0.00%
MG Pará de Minas 18 18 0 0.00%
MG Sete Lagoas 11 11 0 0.00%
MG Três Corações 5 5 0 0.00%
MG Virgínia 4 4 0 0.00%
PR Chopinho 6 6 0 0.00%
PR Francisco Beltrão 1 1 0 0.00%
PR Marechal Candido Rondon 2 2 0 0.00%
PR Nova Cantu 9 9 0 0.00%
PR Palmital 9 9 0 0.00%
RS Almirante do Sul 3 3 0 0.00%
RS Condor 5 5 0 0.00%
RS Saldanha Marinho 5 5 0 0.00%
CE Limoeiro do Norte 19 18 1 5.26% 1.0000
SP Areias 17 16 1 5.88% 1.1250 0.0649 to 19.4973 0.081 0.9355
CE Umirim 14 13 1 7.14% 1.3846 0.0788 to 24.3436 0.222 0.8239
RS Capão do Leão 14 13 1 7.14% 1.3846 0.0780 to 24.5796 0.222 0.8245
SC Xanxerê 14 13 1 7.14% 1.3846 0.0780 to 24.5796 0.222 0.8245
MG Prata 12 11 1 8.33% 1.6364 0.0913 to 29.3207 0.334 0.7380
MG Inhauma 56 51 5 8.93% 1.7647 0.1872 to 16.6390 0.496 0.6198
MG Paraopeba 27 24 3 11.11% 2.2500 0.4964 to 10.1991 1.052 0.2930
MG Uberlandia 42 37 5 11.90% 2.4324 0.5316 to 11.1302 1.146 0.2520
MG Pouso Alto 6 5 1 16.67% 3.6000 0.3463 to 37.4241 1.072 0.2836
RJ Barra Mansa 7 5 2 28.57% 7.2000 0.4831 to 107.3135 1.432 0.1521
PR Ampere 2 1 1 50.00% 18.0000 0.7188 to 450.7804 1.759 0.0786
Total - 361 338 23 6.37% - - - - -
RS Condor Cryptosporidium spp 5 5 0 0.00% 0.0000
MG Cruzalia 3 3 0 0.00% 0.0000
PR Francisco Beltrão 1 1 0 0.00% 0.0000
PR Palmital 9 9 0 0.00% 0.0000
RS Saldanha Marinho 5 4 1 20.00% 1.0000
CE Limoeiro do Norte 19 14 5 26.32% 1.4286 0.1273 to 16.0268 0.289 0.7725
MG Sete Lagoas 11 7 4 36.36% 2.2857 0.4626 to 11.2926 1.014 0.3104
MG Três Corações 5 3 2 40.00% 2.6667 0.3044 to 23.3642 0.886 0.3757
MG Uberlandia 42 25 17 40.48% 2.7200 0.4100 to 18.0470 1.036 0.3000
MG Paraopeba 27 15 12 44.44% 3.2000 1.2038 to 8.5066 2.332 0.0197
GO Morrinhos 22 12 10 45.45% 3.3333 1.0750 to 10.3354 2.085 0.0370
SP Areias 17 9 8 47.06% 3.5556 0.9991 to 12.6530 1.959 0.0502
PR Ampere 2 1 1 50.00% 4.0000 0.2134 to 74.9789 0.927 0.3539
PR Marechal Candido Rondon 2 1 1 50.00% 4,0000 0.0794 to 201.6018 0.693 0.4882
MG Pará de Minas 18 9 9 50.00% 4.0000 0.2153 to 74.2984 0.930 0.3524
MG Baependi 2 1 1 50.00% 4.0000 0.2153 to 74.2984 0.930 0.3524
SC Xanxerê 14 7 7 50.00% 4.0000 0.2066 to 77.4425 0.917 0.3592
MG Curvelo 15 7 8 53.33% 4.5714 1.0635 to 19.6507 2.043 0.0411
PR Nova Cantu 9 4 5 55.56% 5.0000 0.9501 to 26.3133 1.900 0.0575
RS Capão do Leão 14 6 8 57.14% 5.3333 0.9861 to 28.8448 1.944 0.0519
MG Prata 12 5 7 58.33% 5.6000 1.1753 to 26.6835 2.163 0.0306
CE Umirim 14 5 9 64.29% 7.2000 1.4756 to 35.1316 2.441 0.0146
MG Inhauma 56 19 37 66.07% 7.7895 2.2877 to 26.5227 3.284 0.0010
RS Almirante do Sul 3 1 2 66.67% 8.0000 0.6811 to 93.9589 1.654 0.0980
MG Virgínia 4 1 3 75.00% 12.0000 0.4430 to 325.0823 1.476 0.1399
MG Itutinga 11 2 9 81.82% 18.0000 1.1703 to 276.8459 2.073 0.0382
PR Chopinho 6 1 5 83.33% 20.0000 1.4305 to 279.6257 2.226 0.0260
MG Pouso Alto 6 1 5 83.33% 20.0000 0.9601 to 416.6103 1.934 0.0531
RJ Barra Mansa 7 1 6 85.71% 24.0000 1.1768 to 489.4649 2.066 0.0388
Total - 361 179 182 50.41% - - - - -
RS Almirante do Sul Eimeria spp 22 22 0 0.00%
PR Ampere 4 4 0 0.00%
SP Areias 26 26 0 0.00%
MG Baependi 9 9 0 0.00%
PR Chopinho 17 17 0 0.00%
MG Cruzalia 9 9 0 0.00%
MG Curvelo 42 42 0 0.00%
PR Francisco Beltrão 5 5 0 0.00%
MG Inhaúma 61 61 0 0.00%
MG Itutinga 16 16 0 0.00%
CE Limoeiro do Norte 41 41 0 0.00%
PR Marechal Candido Rondon 17 17 0 0.00%
PR Nova Cantu 9 9 0 0.00%
PR Palmital 16 16 0 0.00%
MG Pouso Alto 19 19 0 0.00%
MG Prata 36 36 0 0.00%
PR Realeza 5 5 0 0.00%
RS Saldanha Marinho 12 12 0 0.00%
MG Sete Lagoas 42 42 0 0.00%
MG Três Corações 11 11 0 0.00%
MG Virgínia 12 12 0 0.00%
SC Xanxerê 66 66 0 0.00%
MG Paraopeba 94 92 2 2.13% 1.0000
MG Pará de Minas 37 36 1 2.70% 1.2778 0.1124 to 14.5320 0.198 0.8433
MG Uberlândia 69 66 3 4.35% 2.0909 0.2098 to 20.8417 0.629 0.5295
RS Condor 22 21 1 4.55% 2.1905 0.2162 to 22.1978 0.664 0.5069
RS Capão do Leão 29 27 2 6.90% 3.4074 0.2890 to 40.1756 0.974 0.3301
GO Morrinhos 37 34 3 8.11% 4.0588 0.6324 to 26.0518 1.477 0.1397
CE Umirim 32 29 3 9.38% 4.7586 0.8911 to 25.4121 1.825 0.0680
RJ Barra Mansa 28 25 3 10.71% 5.5200 1.0212 to 29.8368 1.984 0.0472
MG Pequi 23 11 12 52.17% 50.1818 11.7668 to 214.0096 5.292 0.0000
Total - 868 838 30 3.46% - - - - -
PR Ampere Gastrointestinal nematodes 4 4 0 0.00%
SP Areias 26 26 0 0.00%
MG Baependi 9 9 0 0.00%
RS Capão do Leão 29 29 0 0.00%
PR Chopinho 17 17 0 0.00%
MG Cruzalia 9 9 0 0.00%
MG Curvelo 42 42 0 0.00%
PR Francisco Beltrão 5 5 0 0.00%
MG Inhaúma 61 61 0 0.00%
MG Itutinga 16 16 0 0.00%
CE Limoeiro do Norte 41 41 0 0.00%
PR Marechal Candido Rondon 17 17 0 0.00%
GO Morrinhos 37 37 0 0.00%
PR Nova Cantu 9 9 0 0.00%
PR Palmital 16 16 0 0.00%
MG Pará de Minas 37 37 0 0.00%
MG Paraopeba 94 94 0 0.00%
MG Prata 36 36 0 0.00%
PR Realeza 5 5 0 0.00%
RS Saldanha Marinho 12 12 0 0.00%
MG Uberlândia 69 69 0 0.00%
CE Umirim 32 32 0 0.00%
MG Virgínia 12 12 0 0.00%
SC Xanxerê 66 66 0 0.00%
MG Sete Lagoas 42 41 1 2.38% 1.0000
MG Três Corações 11 10 1 9.09% 4.1000 0.2356 to 71.3604 0.968 0.3330
RS Almirante do Sul 22 20 2 9.09% 4.1000 0.3307 to 50.8384 1.098 0.2720
RS Condor 22 20 2 9.09% 4.1000 0.5248 to 32.0293 1.345 0.1785
RJ Barra Mansa 28 22 6 21.43% 11.1818 2.0202 to 61.8901 2.766 0.0057
MG Pouso Alto 19 14 5 26.32% 14.6429 3.7472 to 57.2194 3.860 0.0001
MG Pequi 23 10 13 56.52% 53.3000 14.3470 to 198.0133 5.938 0.0000
Total - 868 838 30 3.46% - - - - -
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Analyzing these results makes it possible to observe that in all 29 municipalities from which fecal samples were obtained, Coronavirus, Rotavirus, and Cryptosporidium spp. were found in 13, 12, and 25 cities, respectively. In this case, the prevalence found for Coronavirus varied between 0.0 and 45.45% (CI 95% 16.03–74.88), with the municipalities of Limoeiro do Norte (Ceará state) and Itutinga (Minas Gerais state) presenting a higher probability of calves being infected by Coronavirus (with OR > 1 and CI 95% > 1). For Rotavirus, such values were between 0.0 and 50.0% (Ampere, Paraná state CI 95% 0.0–119.3). However, no cities were found with elevated probabilities of detecting Rotavirus parasitizing animals. For Cryptosporidium spp., prevalence varied from 0.0 to 85.71% (CI 95% 59.79–116.64), and some municipalities on the states of Minas Gerais (Paraopeba, Curvelo, Prata, Inhauma, and Itutinga), Goiás (Morrinhos), Ceará (Umirin), Paraná (Chopinho), and Rio de Janeiro (Barra Mansa) presented higher probabilities of animals being infected by this protozoan (OR > 1 and CI 95% > 1).

The prevalence of Coronavirus, Rotavirus, Cryptosporidium spp., and diarrhea observed in calves based on the age of animals in days is described in Fig. 2. It is possible to observe that the most prevalence of Coronavirus (4–14%), Rotavirus (5–27%), Cryptosporidium spp. (4–14%), and diarrhea (1–15%) occurred on days 4 to 15, 9 to 15, 8 to 15, and 1 to 21 of age respectively (Fig. 2).

Fig. 2.

Fig. 2

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Results obtained for prevalence of each of the five agents, as well as prevalence of diarrhea observed in calves

Analyses performed by the ROC curve determined the age, in days, associated with a higher occurrence of the investigated agents. Through this analysis, it was possible to verify superior sensitivity and specificity for the occurrence of Coronavirus in cattle ≤ 10 days old. However, for Cryptosporidium spp. and Rotavirus, the association criteria for these diseases in animals were determined to be > 6 and > 8 days old, respectively (Table 2). When analyzing diarrhea occurrence by the ROC curve, higher combined sensitivity and specificity for microbe detection with these parameters were observed when the animals were less than 21 days old (Table 2). Importantly, for diarrhea, ROC curve analysis was performed with all 868 fecal samples collected from calves between 1 and 135 days old.

Table 2.

Calculation of the area under the ROC curve, confidence interval, level of significance, and result of the association criterion for agent occurrence

Variable Value Variable Value
Coronavirus Rotavirus
  Area under the TOC curve (AUC) 0.645 Area under the TOC curve (AUC) 0.667
  95% confidence interval 0.574–0.712 95% confidence interval 0.600–0.730
  Significance level P 0.0433 Significance level P 0.0103
  Associated criterion for occurrence in age (days) ≤ 10 Associated criterion for occurrence in age (days) > 8
Cryptosporidium spp. Eimeria spp.
  Area under the TOC curve (AUC) 0.689 Area under the TOC curve (AUC) 0.654
  95% confidence interval 0.623–0.749 95% confidence interval 0.613–0.694
  Significance level P < 0.0001 Significance level P 0.0007
  Associated criterion for occurrence in age (days) > 6 Associated criterion for occurrence in age (days) > 37
Gastrointestinal nematodes Diarrhea
  Area under the TOC curve (AUC) 0.653 Area under the TOC curve (AUC) 0.652
  95% confidence interval 0.612–0.692 95% confidence interval 0.614–0.688
  Significance level P 0.0002 Significance level P < 0.0001
  Associated criterion for occurrence in age (days) > 36 Associated criterion for occurrence in age (days) ≤ 21
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Concerning the results from all 13 risk factors evaluated (Table 3), regarding the ORs of each of these factors influencing animals acquiring any of the three evaluated agents and diarrhea, it was verified that bovines with Rotavirus presented a greater probability of coinfection by Cryptosporidium spp. (OR 1.4783; P = 0.0039) and diarrhea (OR 1.776; P < 0.0001). Similarly, cattle with Cryptosporidium spp. presented a higher relative risk of being coinfected by Rotavirus (OR 1.4783; P = 0.0039) and of presenting diarrhea (OR 1.6096; P = < 0.0001). In addition, raising animals in compost barn systems presented as a risk factor for bovines to be infected with Cryptosporidium spp. compared with animals raised isolated in housing or tied on chains (OR 1.1928; P = 0.0273).

Table 3.

Risk factors associated with infection by different agents and diarrhea in dairy calves of different states in Brazil

Rotavirus Negative Positive Relative risk
Valor teste 95% CI z statistic Significance level
  Cryptosporidium spp. Negative 169 169 1.4783 1.1339 to 1.9271 2.888 0.0039
Positive 6 17
  Diarrhea Negative 172 165 1.776 1.4655 to 2.1524 2.632 < 0.0001
Positive 3 20
Cryptosporidium spp. Negative Positive Relative risk
Valor teste 95% CI z statistic Significance level
  Rotavirus Negative 169 169 1.4783 1.1339 to 1.9271 2.888 0.0039
Positive 6 17
  Diarrhea Negative 106 68 1.6096 1.2971 to 1.9974 4.322 < 0.0001
Positive 69 117
  Calf rearing Isolated 132 126 1.1928 1.0199 to 1.3949 2.207 0.0273
Collective 129 180
Eimeria spp. Negative Postive Relative risk
Valor teste 95% CI z statistic Significance level
  Source water Artesian well 660 11 7.931 3.7928 to 16.5843 5.502 < 0.0001
No artesian well 174 23
  Gastrointestinal nematodes Negative 820 18 18.6222 9.8865 to 350.767 9.052 < 0.0001
Positive 18 12
Diarrhea Negative Positive Relative risk
Valor teste 95% CI z statistic Significance level
  Cryptosporidium spp. Negative 107 68 1.6188 1.3042 to 2.0094 4.368 < 0.0001
Positive 69 117
  Rotavirus Negative 173 3 6.3423 1.9183 to 20.9698 3.028 0.0025
Positive 165 20
  Milk supply Automatic breastfeeding 18 150 2.0172 1.7600 to 2.3120 10.08 < 0.0001
Bucket/bottle 170 135
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The occurrence of diarrhea in animals presents a relative risk of occurring with the presence of Rotavirus (OR 6.3423; 95% CI 1.9183–20.9698; P = 0.0025) and Cryptosporidium spp. (OR 1.6188; 95% CI 1.3042–2.00094; P < 0.0001) and when milk is provided for animals through automatic feeders (OR 2.0172; 95% CI 1.7600–2.3120; P < 0.0001), as described in Table 3.

The results of the Spearman correlation analysis show that there was a negative correlation (P ≤ 0.05) between the age of calves and the presence of Coronavirus, Cryptosporidium spp., and diarrhea (Table 4). In addition, positive correlations (P ≤ 0.05) were demonstrated between Rotavirus and Cryptosporidium spp., Cryptosporidium spp. and Coronavirus, as well as Coronavirus/Rotavirus/Cryptosporidium spp. and the occurrence of diarrhea (Table 4).

Table 4.

Correlation matrix and significance level of the parameters evaluated by the Spermann method

Variable - Age Coronavirus Cryptosporidium spp Diarrhea Eimeria spp Nematodes Rotavirus
Age correlation matrix 1.0000 -0.5914 -0.4657 -0.6215 -0.0996 -0.1439 -0.0238
value of P 1.0000 0.0003 0.0063 0.0001 0.5812 0.4243 0.8954
Coronavirus correlation matrix -0.5914 1.0000 0.5836 0.6594 0.1526 0.2747 0.3147
value of P 0.0003 1.0000 0.0004 0.0000 0.3965 0.1218 0.0745
Cryptosporidium spp correlation matrix -0.4657 0.5836 1.0000 0.8893 0.2725 0.2555 0.4482
value of P 0.0063 0.0004 1.0000 0.0000 0.1249 0.1513 0.0089
Diarrhea correlation matrix -0.6215 0.6594 0.8893 1.0000 0.3696 0.3808 0.5137
value of P 0.0001 0.0000 0.0000 1.0000 0.3426 0.2879 0.0022
Eimeria spp correlation matrix -0.0996 0.1526 0.2725 0.3696 1.0000 -0.1000 0.2054
value of P 0.5812 0.3965 0.1249 0.0634 1.0000 0.5798 0.2515
Nematodes correlation matrix -0.1439 0.2747 0.2555 0.3808 -0.1000 1.0000 0.2518
value of P 0.4243 0.1218 0.1513 0.0788 0.5798 1.0000 0.1575
Rotavirus correlation matrix -0.0238 0.3147 0.4482 0.5137 0.2054 0.2518 1.0000
value of P 0.8954 0.0745 0.0089 0.0022 0.2515 0.1575 1.0000
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Eimeria spp. and gastrointestinal nematodes

Of all 868 fecal samples collected from animals between 1 and 135 days of age, 30 (3.46%, CI 95% 2.24–4.67) were diagnosed as positive for Eimeria spp. and gastrointestinal nematodes. In the 29 sampled municipalities, it was possible to detect bovines infected by these agents in nine and seven cities, respectively (Table 1). In locations where Eimeria spp. presence was identified, a prevalence of 2.13% (Paraopeba, state of Minas Gerais; CI 95%, 0.0–5.04) to 52.17% (Pequi, state of Minas Gerais; CI 95% 31.56–72.59) was observed. For gastrointestinal nematodes, values of prevalence in places where these agents were found varied between 2.38% (Sete Lagoas, Minas Gerais; CI 95% − 2.23–6.99) and 56.52% (Pequi, Minas Gerais; CI 95% 36.26–76.78). The cities Barra Mansa, state of Rio de Janeiro (10.71%; CI 95% 0.0–22.17), and Pequi (52.17%; CI 95% 31.76–72.59) presented an elevated probability of bovines being infected by Eimeria spp. (OR > 1 and CI 95% > 1), while both these cities, as well as Pouso Alto, state of Minas Gerais, demonstrated elevated probabilities of animals being infected by gastrointestinal nematodes (Table 1).

The prevalence of Eimeria spp. and gastrointestinal nematodes observed in calves based on the age of animals in days is described in Fig. 2. It is possible to observe that the most prevalence of Eimeria spp. (16–23%) and gastrointestinal nematodes (8–16%) occurred on days 40 to 50 of age respectively (Fig. 2). In the ROC curve analysis performed for both of these agents, it was possible to verify the association criteria for the significant occurrence of Eimeria spp. and gastrointestinal nematodes in bovines aged > 37 and > 36 days, respectively (Table 2). Among the risk factors analyzed, it could be verified that water source was presented as a risk factor for calves to be infected by Eimeria spp.; specifically, animals whose water source was lakes, streams, and ponds had an almost eightfold greater probability of infected by this protozoan compared with animals with artesian water sources (OR 7.931; P < 0.0001). Additionally, bovines infected by Eimeria spp. presented a more than 18-fold greater probability of being coinfected by gastrointestinal nematodes (OR 18.622; P < 0.0001), as described in Table 3.

It was possible to identify the following seven Eimeria species in decreasing order of frequency of occurrence: E. bovis (33.2%), E. alabamensis (17.8%), E. zuernii (15.2%), E. cylindrica (13.9%), E. ellipsoidalis (11.6%), E. wyomingensis (5.1%), and E. canadensis (3.3%).

No risk factors were verified for calves to be infected by gastrointestinal nematodes, and no correlation was found between these two agents (Eimeria spp. and gastrointestinal nematodes) and animal age, presence of Coronavirus, Rotavirus, Cryptosporidium spp., and diarrhea when analyzed by Spearman’s method (Table 4).

Figure 2 summarizes the results obtained for the prevalence of each of the five agents described in this research paper, as well as the prevalence of diarrhea observed in calves based on the age of animals in days.

Discussion

Although published research studies are of high importance to science, from an epidemiological point of view, there is a gap in the body of knowledge, since such works study infectious agents separately or grouping only viral pathogens for example.

The joint occurrence of Coronavirus and Rotavirus was investigated by Jerez et al. (2002) on dairy farms located in several municipalities of São Paulo state, Brazil. In this case, coinfections with Rotavirus and Coronavirus were detected in 3% of all calves. Although these two viral agents were observed simultaneously in animals, it was not possible to determine the occurrence of one agent that was a risk factor for the appearance of either agent (P > 0.05). This possibility was confirmed in the present work, as it is possible to observe that these agents appear in different periods and ages, as previously described. Feitosa et al. (2008), analyzing the importance of Cryptosporidium spp. as a cause of diarrhea in calves, verified a peak of occurrence in 15-day-old animals and observed that all samples positive for this protozoan were also positive for Coronavirus and Rotavirus. In the present study, which analyzed all of these enteropathogenic agents together, it is possible to verify that Rotavirus was a risk factor for the occurrence of Cryptosporidium spp., and this protozoan also showed a significant positive correlation with Coronavirus, with these three agents triggering diarrhea in animals. However, when analyzing specificity and sensitivity to detect an agent obtained by the ROC curve, it is possible to determine that there were interactions between Cryptosporidium spp. and Coronavirus from the 6th to the 10th day of age, as well as interactions between Cryptosporidium spp. and Rotavirus from the 7th to 21st day of age, with peaks of diarrhea being observed in animals at the 15th day of life, as observed by Feitosa et al. (2008), only for Cryptosporidium spp. The results of this study indicate that the diarrhea affecting calves between 6 and 10 days of life occurs mainly by Coronavirus and Cryptosporium, whereas the diarrhea described in calves at 10 to 21 days of age occurs mainly by Cryptosporidium and Rotavirus. The results obtained in this work demonstrate the importance of conducting studies involving several enteropathogenic agents together in calves of different ages.

Another risk factor that should be emphasized is that calves raised in compost barns were 1.19 times more likely to become infected with Cryptosporidium spp. than animals bred in isolated housing or tied in chains (P = 0.0273). The results obtained in a study conducted by Waele et al. (2010) support the findings of the present study. In the study by these researchers, the use of halofuginone lactate for preventing cryptosporidiosis in naturally infected neonatal calves was investigated on a dairy farm with a high prevalence of infection, where animals were kept in two different calf rearing systems. These researchers reported that halofuginone lactate use combined with good hygiene measures, such as rearing animals in clean individual pens, was the most effective method to reduce cryptosporidiosis risk among 7–13-day-old calves when comparing these calves with animals bred in collective pens. The latter had easier access to excrement from other calves in comparison with calves reared in isolated housing or tied with chains. In this way, the occurrence of a particular disease tends to be higher in cases in which calves are bred in a collective manner, such as in compost barns.

Another risk factor for diarrhea occurrence that was associated with calves in the present study was the way that milk was offered to animals. Calves that received milk through automatic feeders were 2.01 times more likely to have diarrhea than animals receiving milk in buckets or bottles (P < 0.0001). This fact is directly related to the findings described in the previous paragraph, since automatic feeders are used only in calves on collective pens; in addition, such equipment is likely to be cleaned after several calves have used the same feeder compared with buckets or bottles.

Regarding Eimeria spp., Feitosa et al. (2008) detected samples of diarrhea from calves aged approximately 15 days and positive for Cryptosporidium spp., Rotavirus, and Eimeria spp. However, the results found in the present study allow us to infer that the diarrhea found in 15-day-old calves reported by Feitosa et al. (2008) did not occur due to the presence of Eimeria spp. In the present study, it was possible to find bovines 11 days old infected with Eimeria spp. However, analyzing the ROC curve made it possible to verify the association criteria for Eimeria spp. in calves > 37 days old. This period is significantly later than that reported by Feitosa et al. (2008) for Eimeria spp. and is later than that found in this work for Coronavirus (< 10 days), Rotavirus (> 8 days), Cryptosporidium (> 6 days), and diarrhea (< 21 days) occurrence in calves. A work published by Cruvinel et al. (2018b) reinforces the inference described above for Eimeria spp., since these investigators observed an outbreak of E. zuernii, exhibiting bloody diarrhea and animal mortality, in beef cattle with an average age of 45 days. It is important to emphasize that in this research, unlike the results found by Cruvinel et al. (2018b), it was not possible to verify a relationship between Eimeria spp. and diarrhea in animals. However, the most prevalent species found in the present study was E. bovis followed by E. alabamensis and E. zuernii.

Another risk factor that interfered with Eimeria spp. occurrence in calves was the source of animals’ water supplies. When water originated from rivers, streams, and ponds, bovines presented 7.931 times higher probability of being infected by Eimeria spp. compared with animals that received water from artesian wells (P < 0.0001). The previously mentioned research of consecutive outbreaks by E. zuernii in calves, published by Cruvinel et al. (2018b), also describes such similarity, indicating that water from rivers, streams, and ponds is more easily contaminated by feces/oocysts of Eimeria spp. than water from artesian wells. In addition, calves infected with Eimeria spp. were 18.62 times more likely to be infected with gastrointestinal nematodes (P < 0.0001). These results demonstrate the importance of conducting future studies to determine the true impact that these two parasites can trigger together in infected animals.

Based on previously discussed results obtained in the present study, it is possible to conclude that the prevalence of Coronavirus, Rotavirus, Cryptosporidium spp., Eimeria spp., and gastrointestinal nematodes is 7.20% (CI 95% 4.54–9.87), 6.37% (CI 95% 3.85–8.89), 51.52% (CI 95% 45.26–55.57), 3.46% (CI 95% 2.24–4.67), and 3.46% (CI 95% 2.24–4.67), respectively. The ages with the highest probabilities of occurrence of such diseases are < 10, > 8, > 6, > 37, and > 36 days old, respectively. There is a positive correlation (P ≤ 0.05) between Coronavirus, Rotavirus, Cryptosporidium spp., and diarrhea. In addition, diarrhea occurs more significantly (P < 0.0001) in bovines less than 21 days old and mainly in those that received milk through automatic feeders (P < 0.001). Cryptosporidium spp. and Rotavirus are risk factors for each other’s occurrence (P = 0.0039), and the former also presents a positive correlation with Coronavirus (P = 0.0089). In addition, calves bred in compost barns are more easily infected by Cryptosporidium spp. than those that are raised in isolated housing or tied to chains (P = 0.0273). Young bovines that drink water from rivers, streams, and ponds have a higher chance of being infected by Eimeria spp. (P < 0.0001) and gastrointestinal nematodes (P < 0.0001). The presence of diarrhea in calves was not significantly correlated (P > 0.05) with the presence of Eimeria spp. and gastrointestinal nematodes in animals.

The results found in this study highlight the importance of studying the agents of diarrhea together, once they act as coinfection where the losses triggered for the owners will involve some of these agents simultaneously.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

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References

  1. Alfieri AA, Leite JP, Alfieri AF, Jiang B, Glass RI, Gentsch JR. Detection of field isolates of human and animal group C rotavirus by reverse transcription-polymerase chain reaction anddigoxigenin-labeled oligonucleotide probes. Journal Virology Methods. 1999;83:35–43. doi: 10.1016/S0166-0934(99)00104-4. [DOI] [PubMed] [Google Scholar]
  2. Alfieri AA, Parazzi ME, Takiuchi E, Medici KC, Alfieri AF. Frequency of group A rotavirus in diarrhoeic calves in Brazilian cattle herds, 1998–2002. Tropical Animal Health Production. 2006;38:521–526. doi: 10.1007/s11250-006-4349-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Almeida PR, Lorenzetti F, Cruz RS, Watanabe TT, Zlotowiski P, Dremeier D. Diarrhea caused by rotavirus A, B, and C in suckling piglets from southern Brazil: molecular detection and histologic and immunohistochemical characterization. Journ of Veterinary Diagnostic Investigation. 2018;30:370–376. doi: 10.1177/1040638718756050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bok M, Alassia M, Frank F, Vega CG, Wigdorovitz A, Parreno V. Passive immunity to control Bovine coronavirus diarrhea in a dairy herd in Argentina. Revista Argentina de Microbiologia. 2018;50:23–20. doi: 10.1016/j.ram.2017.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bruhn FRP, Lopes MA, Demeu FA, Perazza CA, Pedrosa MF, Guimarães AM. Frequency of species of Eimeria in females of the holstein-friesian breed at the post-weaning stage during autumn and winter. Revista Brasileira de Parasitolologia Veterinaria. 2011;20:303–307. doi: 10.1590/S1984-29612011000400008. [DOI] [PubMed] [Google Scholar]
  6. Cardoso JMS, Silveira FL, Araujo AJUS, Carvalho JCC, Kanamura HY. Ocorrência de Cryptosporidium spp. em um rebanho bovino leiteiro no município de Caçapava, estado de São Paulo. Brasil, Revista Brasileira de Parasitologia Veterinária. 2008;12:239–242. [PubMed] [Google Scholar]
  7. Coura FM, Freitas MD, Ribeiro J, de Leme RA, de Souza C, Alfieri AA, Facury Filho EJ, de Carvalho AU, Silva MX, Lage AP, Heinemann MB. Longitudinal study of Salmonella spp., diarrheagenic Escherichia coli, Rotavirus, and Coronavirus isolated from healthy and diarrheic calves in a Brazilian dairy herd. Tropical Animal Health Production. 2015;47:3–11. doi: 10.1007/s11250-014-0675-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cruvinel LB, Bastos TSA, Nicaretta JE, Couto LFM, Borges DGL, Borges FA, Soares VE, Lopes WDZ. Surtos consecutivos ocasionado por Eimeria zuernii em bezerros de corte de uma propriedade do estado de São Paulo. Pesquisa Veterinária Brasileira. 2018;38:277–284. doi: 10.1590/1678-5150-pvb-5330. [DOI] [Google Scholar]
  9. Cruvinel LB, Nicaretta JE, Bastos TSA, Couto LFM, Santos JB, Zapa DMB, Cavalcantes ASA, Cruz BC, Borges DGL, Borges FA, Soares VE, Lopes WDZ. Eimeria species in dairy and beef cattle of different ages in Goiás state, Brazil. Revista Brasileira de Parasitologia Veterinária. 2018;27:169–176. doi: 10.1590/s1984-296120180038. [DOI] [PubMed] [Google Scholar]
  10. Daugschies A, Najdrowski M. Eimeriosis in cattle: current understanding. Journal Veterinarian Medicine Infectious Disease Veterinary and Public Health. 2005;52:417–427. doi: 10.1111/j.1439-0450.2005.00894.x. [DOI] [PubMed] [Google Scholar]
  11. Elliot A, Morgan UM, Thompson RC. Improved staining method for detecting Cryptosporidium oocysts in stools using malachite green. The Journal of General and Applied Microbiology. 1999;45:139–142. doi: 10.2323/jgam.45.139. [DOI] [PubMed] [Google Scholar]
  12. Feitosa FLF, Schimamura GM, Roberto T, Mendes LCN, Peiró JR, Feres FC, Bovino F, Perri SHV, Meireles MV. Importância de Cryptosporidium spp. como causa de diarréia em bezerros. Pesquisa Veterinária Brasileira. 2008;28:452–456. doi: 10.1590/S0100-736X2008001000002. [DOI] [Google Scholar]
  13. Felippelli G, Lopes WDZ, Cruz BC, Teixeira WFP, Maciel WG, Favero FC, Buzulini C, Sakamoto CAM, Soares VE, Gomes LVC, Oliveira GP, Costa AJ. Nematode resistance to ivermectin (630 and 700μg/kg) in cattle from the Southeast and South of Brazil. Parasitology International. 2014;63:835–840. doi: 10.1016/j.parint.2014.08.001. [DOI] [PubMed] [Google Scholar]
  14. Fritzen JTT, Oliveria V, Lorenzetti E, Miyabe FM, Viziaki MP, Rodrigues CA, Ayres H, Alfieri AF, Alfieri AA. Longitudinal surveillance of rotavirus A genotypes circulating in a high milk yield dairy cattle herd after the introduction of a rotavirus vaccine. Veterinary Microbiology. 2019;230:260–264. doi: 10.1016/j.vetmic.2019.02.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Galpasoro, L.U. and Fernandéz, S.P., 1998. Curva roc. Caderno Atenia Primaria. 5: 229-235.
  16. Garcia AM, Lima JD. Freqüência do Cryptosporidiumem bezerros lactentes de rebanhos leiteiros de Minas Gerais. Arquivo Brasileiro de Medicina Veterinária e Zootecnia. 1994;45:193–198. [Google Scholar]
  17. Gentsch JR, Glass RI, Woods P, Gouvea V, Gorziglia M, Flores J, Das BK, Bhan MK. Identification of group A rota-virus gene 4 types by polymerase chain reaction. Journal of Clinical Microbiology. 1992;30:1365–1373. doi: 10.1128/JCM.30.6.1365-1373.1992. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gordon HML, Whitlock HV. A new technique for counting nematode eggs in sheep feces. Journal Counsumition Science Industry Research. 1939;12:50–52. [Google Scholar]
  19. Gouvea V, Glass RI, Woods P, Taniguchi K, Clark HF, Forrester B, Fang ZY. Polymerase chain reaction amplification and typing of rotavirus nucleic acid fromstool specimens. Journal of Clinical Microbiology. 1990;28:276–282. doi: 10.1128/JCM.28.2.276-282.1990. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Gouvea V, Allen JR, Glass RI, Fang ZY, Bremont M, Cohen J, McCrae MA, Saif LJ, Sinarachatanant P, Caul EO. Detection of group B and C rotaviruses by polymerase chain reaction. Journal of Clinical Microbiology. 1991;29:519–523. doi: 10.1128/JCM.29.3.519-523.1991. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hayashi M, Murakami T, Fukuda FM, Tsuchiaka S, Kishimoto M, Sano K, Asano K, Ichimaru T, Haga K, Omatsu T, Katamyama Y, Oba M, Aoki H, Shirai J, Ishida M, Kazuhilo K, Mizutani T, Nagai M. Diversity in VP3, NSP3, and NSP4 of rotavirus B detected from Japanese cattle. Infection, Genetics and Evolution. 2017;49:107–113. doi: 10.1016/j.meegid.2017.01.003. [DOI] [PubMed] [Google Scholar]
  22. Herring AJ, Inglis NF, Ojeh CK, Snodgrass DR, Menzies JD. Rapid diagnosis of rotavirus infection by direct detection of viral nucleic acid in silver-stained polyacrylamide gels. Journal Clinical Microbiology. 1982;16:473–7. doi: 10.1128/JCM.16.3.473-477.1982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. IBGE. Brazilian Institute of Geographic and Statistics. (2017). Avaiable in: https://sidra.ibge.gov.br/pesquisa/ppm/quadros/brasil/2017 Acessed on 10 feb. 2017
  24. Jerez JA, Brandão PE, Buzinaro MG, Gregori F, Rosales CAR, Ito FH, Sakai T. Detecção derotavírus e coronavírus em fezes de bezerros neonatos com diarréia criados em vários municípios do estado de São Paulo, Brasil. Arquivo do Instituto Biológico. 2002;69:19–23. [Google Scholar]
  25. Langoni H, Linhares AC, Avila FA, Silva AV, Elias AO. Contribuição ao estudo da etiologia das diarréias em bezerros de aptidão leiteira no Estado de São Paulo, Brasil. Brazilian Journal of Veterinary Research and Animal Science. 2004;41:313–319. doi: 10.1590/S1413-95962004000500004. [DOI] [Google Scholar]
  26. Lucas AS, Swecker S, Lindsay DS, Scagli G, Neel JPS, Elvinger C, Zaja AM. A study of the level and dynamics of Eimeria populations innaturally infected, grazing beef cattle at various stages ofproduction in the Mid-Atlantic USA. Veterinary Parasitology. 2014;202:201–206. doi: 10.1016/j.vetpar.2014.02.053. [DOI] [PubMed] [Google Scholar]
  27. Malik YS, Sharma K, Vaid N, Chakravarti S, Chandrashekar KM, Basera SS. Frequency of group A rotavirus with mixed G and P genotypes in bovines: predominance of G3 genotype and its emergence in combination with G8/G10 types. Journal of Veterinary Science. 2012;3:271–278. doi: 10.4142/jvs.2012.13.3.271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Masatani T, Ferei RM, Otomaru K, Ishikawa S, Kojima I, Hobo S, Nishikawa Y. Seroprevalence of Cryptosporidium parvum and Neospora caninum in cattle in the southern Kyushu region of Japan. Parasitology International. 2018;63:763–767. doi: 10.1016/j.parint.2018.08.001. [DOI] [PubMed] [Google Scholar]
  29. Meireles MV. Cryptosporidium infection in Brazil: implications for veterinary medicine and public health. Revista Brasileira de Parasitologia Veterinária. 2010;19:197–204. doi: 10.1590/S1984-29612010000400002. [DOI] [PubMed] [Google Scholar]
  30. Meloni BP, Thompson RCA. Simplified methods for obtaining purified oocysts from mice and for growing Cryptosporidium parvum in vitro. Journal of Parasitology. 1996;82:757–762. doi: 10.2307/3283888. [DOI] [PubMed] [Google Scholar]
  31. Murakoshi F, Tozawa Y, Inomata A, Horimoto T, Wada Y, Kato K. Molecular characterization of Cryptosporidium isolates from calves in Ishikari Distinct. Hokkaido, Japan, Journal of Veterinary Medicine Science. 2013;75:837–840. doi: 10.1292/jvms.12-0435. [DOI] [PubMed] [Google Scholar]
  32. Passafaro TL, Carrera JPB, Santos LL, Silva FS, Santos DC, Cardoso EP, Leite RC, Toral FLB. Genetic analysis of resistance to ticks, gastrointestinal nematodes and Eimeria spp. in Nellore cattle. Veterinary Parasitology. 2015;210:224–234. doi: 10.1016/j.vetpar.2015.03.017. [DOI] [PubMed] [Google Scholar]
  33. Pereira HG, Azeredo RS, Leite JP, Candeias JA, Racz ML, Linhares AC. Electrophoretic study of the genome of human rotaviruses from Rio de Janeiro, Sao Paulo and Para, Brazil. The Journal of Hygiene. 1983;90:117–25. doi: 10.1017/S0022172400063919. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Silva FDF, Gregori F, Gonçalves ACS, Samara SI, Buzinaro MG. Molecular characterization of group A bovine rotavirus in southeastern and central-western Brazil, 2009-2010. Pesquisa Veterinaria Brasileira. 2012;32:237–42. doi: 10.1590/S0100-736X2012000300010. [DOI] [Google Scholar]
  35. Souza AP, Ramos CI, Bellato V, Sartor AA, Schelbauer CA. Resistência de helmintos gastrintestinais de bovinos a anti-helmínticos no Planalto Catarinense. Ciência Rural. 2008;38:1363–1367. doi: 10.1590/S0103-84782008000500026. [DOI] [Google Scholar]
  36. Takiuchi E, Stipp DT, Alfieri AF, Alfieri AA. Improved detection of bovine coronavirus N gene in faeces of calves infected naturally by a semi-nested PCR assay anda internal control. Journal of Virology Methods. 2006;131:148–154. doi: 10.1016/j.jviromet.2005.08.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Taubert A, Hermosilla C, Suhwold A, Zahner H. Antigen-induced cytokine production in lymphocytes of Eimeria bovis primary and challenge infected calves. Veterinary Immunology and Immunopathology. 2008;126:309–320. doi: 10.1016/j.vetimm.2008.09.003. [DOI] [PubMed] [Google Scholar]
  38. Thomson S, Hamilton CA, Hope JC, Katzer F, Mabbott NA, Morrison LJ, Innes EA. Bovine cryptosporidiosis: impact, host-parasite interaction and control strategies. Veterinary Research. 2017;48:42. doi: 10.1186/s13567-017-0447-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Tsunemitsu H, Smith DR, Saif LJ. Experimental inoculation of adult dairy cows with bovine coronavirus and detection of coronavirus in feces by RT-PCR. Archives of Virology. 1999;144:167–175. doi: 10.1007/s007050050493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Ueno, H. and Gonçalves, P.C., 1998. Manual para diagnóstico das helmintoses de ruminantes. 4 ed. (Japan International Cooperation Agency).
  41. De Waele V., Speybroeck N., Berkvens D., Mulcahy G., Murphy T.M. Control of cryptosporidiosis in neonatal calves: Use of halofuginone lactate in two different calf rearing systems. Preventive Veterinary Medicine. 2010;96(3-4):143–151. doi: 10.1016/j.prevetmed.2010.06.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. WHO. World Health Organization. EPI INFO. Database and statistics software for public heath professonals. Centers for Disease Control and Prevention (CDC) Version 7.1.5.2; 2015. https://search.cdc.gov/search/?query=regression+logistic&utf8=%E2%9C%93&affiliate=cdc-main

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