Evaluation of Parasight All-in-One system for the automated enumeration of helminth ova in canine and feline feces.

BACKGROUND:  Digital imaging combined with deep-learning-based computational image analysis is a growing area in medical diagnostics, including parasitology, where a number of automated analytical devices have been developed and are available for use in clinical practice. METHODS: The performance of Parasight All-in-One (AIO), a second-generation device, was evaluated by comparing it to a well-accepted research method (mini-FLOTAC) and to another commercially available test (Imagyst). Fifty-nine canine and feline infected fecal specimens were quantitatively analyzed by all three methods. Since some samples were positive for more than one parasite, the dataset consisted of 48 specimens positive for Ancylostoma spp., 13 for Toxocara spp. and 23 for Trichuris spp. RESULTS: The magnitude of Parasight AIO counts correlated well with those of mini-FLOTAC but not with those of Imagyst. Parasight AIO counted approximately 3.5-fold more ova of Ancylostoma spp. and Trichuris spp. and 4.6-fold more ova of Toxocara spp. than the mini-FLOTAC, and counted 27.9-, 17.1- and 10.2-fold more of these same ova than Imagyst, respectively. These differences translated into differences between the test sensitivities at low egg count levels (< 50 eggs/g), with Parasight AIO > mini-FLOTAC > Imagyst. At higher egg counts Parasight AIO and mini-FLOTAC performed with comparable precision (which was significantly higher that than Imagyst), whereas at lower counts (> 30 eggs/g) Parasight was more precise than both mini-FLOTAC and Imagyst, while the latter two methods did not significantly differ from each other. CONCLUSIONS: In general, Parasight AIO analyses were both more precise and sensitive than mini-FLOTAC and Imagyst and quantitatively correlated well with mini-FLOTAC. While Parasight AIO produced lower raw counts in eggs-per-gram than mini-FLOTAC, these could be corrected using the data generated from these correlations.

Rapid, automated quantification of Haemonchus contortus ova in sheep faecal samples.

Haemonchus contortus is one of the most pathogenic nematodes affecting small ruminants globally and is responsible for large economic losses in the sheep and goat industry. Anthelmintic resistance is rampant in this parasite and thus parasite control programs must account for drug efficacy on individual farms and, sometimes, whether H. contortus is the most prevalent trichostrongylid. Historically, coproculture has been the main way to determine the prevalence of H. contortus in faecal samples due to the inability to morphologically differentiate between trichostrongylid egg types, but this process requires a skilled technician and takes multiple days to complete. Fluoresceinated peanut agglutinin (PNA) has been shown to specifically bind H. contortus and thus differentiate eggs based on whether they fluoresce, but this method has not been widely adopted. The ParasightTM System (PS) fluorescently stains helminth eggs in order to identify and quantify them, and the H. contortus PNA staining method was therefore adapted to this platform using methodology requiring only 20 min to obtain results. In this study, 74 fecal samples were collected from sheep and analyzed for PNA-stained H. contortus, using both PS and manual fluorescence microscopy. The percentage of H. contortus was determined based on standard total strongylid counts with PS or brightfield microscopy. Additionally, 15 samples were processed for coproculture with larval identification, and analyzed with both manual and automated PNA methods. All methods were compared using the coefficient of determination (R2) and the Lin's concordance correlation coefficient (ρc). ParasightTM and manual PNA percent H. contortus results were highly correlated with R2 = 0.8436 and ρc = 0.9100 for all 74 fecal samples. Coproculture versus PS percent H. contortus were also highly correlated with R2 = 0.8245 and ρc = 0.8605. Overall, this system provides a rapid and convenient method for determining the percentage of H. contortus in sheep and goat fecal samples without requiring specialized training.

Parascaris spp. eggs shedding patterns in juvenile horses.

Parascaris spp. infect foals worldwide and foals typically shed eggs in the feces from about three to six months of age, upon which natural immunity is incurred. High levels of anthelmintic resistance of Parascaris spp. are a global concern, and further understanding egg shedding patterns and fecal egg counting (FEC) data variability is of high importance. The aims of this study were to monitor Parascaris spp. egg shedding in untreated foals during 12-23 weeks of age, estimate sources of data variability, and assess precision of two ascarid FEC techniques. Fecal samples were collected weekly from 11 foals born in 2022, from May through November (29 weeks). Six subsamples were extracted from each weekly sample to determine 30 FECs between two techniques: a McMaster technique and an Automated Egg Counting System (AECS). Mixed linear modeling was carried out with age, sex, birth month, seasonality, spring- or summer-born foals, and egg counting technique as explanatory variables. Ascarid FECs were associated with age (p < 0.001), seasonality (p < 0.001), and technique (p < 0.001). The McMaster technique was more precise with a mean coefficient of variation (CV) of 34.57% and a 95% confidence interval (CI) of 30.80%- 38.30% compared to the CV for the AECS, which was 42.22% (CI: 37.70%-46.70%). Seasonality accounted for the highest proportion of variance (PV) of all covariates, but differences in PVs for covariates existed between techniques with foal age and subsample contributing more variance to the McMaster, and individual foal and seasonality contributing more to the AECS. Subsamples and replicate counts accounted for less than 1% of the total data variance. The results highlighted substantial differences in PVs between the two techniques at the subsample (AECS: 57.14%; McMaster: 77.51%) and replicate count levels (AECS: 42.86%; McMaster: 22.49%). While differences in precision were observed between the two FEC techniques, they were negligible in the data set, as the overwhelming majority of the data variability in ascarid FECs was attributed to individual foal, seasonality, and foal age.