The chorion ultrastructure of ova of Lophius spp.

The chorion surface ultrastructure of unfertilized eggs of black anglerfish Lophius budegassa and white anglerfish Lophius piscatorius was examined by scanning electron microscopy. Species-specific differences were observed.

Eggshell biometrics for individual egg identification based on convolutional neural networks.

Individual egg identification technology has potential applications in breeding, product tracking/tracing, and anti-counterfeit. This study developed a novel method for individual egg identification based on eggshell images. A convolutional neural network-based model, named Eggshell Biometric Identification (EBI) model, was proposed and evaluated. The main workflow included eggshell biometric feature extraction, egg information registration, and egg identification. The image dataset of individual eggshell was collected from the blunt-end region of 770 chicken eggs using an image acquisition platform. The ResNeXt network was then trained as a texture feature extraction module to obtain sufficient eggshell texture features. The EBI model was applied to a test set of 1,540 images. The testing results showed that when an appropriate Euclidean distance threshold for classification was set (17.18), the correct recognition rate and the equal error rate reached 99.96% and 0.02%. This new method provides an efficient and accurate solution for individual chicken egg identification, and can be extended to eggs of other poultry species for product tracking/tracing and anti-counterfeit.

Enhancing the Yield of a Lab-on-a-Disk-Based Single-Image Parasite Quantification Device.

The recently proposed single-image parasite quantification (SIMPAQ) platform based on a Lab-on-a-Disc (LOD) device was previously successfully tested in field conditions, demonstrating its efficiency in soil-transmitted helminth (STH) egg detection and analysis on the level delivered by the current state-of-the-art methods. Furthermore, the SIMPAQ provides relatively quick diagnostics and requires small amounts of sample and materials. On the other hand, in a recent related study, it was revealed that the performance of the SIMPAQ method can be limited due to the action of the tangential Euler and Coriolis forces, and the interaction of the moving eggs with the walls of the LOD chamber. Here, we propose a new improved design that allows us to overcome these limitations and enhance the yield of the SIMPAQ LOD device, as demonstrated in experiments with a synthetic particle model system and real parasite eggs. Despite the simplicity, the proposed design modification is demonstrated to allow a substantial improvement in the yield of the SIMPAQ device, i.e., above 90% of parasite eggs and 98% of synthetic model particles were transported to the field of view. The new design proposed here will be further examined in the new generation of SIMPAQ devices within ongoing research on STH egg detection in field conditions.

Migration Behavior of Low-Density Particles in Lab-on-a-Disc Devices: Effect of Walls.

The effect of the lateral walls of a Lab-On-a-Disc device on the dynamics of a model system of particles with a density lower than that of the solvent (modelling parasites eggs) is analyzed theoretically and experimentally. In the absence of lateral walls, a particle always moves in the direction of the centrifugal force, while its trajectory is deflected in the tangential direction by the inertial Coriolis and Euler forces. Lateral walls, depending on the angle forming with the radial direction, can guide the particle either in the same or in the opposite direction to the centrifugal force, thus resulting in unusual particle trajectories including zig-zag or backwards particle motion. The effect is pronounced in the case of short operation times when the acceleration of the angular rotation, and thus the Euler force, is considerable. The predicted unusual motion is demonstrated by numerically solving the equation of motion in the presence of lateral walls and verified in the experiment with particles of density lower than that of the solvent. Our analysis is useful for design and operational considerations of Lab-On-a-Disc devices aiming for or involving (bio)particle handling.

Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm.

BACKGROUND: Fecal examinations in pet cats and dogs are key components of routine veterinary practice; however, their accuracy is influenced by diagnostic methodologies and the experience level of personnel performing the tests. The VETSCAN IMAGYST system was developed to provide simpler and easier fecal examinations which are less influenced by examiners' skills. This system consists of three components: a sample preparation device, an automated microscope scanner, and analysis software. The objectives of this study were to qualitatively evaluate the performance of the VETSCAN IMAGYST system on feline parasites (Ancylostoma and Toxocara cati) and protozoan parasites (Cystoisospora and Giardia) and to assess and compare the performance of the VETSCAN IMAGYST centrifugal flotation method to reference centrifugal and passive flotation methods. METHODS: To evaluate the diagnostic performance of the scanning and algorithmic components of the VETSCAN IMAGYST system, fecal slides were prepared by the VETSCAN IMAGYST centrifugal flotation technique with pre-screened fecal samples collected from dogs and cats and examined by both an algorithm and parasitologists. To assess the performance of the VETSCAN IMAGYST centrifugal flotation technique, diagnostic sensitivity and specificity were calculated and compared to those of conventional flotation techniques. RESULTS: The performance of the VETSCAN IMAGYST algorithm closely correlated with evaluations by parasitologists, with sensitivity of 75.8-100% and specificity of 93.1-100% across the targeted parasites. For samples with 50 eggs or less per slide, Lin's concordance correlation coefficients ranged from 0.70 to 0.95 across the targeted parasites. The results of the VETSCAN IMAGYST centrifugal flotation method correlated well with those of the conventional centrifugal flotation method across the targeted parasites: sensitivity of 65.7-100% and specificity of 97.6-100%. Similar results were observed for the conventional passive flotation method compared to the conventional centrifugal flotation method: sensitivity of 56.4-91.7% and specificity of 99.4-100%. CONCLUSIONS: The VETSCAN IMAGYST scanning and algorithmic systems with the VETSCAN IMAGYST fecal preparation technique demonstrated a similar qualitative performance to the parasitologists' examinations with conventional fecal flotation techniques. Given the deep learning nature of the VETSCAN IMAGYST system, its performance is expected to improve over time, enabling it to be utilized in veterinary clinics to perform fecal examinations accurately and efficiently.

Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm.

BACKGROUND: Fecal examination is an important component of routine companion animal wellness exams. Sensitivity and specificity of fecal examinations, however, are influenced by sample preparation methodologies and the level of training and experience of personnel who read fecal slides. The VETSCAN IMAGYST system consists of three components: a sample preparation device, a commercially available scanner, and an analysis software. The VETSCAN IMAGYST automated scanner and cloud-based, deep learning algorithm, locates, classifies, and identifies parasite eggs found on fecal microscopic slides. The main study objectives were (i) to qualitatively evaluate the capabilities of the VETSCAN IMAGYST screening system and (ii) to assess and compare the performance of the VETSCAN IMAGYST fecal preparation methods to conventional fecal flotation techniques. METHODS: To assess the capabilities of VETSCAN IMAGYST screening components, fecal slides were prepared by the VETSCAN IMAGYST centrifugal and passive flotation techniques with 100 pre-screened fecal samples collected from dogs and cats and examined by both the algorithm and parasitologists. To determine the diagnostic sensitivity and specificity of the VETSCAN IMAGYST sample preparation techniques, fecal flotation slides were prepared by four different techniques (VETSCAN IMAGYST centrifugal and passive flotations, conventional centrifugal flotation, and passive flotation using OVASSAY® Plus) and examined by parasitologists. Additionally, required sample preparation and scanning times were estimated on a subset of samples to evaluate VETSCAN IMAGYST ease-of-use. RESULTS: The algorithm performance of the VETSCAN IMAGYST closely matched that of the parasitologists, with Pearson's correlation coefficient (r) ranging from 0.83-0.99 across four taxa of parasites, Ancylostoma, Toxocara, Trichuris and Taeniidae. Both VETSCAN IMAGYST centrifugal and passive flotation methods correlated well with conventional preparation methods on all targeted parasites (diagnostic sensitivity of 75.8-100%, specificity of 91.8-100%, qualitative agreement between methods of 93.8-94.5%). Sample preparation, slide scan and image analysis were completed within 10-14 min by VETSCAN IMAGYST centrifugal and passive flotations, respectively. CONCLUSIONS: The VETSCAN IMAGYST scanning system with the VETSCAN IMAGYST sample preparation methods demonstrated a qualitative match in comparison to the results of parasitologists' examinations with conventional fecal flotation techniques. The VETSCAN IMAGYST is an easy-to-use, next generation qualitative and possibly quantitative diagnostic platform that brings expert clinical results into the hands of veterinary clinics.

Development of a Lab-on-a-Disk Platform with Digital Imaging for Identification and Counting of Parasite Eggs in Human and Animal Stool.

We present a lab-on-a-disk technology for fast identification and quantification of parasite eggs in stool. We introduce a separation and packing method of eggs contained in 1 g of stool, allowing for removal of commonly present solid particles, fat droplets and air bubbles. The separation is based on a combined gravitational and centrifugal flotation, with the eggs guided to a packed monolayer, enabling quantitation and identification of subtypes of the eggs present in a single field of view (FOV). The prototype was tested with stool samples from pigs and humans infected with intestinal parasites (soil-transmitted helminths eggs). The quality of the images created by this platform was appropriate for identification and quantification of egg types present in the sample.