Development of a PCR assay and pyrosequencing for identification of important human fish-borne trematodes and its potential use for detection in fecal specimens

BACKGROUND: Small liver and minute intestinal flukes are highly prevalent in Southeast Asia. Definitive diagnosis of parasite infection is usually achieved parasitologically by finding the fluke eggs in feces. However, their eggs are difficult to differentiate morphologically in fecal samples, even for experienced technicians. The present study developed a PCR assay coupled with DNA pyrosequencing for identification of the fish-borne trematodes (FBT), Opisthorchis viverrini, Clonorchis sinensis, Haplorchis taichui, H. pumilio and Stellantchasmus falcatus, and to evaluate potential detection in fecal specimens, and identification and differentiation of cercarial and metacercarial stages. METHODS: Primers targeting the partial 28S large subunit ribosomal RNA gene were designed and about 46–47 nucleotides were selected as the target region for species identification by a PCR assay coupled with a pyrosequencing technique. RESULTS: The nucleotide variations at 24 positions, which is sufficient for the identification of the five species of FBT were selected. The method could identify O. viverrini and C. sinensis eggs in feces, cercarial and metacercarial stages of O. viverrini, and metacercarial stage of H. pumilio and H. taichui. The detection limit was as little as a single O. viverrini or C. sinensis egg artificially inoculated in 100 mg of non-infected fecal sample (equivalent to 10 eggs per gram), indicating highly sensitivity. The method was found to be superior to the traditional microscopy method and was more rapid than Sanger DNA sequencing. CONCLUSIONS: DNA pyrosequencing-based identification is a valuable tool for differentiating O. viverrini and other Opisthorchis-like eggs, and can be applied to epidemiological studies and for molecular taxonomic investigation of FBT in endemic areas

Growth and development of Gnathostoma spinigerum (Nematoda: Gnathostomatidae) larvae in Mesocyclops aspericornis (Cyclopoida: Cyclopidae)

<p>Abstract</p> <p>Background</p> <p><it>Gnathostoma spinigerum </it>larva is pathogenic, causing gnathostomiasis in humans and certain animals, and is prevalent mainly in Asia. Growth and development of <it>Gnathostoma spinigerum </it>larvae in the cyclopoid copepod <it>Mesocyclops aspericornis</it>, the first intermediate host, were examined.</p> <p>Results</p> <p>When newly hatched, ensheathed second-stage larvae (L2) were ingested by <it>M. aspericornis</it>, they immediately appeared exsheathed in the stomach of <it>M. aspericornis</it>. They then penetrated the stomach wall and entered the body cavity, where they immediately metamorphosed to a stunted form with the body length/width ratio equal to the early third-stage larvae (EL3) up to 2 h after being ingested. During metamorphosis, the anterior spine-like structure of L2 transformed into unequal transparent lips. The larvae moulted into EL3 in the body cavity of the copepod at around day 5-7 post-infection. Minute cuticular striations were seen on the whole body, with prominent single-pointed spines on the anterior part of the body. The head bulb had four rows of hooklets and two lateral trilobed lips. The size of EL3 in copepods continuously increased towards day 12 and showed a negative correlation to their density per copepod (R = -0.881, <it>P </it>< 0.05 for body length, and R = -0.906, <it>P </it>< 0.05 for body width).</p> <p>Conclusions</p> <p>The results revealed for the first time that <it>M. aspericornis</it>, one of the most abundant freshwater copepods in Thailand, is a suitable first intermediate host for <it>G. spinigerum</it>. High susceptibility of <it>M. aspericornis </it>suggests its importance for the maintenance of the life cycle of <it>G. spinigerum </it>in Thailand.</p

Molecular detection of Schistosoma japonicum in infected snails and mouse faeces using a real-time PCR assay with FRET hybridisation probes

A real-time polymerase chain reaction (PCR) assay with fluorescence resonance energy transfer (FRET) hybridisation probes combined with melting curve analysis was developed to detect Schistosoma japonicum in experimentally infected snails and in faecal samples of infected mice. This procedure is based on melting curve analysis of a hybrid between an amplicon from the S. japonicum internal transcribed spacer region 2 sequence, which is a 192-bp S. japonicum-specific sequence, and fluorophore-labelled specific probes. Real-time FRET PCR could detect as little as a single cercaria artificially introduced into a pool of 10 non-infected snails and a single egg inoculated in 100 mg of non-infected mouse faeces. All S. japonicum-infected snails and all faecal samples from infected mice were positive. Non-infected snails, non-infected mouse faeces and genomic DNA from other parasites were negative. This assay is rapid and has potential for epidemiological S. japonicum surveys in snails, intermediate hosts and faecal samples of final hosts

Development and validation of a real-time SYBR green PCR method for the detection and differentiation of Babesia and Theileria species (Apicomplexa: Piroplasmida) in hard ticks and cattle blood from Thailand

Tick-borne pathogens, particularly Babesia and Theileria species, are major threats to cattle production, causing economically significant diseases such as babesiosis and theileriosis. In this study, a real-time SYBR Green PCR assay was developed to detect Babesia and Theileria species in hard ticks (N = 65) and cattle blood samples (N = 143) from Thailand. Using primers targeting the mitochondrial cytochrome b gene for Babesia and the nuclear 18S rRNA gene for Theileria, the assay measured specific melting temperatures (Tm) for each species. The results showed distinct Tm values for Babesia bigemina (74.38 ± 0.04 °C), Babesia bovis (75.7 ± 0.06 °C), Theileria orientalis (74.61 ± 0.03 °C), Theileria sinensis (75.84 ± 0.03 °C), and Theileria annulata (74.06 ± 0.03 °C). The assay demonstrated high specificity, with a cutoff cycle threshold of < 35 cycles and a minimum detectable concentration of 10 copies/μL. Significant species differences in melting curves were confirmed using Tukey’s HSD test (p < 0.05). Theileria orientalis was detected in 8.4% of cattle blood samples, while T. sinensis was found in 25.9%, and B. bigemina in 0.7%. Theileria orientalis was also detected in 7.7% of tick samples, T. sinensis in 16.9%, and B. bigemina in 6.1%. The assay returned negative results for all non-target blood and tissue pathogens tested for specificity. This robust, high-throughput assay is highly effective for monitoring Babesia and Theileria infections, facilitating close surveillance and intervention efforts against tick-borne diseases in cattle

Specificity of immunoblotting analyses in eosinophilic meningitis

Angiostrongylus cantonensis and Gnathostoma spinigerum are the two most common causative parasites of eosinophilic meningitis (EOM). Serological tests are helpful tools for confirming the identity of the pathogen. Recent reports determined the specificity of such tests by using normal healthy controls. There have been limited studies done to rule out the cross-reactivity between these two causative parasites of EOM. This study aims to assess the specificity of the serological test in EOM by using each condition as a control for the other. Thirty-three patients with a diagnosis of EOM were enrolled. Sera from 22 patients with a positive 29-kDa antigenic diagnostic band of A. cantonensis were tested for the 21 and 24-kDa antigenic bands of G. spinigerum. Similarly, sera of 11 gnathostomiasis patients were tested for the 29-kDa diagnostic band for A. cantonensis. Only one patient in the angiostrongyliasis group had a positive result for the 21 and 24-kDa antigenic bands of G. spinigerum, while no gnathostomiasis patients showed a positive result for the 29-kDa antigenic band of A. cantonensis. The specificity of the 21 and 24-kDa antigenic bands for gnathostomiasis and the 29-kDa antigenic band for A. cantonensis was 95.5% and 100%, respectively. The antigenic bands for the diagnosis of gnathostomiasis and angiostrongyliasis in EOM were highly specific

Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification

A. lumbricoides infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to A. lumbricoides. Generally, allied health classifies parasite egg type by using on microscopy-based methods that are laborious, are limited by low sensitivity, and require high expertise. However, misclassification may occur due to their heterogeneous experience. For their reason, computer technology is considered to aid humans. With the benefit of speed and ability of computer technology, image recognition is adopted to recognize images much more quickly and precisely than human beings. This research proposes deep learning for A. lumbricoides’s egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. The challenge is to recognize 3 types of eggs of A. lumbricoides with the optimal architecture of deep learning. The results showed that the classification accuracy of the parasite eggs is up to 93.33%. This great effectiveness of the proposed model could help reduce the time-consuming image classification of parasite egg.</jats:p

Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification

A. lumbricoides infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to A. lumbricoides. Generally, allied health classifies parasite egg type by using on microscopy-based methods that are laborious, are limited by low sensitivity, and require high expertise. However, misclassification may occur due to their heterogeneous experience. For their reason, computer technology is considered to aid humans. With the benefit of speed and ability of computer technology, image recognition is adopted to recognize images much more quickly and precisely than human beings. This research proposes deep learning for A. lumbricoides’s egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. The challenge is to recognize 3 types of eggs of A. lumbricoides with the optimal architecture of deep learning. The results showed that the classification accuracy of the parasite eggs is up to 93.33%. This great effectiveness of the proposed model could help reduce the time-consuming image classification of parasite egg