Parasitologia

A supplemental description of Lamproglena barbicola Fryer, 1961 is provided based on specimens collected from the gills of Labeobarbus altianalis (Boulenger, 1900) from the Nyando River, Lake Victoria Basin, Kenya, using an integrated approach of scanning electron microscopy (SEM) and molecular analysis (18S, 28S rDNA, and cox1 gene regions). Morphologically, the specimens conform to L. barbicola and closely resemble Lamproglena hoi Dippenaar, Luus-Powell & Roux, 2001; however, SEM revealed a previously undescribed feature on the uniramous antennule in L. barbicola, namely indistinctly three-segmented, tapering from a broad base to the apex, basal segment much longer than distal, comprising 14 setae of varying sizes, ventral laterally, absence of distinctive anterior fringe of setae on the antennule, as well as several characters that differentiate L. barbicola from L. hoi, including 5 setae at the basal endopod of leg one, five cuticular protuberances in the oral region, 19 setae on the basal antennular segment, and 10 setae on the distal segment, with 1 seta on each ramus. The phylogenetic analysis confirms L. barbicola as a sister taxon of L. hoi, supporting their close relationship. The genetic divergence presented as the uncorrected genetic p-distances between L. barbicola and L. hoi are 23.1% and 0.45% for cox1 and 28S rDNA regions, respectively, with observed nucleotide differences of 145 and 3 bp between the sequences, respectively. There was no interspecific variability detected in the 18S rDNA sequences. This study provides novel molecular sequences and the first high-resolution SEM images, which reveal additional taxonomic features for L. barbicola, establishing a robust reference for future identification.

Soil-transmitted helminthiases (STH) represent the most prevalent helminth infections worldwide and are closely associated with inadequate sanitation and poverty. During 2020 and 2021, a significant information gap emerged as a consequence of the SARS-CoV-2 pandemic. Therefore, the objective of this study was to provide an updated post-pandemic overview by estimating the prevalence of STH, identifying the etiological agents involved, and analyzing the risk factors associated with these infections. The study was conducted in Ilaló, Pichincha, Ecuador, where a total of 320 individuals were examined using three diagnostic methods: Kato-Katz, McMaster, and Mini-FLOTAC. Of these, 73 participants tested positive (22.81%, 95% CI: 22.3–23.4). The most frequently identified parasite was Ascaris lumbricoides (74.73%, 95% CI: 73.7–75.8). The main risk factors identified were pig farming (OR: 4.16; 95% CI: 2.34–7.42) and vegetable and fruit cultivation (OR: 11.66; 95% CI: 4.32–41.08). These findings provide relevant epidemiological data on STH in the region, demonstrating a reduction in infection prevalence likely associated with improved prevention and control measures implemented during the COVID-19 pandemic.

Clonorchis sinensis and Metagonimus spp. are prevalent parasites in Korea, and accurate diagnosis is essential because treatment dosages differ between infections. However, their eggs are morphologically similar under light microscopy, making differentiation difficult and dependent on examiner expertise. To address this limitation, we evaluated an artificial intelligence (AI)-based automated microscope solution for the simultaneous detection and discrimination of both parasites. Microscopic images from 170 stool samples were analyzed using an AI system based on You Only Look Once version 5. The annotated dataset comprised 9455 egg images (6494 C. sinensis and 2961 Metagonimus spp.), randomly divided at the slide/patient level into training (6862), validation (1301), and test (1292) sets. Diagnostic performance was evaluated using mean average precision, confusion matrix analysis, and correlation with conventional microscopy. The model achieved a classification accuracy of up to 97.8%. C. sinensis showed higher recall and F1 scores, whereas Metagonimus spp. showed higher precision and specificity. Species identification showed complete concordance with conventional microscopy, and egg quantification was strongly correlated. These results indicate that the proposed AI system may serve as a supportive diagnostic tool comparable to conventional microscopy.