Skip to main content
Download PDF

Comparison of Mini-FLOTAC, Flukefinder® and sedimentation techniques for detection and quantification of Fasciola hepatica and Calicophoron daubneyi eggs using spiked and naturally infected bovine faecal samples

Parasites & Vectors volume 16, Article number: 260 (2023) Cite this article

This article has been updated

Abstract

Background

Fasciolosis (Fasciola hepatica) and paramphistomosis (Calicophoron daubneyi) are two important infections of livestock. Calicophoron daubneyi is the predominant Paramphistomidae species in Europe, and its prevalence has increased in the last 10–15 years. In Italy, evidence suggests that the prevalence of F. hepatica in ruminants is low in the southern part, but C. daubneyi has been recently reported at high prevalence in the same area. Given the importance of reliable tools for liver and rumen fluke diagnosis in ruminants, this study evaluated the diagnostic performance of the Mini-FLOTAC (MF), Flukefinder® (FF) and sedimentation (SED) techniques to detect and quantify F. hepatica and C. daubneyi eggs using spiked and naturally infected cattle faecal samples.

Methods

Briefly, negative bovine faecal samples were artificially spiked with either F. hepatica or C. daubneyi eggs to achieve different egg count levels: 10, 50 and 100 eggs per gram (EPG) of faeces. Moreover, ten naturally infected cattle farms from southern Italy with either F. hepatica and/or C. daubneyi were selected. For each farm, the samples were analysed individually only with MF technique and as pools using MF, FF and SED techniques. Bayesian latent class analysis (LCA) was used to estimate sensitivity and accuracy of the predicted intensity of infection as well as the infection rate in the naturally infected farms.

Results

The outcome of this study showed that the highest number of eggs (F. hepatica and C. daubneyi) recovered was obtained with MF, followed by FF and SED in spiked infected samples at 50 and 100 EPG, while at lower infection levels of 10 EPG, FF gave the best results. Moreover, the sensitivity for all the techniques included in the study was estimated at > 90% at infection levels > 20 EPG for both F. hepatica and C. daubneyi eggs. However, MF was the most accurate of the three techniques evaluated to estimate fluke infection intensity. Nevertheless, all three techniques can potentially estimate infection rate at farm level accurately.

Conclusions

Optimization and standardization of techniques are needed to improve the FEC of fluke eggs.

Graphical Abstract

Background

Among the parasitic helminths that freshwater snails (e.g. Galba truncatula) can transmit, Fasciola hepatica (liver fluke) and Calicophoron daubneyi (rumen fluke) are wide distributed in temperate countries. Fasciolosis is highly endemic mainly in Western Europe [1,2,3,4], causing losses of production with significant costs at > $3 billion per year for the global livestock farming business [5].

Paramphistomosis is considered an emerging parasitic disease of ruminants in Europe [6]. Over the last 20 years, its incidence and prevalence have increased significantly in Europe and many outbreaks of clinical manifestation have been reported in different countries mainly induced by C. daubneyi, the predominant Paramphistomidae species in Europe [7,8,9,10,11]. Clinical paramphistomosis is mostly caused by a large burden of juvenile flukes in the small intestine of the infected host, as adult parasites, generally located in rumen and reticulum, are quite well tolerated [12].

In Italy the prevalence of F. hepatica in ruminants appears to be low (0.7–6.0% in sheep and 0.9–7.8% in cattle); however C. daubneyi has been recently reported at high prevalence (4.5–51.1% in sheep and 9.6–60.9% in cattle) in the same area [13,14,15].

Diagnosis is very important in performing the most effective programmes for fasciolosis and paramphistomosis control [16].

The results of a survey by Hoyle et al. [17] revealed confusion amongst sheep and/or cattle farmers over the diagnosis and control of flukes, highlighting the need to provide best practice advice. Diagnosis and monitoring of fasciolosis and paramphistomosis in ruminants are challenging. Usually, liver fluke presence information on a farm occurs from liver condemnation reports [18], which are based on visual inspections at abattoirs, but these procedures are variable and potentially erroneous, because they are not standardized [19]. Several tests for ante-mortem diagnosis of flukes exist, but none can be considered sufficiently sensitive and specific for use in the field [20] as ‘pen-side test’. Coproantigen-based techniques are promising tools in fluke diagnosis; they showed 100% sensitive to detect experimental ovine fasciolosis and give information about correlation between fluke burden and coproantigen amounts [21]. However, ELISA-based techniques cannot be used directly on farm and have not been validated extensively under field conditions to confirm the sensitivity obtained in experimental infections [22].

Fluke faecal egg counts (flukeFECs) are, instead, simple and rapid [19] and can also be performed directly on farm, because neither specialist sampling techniques nor sophisticated laboratory equipment is required. FlukeFECs are routinely used in veterinary parasitic diagnosis, with almost 100% specificity, although they are able to detect only patent infections [20]. There is no gold-standard diagnostic tool for fluke infection so often a combination of clinical signs, grazing history, serological, coproantigen and flukeFECs and/or abattoir reports are used to confirm fluke infections. Several variations of flukeFECs have been developed, using simple sedimentation (SED) [23], sedimentation combined with flotation [23], sedimentation with fine filtration [24], Flukefinder® technique (FF) [25] or flotation with (Mini-) FLOTAC techniques [1, 26]. Given the importance of reliable tools for liver and rumen fluke diagnosis in ruminants, this study aimed to evaluate the diagnostic performance of the MF, FF and SED techniques to detect and quantify F. hepatica and C. daubneyi eggs using spiked and naturally infected cattle faecal samples.

Methods

Egg-spiked faecal samples

Negative bovine faecal samples were artificially spiked with either F. hepatica or C. daubneyi eggs to achieve different egg count levels of 10, 50 or 100 eggs per gram (EPG) of faeces. The F. hepatica and C. daubneyi positive and negative faecal samples were collected from adult cattle (> 24 months old) in three farms located in the Campania region (southern Italy). Naturally infected positive samples by one of the two flukes were collected from grazing cattle, whilst negative samples were collected from housed dairy cattle without pasture access.

To identify the positive samples for potential egg extraction, as well as the negative samples to be experimentally infected, each sample was analysed in five replicates by the FLOTAC basic technique (sensitivity = 94% and specificity = 98%) with a detection limit of 1 EPG of faeces using zinc sulphate flotation solution (specific gravity = 1.35) [14, 27]. The positive cattle were used as donors for the extraction of F. hepatica and C. daubneyi eggs from faeces, using the egg recovery technique described by Bosco et al. [28] with some modifications. Briefly, four sieves of different mesh size (1 mm, 250 μm, 212 μm and 63 μm) were employed to separate the fluke eggs from the faeces. The 63-μm sieve was washed with tap water to recover eggs and sedimented in a conical beaker for 4 min. The supernatant was eliminated, and the sediment obtained was a purified suspension of eggs. The extraction method was used separately for the two flukes to obtain mono-infected samples. The purified eggs obtained for each fluke were suspended in distilled water to determine their concentration by calculating the arithmetic mean of egg counts in ten aliquots of 10 μl each. Three concentrations of 10, 50 and 100 EPG were prepared, adding appropriate egg suspensions to three negative faecal samples (helminth free) and homogenizing them. Six replicates of each sample were analysed using the three methods (MF, FF and SED) (Fig. 1).

Fig. 1
figure 1

Number of egg-spiked and naturally infected (individually or pooled) faecal samples analysed using Mini-FLOTAC (MF), Flukefinder® (FF) and sedimentation (SED) techniques for detection and quantification of Fasciola hepatica and Calicophoron daubneyi eggs

Full size image

Naturally infected faecal samples

Ten farms with fluke positive cattle (infections with C. daubneyi and/or F. hepatica) in southern Italy were selected. These farms were identified from the diagnostic activity of the Regional Center for Monitoring Parasitic infections (CREMOPAR, Campania region, southern Italy).

In each farm, individual faecal samples were collected directly from the rectum of 20 adult cattle (> 12 months). For each farm, the samples were analysed individually only with MF technique and as pools using also FF and SED techniques. The four pools of faeces (each consisting of 5 samples) were performed using the protocol described by Rinaldi et al. [29] (Fig. 1).

The three copromicroscopic methods were performed for both the spiked and the naturally infected samples following the manufacturer’s instructions. The faecal egg counts (FECs; expressed in EPG) were obtained using a multiplication factor of 5 for MF (0.2 g of faeces examined = 2 ml of faecal suspension which contains 5 g in a total volume 50 ml), 0.5 for FF (2 g of faeces examined) and 0.1 for SED (10 g of faeces examined) (Table 1).

Table 1 Schematic features of Mini-FLOTAC (MF), Flukefinder® (FF) and sedimentation (SED) techniques included in the study
Full size table

Statistical analyses

The sensitivity of the three techniques as well as the accuracy in predicting the intensity of infection was estimated from the egg-spiked faecal samples. First, the percentage recovery of fluke eggs was calculated to assess the accuracy of FEC for each technique at each level of egg count, using the following formula: % egg recovery = 100 – (true FEC – observed FEC)/true FEC × 100 [28]. Moreover, a simple model was developed to estimate the overdispersion of the eggs counted with the different techniques, assuming measurement error is distributed according to a negative binomial (such as in Prada et al. [30]), which is a more flexible assumption than using a Poisson, like in Atljia et al. [31]. As the size of the faecal sample examined (in grams) is different across the three techniques, the dosage in EPG needs to be transformed to the number of eggs expected in the faecal sample (Table 1). The model is run in a Bayesian framework using a Gibbs sampling package in R [32], “jags” [33] and “runjags” [34], with a burn in of 1000 (discarded runs), drawing a total of 10,000 samples with a thinning of 10.

Preliminary simulations (not shown) suggested that overdispersion was the same across the two parasite species examined; thus, a single parameter was used for each diagnostic technique. Using the posterior distributions of overdispersion obtained from the model above, we simulated 1000 repeated measurements across a range of true intensity of infection measured in EPG from 1 to 100 (at 0.5 EPG steps, 199,000 total samples) for the three diagnostics. We then calculated the sensitivity (proportion of samples correctly identified as positive) and accuracy in estimating intensity of infection by comparing the true intensity of infection to those estimated through the different diagnostics.

To estimate the infection rate in the farms naturally infected, we developed a Bayesian latent class analysis (LCA) model, following recent work in other parasites [30]. The infection status of each individual animal is estimated from the different diagnostics (both individual-level MF and their contribution to the pooled samples). Each infected individual will have their intensity of infection (true egg count) drawn from a gamma distribution. The parameters for the gamma distribution for C. daubneyi are estimated by the model; however, for F. hepatica, due to the low number of positive samples, it could not be estimated from these data, so it was calculated from the data reported in Rinaldi et al. [1]. The number of eggs in each pool is assumed to be the average of the true number of eggs across the five individuals contributing to that pool. The data from the different diagnostics are assumed to be generated from a negative binomial draw of the true number of eggs (in the individual or the pool). The overdispersion value needed was drawn from the posterior distributions generated from the model above for the egg-spiked faecal samples. Farm-level infection rate can then be estimated, and the expected farm infection rate with FF and SED, which were not collected at the individual level, can be simulated. As before, we used the “jags” [33] and “runjags” [34] packages to run the model; we again discarded 1000 runs (burn in), drawing a total of 10,000 samples without thinning. All code is available at: https://github.com/joaquinprada/Fluke-MF-FF-SED-Comparison.

Results

Table 2 reports the outcome of the EPG levels of F. hepatica and C. daubneyi using egg-spiked cattle faecal samples at different known EPG concentrations (10, 50, 100). The results were expressed as mean EPG of the six replicates comparing the performances of three different techniques included in the study (MF, FF and SED). The egg-spiking test revealed that all the methods were able to recover eggs of F. hepatica and C. daubneyi from cattle faeces, with MF having the lowest sensitivity below 15 EPG, and SED having the lowest above 15 EPG (Fig. 2). Nevertheless, all three techniques had a sensitivity > 90% at infection levels above 20 EPG. Moreover, when comparing true and estimated intensity of infection (in EPG) with the different diagnostics, MF was the technique that provided the better estimation, in terms of both accuracy (median estimated EPG) and precision (size of the 95% credible interval) (Fig. 3).

Table 2 Mean number of detection and percentage recovery of Fasciola hepatica (FH) and Calicophoron daubneyi (CD) eggs at the different egg count levels using spiked infected cattle faecal samples performed with Mini-FLOTAC (MF), Flukefinder® (FF) and sedimentation (SED) techniques included in the study
Full size table
Fig. 2
figure 2

Estimated sensitivity of the three diagnostic techniques, Mini-FLOTAC (MF), Flukefinder® (FF) and sedimentation (SED), across a range of infection intensities, measured in eggs per gram (EPG) of faeces

Full size image
Fig. 3
figure 3

Comparison between true and estimated infection intensity in eggs per gram (EPG) of faeces. Dashed diagonal black line indicates where true and estimated EPGs are equal. The median estimated infection intensity is represented by the solid lines, while the shaded area shows the 95% credible interval, across the three diagnostic techniques, Mini-FLOTAC (MF), Flukefinder® (FF) and sedimentation (SED)

Full size image

When evaluating natural infection across ten farms in southern Italy, while all farms appear infected with C. daubneyi at a high infection rate, four appear to be negative for F. hepatica or at a low infection rate (Fig. 4). Overall, the infection rate estimated by the individual-level MF technique is very similar to the model estimated infection rate, albeit potentially underestimating infection rate when infection is low (Fig. 4—left side). Simulated individual-level FF and SED diagnostic results also yield comparable infection rate values across all farms for both parasite species (Fig. 4—dark red and dark green). The mean EPGs across the pooled samples were higher for MF and FF compared to SED (Table 3).

Fig. 4
figure 4

Infection rate of Fasciola hepatica (left) and Calicophoron daubneyi (right) across ten farms in Southern Italy. Infection rate by Mini-FLOTAC was calculated from the individual-level samples (purple), while median infection rate by Flukefinder® (dark red) and sedimentation (dark green) was estimated from the simulated individual-level data. The model estimate, considering all three diagnostic techniques, and both individual-level and pooled samples, is shown in black. Error bars show the 95% credible interval

Full size image
Table 3 Eggs per gram (EPG) of faeces mean of Fasciola hepatica (FH) and Calicophoron daubneyi (CD) using naturally infected bovine faecal samples from ten farms analysed individually (no. 20 samples for each farm) only with Mini-FLOTAC (MF) technique and as pooled faecal samples (no. 4 pools samples for each farm) using also Flukefinder® (FF) and sedimentation (SED) techniques included in the study
Full size table

Discussion

In this study MF was compared for the first time to our knowledge with FF and SED for detection of liver and rumen flukes in spiked and naturally infected cattle faecal samples. The MF was already successfully used by Malrait et al. [26] to assess the presence of rumen fluke infections and for FEC of C. daubneyi eggs, showing that it was a reliable method, with sensitivity and specificity of 94% and 98%, respectively. In our study, the MF technique permitted the recovery of the highest number of eggs, followed by FF and SED for spiked infected samples at 50 and 100 EPG, while lower infection level of 10 EPG FF gave the best results. These abovementioned findings were similar for both flukes. In particular, the highest percentages of recovery were obtained with MF at 100 EPG, but the retrieved values—64.2% for liver flukes and 70.8% for rumen fluke—were lower than those reported in previous studies for gastrointestinal nematodes (GIN) in cattle (98.1% by Amadesi et al. [35]; 70.9% by Paras et al. [36]), sheep (100% by Bosco et al. [28] and Godber et al. [37]) and horses (74.2% by Napravnikova et al. [38]), but higher than equine strongyle eggs reported by Noel et al. [39] (42.6%). This difference can be explained by the fact that fluke eggs are heavy, while GINs of ruminants and horses are light eggs; therefore, the behaviour in flotation solutions and the recovery rate are completely different [27, 28, 35, 40]. However, the SED was the least efficient technique, recovering the lower rate of eggs. Our results agree with a previous study on comparison between FF and SED techniques showing that FF was more efficient than simple Becker sedimentation to retrieve F. hepatica eggs in sheep and cattle faeces; in fact, it was able to detect 2 EPG with a higher sensitivity (100%) already at low levels of infection [19]. Although the sedimentation methods (i.e. simple, with fine filtration, followed by flotation) are the most used for fluke infection detection, their sensitivity is low [16, 19]. The estimated sensitivity for all the techniques included in the study was > 90% when the intensity of infection was > 20 EPG (Fig. 2). However, at very low intensities of infection, sensitivity was estimated to drop, particularly for MF, which has a detection limit of 5 EPG. The findings obtained with MF agree with Zarate-Rendon et al. [41], showing that MF and FF gave better results than Kato-Katz for FEC of F. hepatica also in humans, with a sensitivity of 100% at 96 EPG level, but the sensitivity decreased at 40% for MF and at 60% for FF at level of 14 EPG. In our previous studies in which MF and FLOTAC were compared for the detection of fluke eggs, the outcome revealed that FLOTAC had a higher sensitivity than MF at low levels of infection. The step of centrifugation in the FLOTAC technique, which is missing in the MF method, helped to increase the number of fluke eggs detected [40, 42].

On the other hand, MF is the most accurate of the three techniques to estimate the intensity of infection, which aligns with previous work on GIN in ruminants and horses [28, 35, 43]. FF and SED however can over- and under- estimate intensity of infection more, with these results consistent across the whole infection intensity range evaluated (from 1 to 100 EPG). In other studies [35, 44, 45], the accuracy improves when the EPG in faecal sample increases. However, it is very important to use diagnostic tools with a low detection limit and high accuracy also at low levels of infection, because also the presence of only few flukes (especially for F. hepatica and sheep, which are highly sensitive to this parasite) can cause significantly reduced productivity [46, 47].

Findings obtained by MF from naturally infected cattle showed that individual and pooled samples did not give statistically different results for all ten farms analysed for both flukes, as previously shown also for GINs in ruminants [29, 42, 48,49,50]. Moreover, the estimated prevalence with the model, which includes the information across the three techniques, aligns with the infection rate measured with MF and the infection rate estimated from the simulated FF and SED techniques. All farms showed presence of C. daubneyi at very high infection rate, while F. hepatica was more moderate in most farms, four of which had no positive samples recovered, which would suggest no infection (or at a relatively low infection rate). Due to the low number of F. hepatica eggs detected, further studies are needed, as it was not possible to assess the intensity of infection in the region.

Conclusion

Optimization and standardization of techniques are needed to improve the FEC of fluke eggs. The combination of sensitive, accurate, precise and standardized FEC techniques with a reliable automated system will permit not only an efficient observation, but also a quantification of parasitic elements thanks to the use of an artificial intelligence software [51, 52]. MF is an accurate technique to estimate intensity of infection at moderate to high levels and can be recommended in endemic areas, for example for diagnosing C. daubneyi in southern Italy.

Finally, further studies could be important to support informed treatment decisions as well as to determine the threshold for ‘economically relevant’ infection levels and to evaluate the diagnostic performance of the different tests around this threshold.

Availability of data and materials

All data generated or analysed during this study are included in this published article. The datasets used and/or analysed during the present study available from the corresponding author upon reasonable request.

Change history

  • 16 August 2023

    This article has been updated following publication so that each instance of ‘Flukefinder®’ is marked as a registered trademark.

Abbreviations

MF:

Mini-FLOTAC

FF:

Flukefinder®

SED:

Sedimentation

EPG:

Eggs per gram

FEC:

Faecal egg count

References

  1. Rinaldi L, Biggeri A, Musella V, De Waal T, Hertzberg H, Mavrot F, et al. Sheep and F. hepatica in Europe: the GLOWORM experience. Geospat Health. 2015;9:309–17.

    Article  PubMed  Google Scholar 

  2. Mas-Coma S, Valero MA, Bargues MD. Fascioliasis. Adv Exp Med Biol. 2019;1154:71–103.

    Article  PubMed  Google Scholar 

  3. Munita MP, Rea R, Martinez-Ibeas AM, Byrne N, McGrath G, Munita-Corbalan LE, et al. Liver fluke in Irish sheep: prevalence and associations with management practices and co-infection with rumen fluke. Parasit Vectors. 2019;12:525.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Mas-Coma S, Valero MA, Bargues MD. Human and animal fascioliasis: origins and worldwide evolving scenario. Clin Microbiol Rev. 2022;35:e0008819.

    Article  PubMed  Google Scholar 

  5. Beesley NJ, Caminade C, Charlier J, Flynn RJ, Hodgkinson JE, Martinez-Moreno A, et al. Fasciola and fasciolosis in ruminants in Europe: identifying research needs. Transbound Emerg Dis. 2018;65:199–216.

    Article  PubMed  Google Scholar 

  6. Huson KM, Oliver NAM, Robinson MW. Paramphistomosis of ruminants: an emerging parasitic disease in Europe. Trends Parasitol. 2017;33:836–44.

    Article  PubMed  Google Scholar 

  7. Cringoli G, Taddei R, Rinaldi L, Veneziano V, Musella V, Cascone C, et al. Use of remote sensing and geographical information systems to identify environmental features that influence the distribution of paramphistomosis in sheep from the southern Italian Apennines. Vet Parasitol. 2004;122:15–26.

    Article  CAS  PubMed  Google Scholar 

  8. Rinaldi L, Perugini AG, Capuano F, Fenizia D, Musella V, Veneziano V, et al. Characterization of the second internal transcribed spacer of ribosomal DNA of Calicophoron daubneyi from various hosts and locations in southern Italy. Vet Parasitol. 2005;131:247–53.

    Article  CAS  PubMed  Google Scholar 

  9. Ploeger HW, Ankum L, Moll L, van Doorn DCK, Mitchell G, Skuce PJ, et al. Presence and species identity of rumen flukes in cattle and sheep in the Netherlands. Vet Parasitol. 2017;243:42–6.

    Article  CAS  PubMed  Google Scholar 

  10. Fenemore C, Floyd T, Mitchell S. Rumen Fluke in Great Britain. J Comp Pathol. 2021;184:31–6.

    Article  CAS  PubMed  Google Scholar 

  11. Červená B, Anettová L, Nosková E, Pafčo B, Pšenková I, Javorská K, et al. The winner takes it all: dominance of Calicophoron daubneyi (Digenea: Paramphistomidae) among flukes in Central European beef cattle. Parasitology. 2022;1–10.

  12. O’Shaughnessy J, Garcia-Campos A, McAloon CG, Fagan S, de Waal T, McElroy M, et al. Epidemiological investigation of a severe rumen fluke outbreak on an Irish dairy farm. Parasitology. 2018;145:948–52.

    Article  CAS  PubMed  Google Scholar 

  13. Bosco A, Nocerino M, Santaniello M, Maurelli MP, Cringoli G, Rinaldi L. Mapping the Spatial Distribution of the Rumen Fluke Calicophoron daubneyi in a Mediterranean Area. Pathogens. 2021;10:1122.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Maurizio A, Perrucci S, Tamponi C, Scala A, Cassini R, Rinaldi L, et al. Control of gastrointestinal helminths in small ruminants to prevent anthelmintic resistance: the Italian experience. Parasitology. 2023; 1–14.

  15. Sanna G, Varcasia A, Serra S, Salis F, Sanabria R, Pipia AP, et al. Calicophoron daubneyi in sheep and cattle of Sardinia, Italy. Helminthologia. 2016;53:87–93.

    Article  Google Scholar 

  16. Sabatini GA, de Almeida BF, Claerebout E, Gianechini LS, Höglund J, Kaplan RM, et al. Practical guide to the diagnostics of ruminant gastrointestinal nematodes, liver fluke and lungworm infection: interpretation and usability of results. Parasit Vectors. 2023;16:58.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Hoyle RC, Rose Vineer H, Duncan JS, Williams DJL, Hodgkinson JE. A survey of sheep and/or cattle farmers in the UK shows confusion over the diagnosis and control of rumen fluke and liver fluke. Vet Parasitol. 2022;312:109812.

    Article  PubMed  Google Scholar 

  18. Hanley J, Garcia-Ara A, Wapenaar W. Cattle and sheep farmers’ opinions on the provision and use of abattoir rejection data in the United Kingdom. Vet Rec. 2020;186:217.

    Article  PubMed  Google Scholar 

  19. Reigate C, Williams HW, Denwood MJ, Morphew RM, Thomas ER, Brophy PM. Evaluation of two F. hepatica faecal egg counting protocols in sheep and cattle. Vet Parasitol. 2021;294:109435.

    Article  PubMed  Google Scholar 

  20. Mazeri S, Sargison N, Kelly RF, Bronsvoort BM, Handel I. Evaluation of the performance of five diagnostic tests for F. hepatica infection in naturally infected cattle using a bayesian no gold standard approach. PLoS ONE. 2016;11:0161621.

    Article  Google Scholar 

  21. Brockwell YM, Spithill TW, Anderson GR, Grillo V, Sangster NC. Comparative kinetics of serological and coproantigen ELISA and faecal egg count in cattle experimentally infected with Fasciola hepatica and following treatment with triclabendazole. Vet Parasitol. 2013;196:417–26.

    Article  CAS  PubMed  Google Scholar 

  22. Calvani NED, George SD, Windsor PA, Bush RD, Šlapeta J. Comparison of early detection of F. hepatica in experimentally infected Merino sheep by real-time PCR, coproantigen ELISA and sedimentation. Vet Parasitol. 2018;251:85–9.

    Article  CAS  PubMed  Google Scholar 

  23. Becker AC, Kraemer A, Epe C, Strube C. Sensitivity and efficiency of selected coproscopical methods-sedimentation, combined zinc sulfate sedimentation-flotation, and McMaster method. Parasitol Res. 2016;115:2581–7.

    Article  PubMed  Google Scholar 

  24. Arifin MI, Höglund J, Novobilský A. Comparison of molecular and conventional methods for the diagnosis of Fasciola hepatica infection in the field. Vet Parasitol. 2016;232:8–11.

    Article  CAS  PubMed  Google Scholar 

  25. Dixon R, Wescott R. A fast and accurate fecal examination technique for diagnosis of F. hepatica. Presented at the annual meeting of the american association of veterinary parasitologists. 1987; 28.

  26. Malrait K, Verschave S, Skuce P, Van Loo H, Vercruysse J, Charlier J. Novel insights into the pathogenic importance, diagnosis and treatment of the rumen fluke (Calicophoron daubneyi) in cattle. Vet Parasitol. 2015;207:134–9.

    Article  PubMed  Google Scholar 

  27. Cringoli G, Rinaldi L, Maurelli MP, Utzinger J. FLOTAC: new multivalent techniques for qualitative and quantitative copromicroscopic diagnosis of parasites in animals and humans. Nat Protoc. 2010;5:503–15.

    Article  CAS  PubMed  Google Scholar 

  28. Bosco A, Maurelli MP, Ianniello D, Morgoglione ME, Amadesi A, Coles GC, et al. The recovery of added nematode eggs from horse and sheep faeces by three methods. BMC Vet Res. 2018;14:7.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Rinaldi L, Amadesi A, Dufourd E, Bosco A, Gadanho M, Lehebel A, et al. Rapid assessment of faecal egg count and faecal egg count reduction through composite sampling in cattle. Parasit Vectors. 2019;12:353.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Prada JM, Touloupou P, Adriko M, Tukahebwa EM, Lamberton PHL, Hollingsworth TD. Understanding the relationship between egg- and antigen-based diagnostics of Schistosoma mansoni infection pre- and post-treatment in Uganda. Parasit Vectors. 2018;11:21.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Atlija M, Prada JM, Gutiérrez-Gil B, Rojo-Vázquez FA, Stear MJ, Arranz JJ, et al. Implementation of an extended ZINB model in the study of low levels of natural gastrointestinal nematode infections in adult sheep. BMC Vet Res. 2016;12:97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. R Core Team 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2021. https://www.R-project.org/.

  33. Plummer M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), Vienna, 20–22 March 2003; 1–10.

  34. Denwood MJ. Runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. J Stat Softw. 2016;71:1–25.

    Article  Google Scholar 

  35. Amadesi A, Bosco A, Rinaldi L, Cringoli G, Claerebout E, Maurelli MP. Cattle gastrointestinal nematode egg-spiked faecal samples: high recovery rates using the Mini-FLOTAC technique. Parasit Vectors. 2020;13:230.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Paras KL, George MM, Vidyashankar AN, Kaplan RM. Comparison of fecal egg counting methods in four livestock species. Vet Parasitol. 2018;257:21–7.

    Article  PubMed  Google Scholar 

  37. Godber OF, Phythian CJ, Bosco A, Ianniello D, Coles G, Rinaldi L, et al. A comparison of the FECPAK and Mini-FLOTAC faecal egg counting techniques. Vet Parasitol. 2015;207:342–5.

    Article  PubMed  Google Scholar 

  38. Nápravníková J, Petrtýl M, Stupka R, Vadlejch J. Reliability of three common fecal egg counting techniques for detecting strongylid and ascarid infections in horses. Vet Parasitol. 2019;272:53–7.

    Article  PubMed  Google Scholar 

  39. Noel ML, Scare JA, Bellaw JL, Nielsen MK. Accuracy and precision of mini-FLOTAC and McMaster techniques for determining equine strongyle egg counts. J Equine Vet Sci. 2017;48:182–7.

    Article  Google Scholar 

  40. Cringoli G, Maurelli MP, Levecke B, Bosco A, Vercruysse J, Utzinger J, et al. The Mini-FLOTAC technique for the diagnosis of helminth and protozoan infections in humans and animals. Nat Protoc. 2017;12:1723–32.

    Article  CAS  PubMed  Google Scholar 

  41. Zárate-Rendón DA, Vlaminck J, Levecke B, Briones-Montero A, Geldhof P. Comparison of Kato-Katz Thick Smear, Mini-FLOTAC, and Flukefinder® for the detection and quantification of Fasciola hepatica eggs in artificially spiked human stool. Am J Trop Med Hyg. 2019;101:59–61.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Maurelli MP, Dourado Martins OM, Morgan ER, Charlier J, Cringoli G, Letra Mateus T, et al. A qualitative market analysis applied to mini-FLOTAC and Fill-FLOTAC for diagnosis of helminth infections in ruminants. Front Vet Sci. 2020;7:580649.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Rinaldi L, Krücken J, Martinez-Valladares M, Pepe P, Maurelli MP, de Queiroz C, et al. Advances in diagnosis of gastrointestinal nematodes in livestock and companion animals. Adv Parasitol. 2022;118:85–176.

    Article  PubMed  Google Scholar 

  44. Mes TH. Technical variability and required sample size of helminth egg isolation procedures. Vet Parasitol. 2003;115:311–20.

    Article  PubMed  Google Scholar 

  45. Das G, Savas T, Kaufmanna F, Idris A, Abela H, Gaulya M. Precision, repeatability and representative ability of faecal egg counts in Heterakis gallinarum infected chickens. Vet Parasitol. 2011;183:87–94.

    Article  PubMed  Google Scholar 

  46. Mazeri S, Rydevik G, Handel I, de Bronsvoort CBM, Sargison N. Estimation of the impact of Fasciola hepatica infection on time taken for UK beef cattle to reach slaughter weight. Sci Rep. 2017;7:7319.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Sabatini GA, de Almeida BF, Claerebout E, Sicalo Gianechini L, Hoglund J, Kaplan RM, et al. Practical guide to the diagnostics of ruminant gastrointestinal nematodes, liver fluke and lungworm infection: interpretation and usability of results. Parasit Vectors. 2023;16:58.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Rinaldi L, Levecke B, Bosco A, Ianniello D, Pepe P, Charlier J, et al. Comparison of individual and pooled faecal samples in sheep for the assessment of gastrointestinal strongyle infection intensity and anthelmintic drug efficacy using McMaster and Mini-FLOTAC. Vet Parasitol. 2014;205:216–23.

    Article  CAS  PubMed  Google Scholar 

  49. Kenyon F, Rinaldi L, McBean D, Pepe P, Bosco A, Melville L, et al. Pooling sheep faecal samples for the assessment of anthelmintic drug efficacy using McMaster and Mini-FLOTAC in gastrointestinal strongyle and Nematodirus infection. Vet Parasitol. 2016;225:53–60.

    Article  CAS  PubMed  Google Scholar 

  50. George MM, Paras KL, Howell SB, Kaplan RM. Utilization of composite fecal samples for detection of anthelmintic resistance in gastrointestinal nematodes of cattle. Vet Parasitol. 2017;240:24–9.

    Article  PubMed  Google Scholar 

  51. Alva A, Cangalaya C, Quiliano M, Krebs C, Gilman RH, Sheen P, et al. Mathematical algorithm for the automatic recognition of intestinal parasites. PLoS ONE. 2017;12:e0175646.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Cringoli G, Amadesi A, Maurelli MP, Celano B, Piantadosi G, Bosco A, et al. The Kubic FLOTAC microscope (KFM): a new compact digital microscope for helminth egg counts. Parasitology. 2021;148:427–34.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This article is based upon work from the project "A Cross-Disciplinary Alliance to Identify, PREdict and prePARE for Emerging Vector-Borne Diseases—PREPARE4VBD".

Funding

This research was conceptualized and funded under the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101000365.

Author information

Authors and Affiliations

  1. Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Regional Center for Monitoring Parasitic infections (CREMOPAR), Naples, Italy

    Antonio Bosco, Lavinia Ciuca, Maria Paola Maurelli, Paola Vitiello, Giuseppe Cringoli & Laura Rinaldi

  2. School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guilford, UK

    Joaquin M. Prada

Authors
  1. Antonio Bosco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lavinia Ciuca
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria Paola Maurelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paola Vitiello
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Giuseppe Cringoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joaquin M. Prada
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Laura Rinaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LR and GC conceived, designed and coordinated the study. AB, LC, MPM and PV performed sampling and laboratory analyses. JMP performed the Bayesian data analysis. AB, LC, JMP and MPM drafted the manuscript. LR and GC revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Maria Paola Maurelli.

Ethics declarations

Ethics approval and consent to participate

We obtained verbal informed consent from the owners of farms to collect the faecal samples from animals.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bosco, A., Ciuca, L., Maurelli, M.P. et al. Comparison of Mini-FLOTAC, Flukefinder® and sedimentation techniques for detection and quantification of Fasciola hepatica and Calicophoron daubneyi eggs using spiked and naturally infected bovine faecal samples. Parasites Vectors 16, 260 (2023). https://doi.org/10.1186/s13071-023-05890-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13071-023-05890-2

Keywords

Parasites & Vectors

ISSN: 1756-3305

Contact us