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Phuangsri C, Japa O. High prevalence of natural infection by the ruminant blood fluke Schistosoma spindale in the intermediate snail host Indoplanorbis exustus in Uttaradit, Northern Thailand. Vet World 2024; 17:413-420. [PMID: 38595665 PMCID: PMC11000477 DOI: 10.14202/vetworld.2024.413-420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 01/29/2024] [Indexed: 04/11/2024] Open
Abstract
Background and Aim Freshwater snails belonging to the family Planorbidae serve as the first intermediate hosts of many species of important parasitic flukes of animals and humans. Information regarding the occurrence of planorbid snail larval trematode infection is limited in Northern Thailand. Thus, this study aimed to estimate the prevalence of larval trematode infection of the freshwater snail Indoplanorbis exustus in Uttaradit, Thailand, and to identify trematode species based on their morphological and molecular characteristics. Materials and Methods Planorbid snail specimens were collected from a water reservoir in Uttaradit, Thailand, from June to August 2023. Snails were assessed for larval trematode infection through cercarial shedding and crushing methods. The released cercariae were preliminarily identified on the basis of their morphological characteristics. In addition, species identification of the detected cercariae was conducted using 28S ribosomal RNA and cytochrome c oxidase subunit 1 gene sequence analyses. Results The overall prevalence of cercarial infection was 61.5% (107/174) in planorbid snails in Uttaradit province. Two species of cercarial trematodes, Schistosoma spindale (106/174, 60.9%) and Artyfechinostomum malayanum (1/174, 0.6%), were identified using morphological and molecular analyses, of which S. spindale was the most abundant species. Our studied snails did not have mixed infection with more than two cercarial species. Conclusion Our findings reveal a remarkably high prevalence of S. spindale cercariae infecting planorbid snails in Uttaradit, indicating that humans and animals across the study area are at risk of infection. Our data may contribute to the development of effective strategies to control this zoonotic infectious disease.
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Affiliation(s)
- Chorpaka Phuangsri
- Division of Microbiology and Parasitology, School of Medical Sciences, University of Phayao, Phayao, Thailand
| | - Ornampai Japa
- Division of Microbiology and Parasitology, School of Medical Sciences, University of Phayao, Phayao, Thailand
- Scientific Instrument and Product Standard Quality Inspection Center, University of Phayao, Phayao, Thailand
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Huguenin A, Kincaid-Smith J, Depaquit J, Boissier J, Ferté H. MALDI-TOF: A new tool for the identification of Schistosoma cercariae and detection of hybrids. PLoS Negl Trop Dis 2023; 17:e0010577. [PMID: 36976804 PMCID: PMC10081743 DOI: 10.1371/journal.pntd.0010577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 04/07/2023] [Accepted: 03/06/2023] [Indexed: 03/29/2023] Open
Abstract
Schistosomiasis is a neglected water-born parasitic disease caused by Schistosoma affecting more than 200 million people. Introgressive hybridization is common among these parasites and raises issues concerning their zoonotic transmission. Morphological identification of Schistosoma cercariae is difficult and does not permit hybrids detection. Our objective was to assess the performance of MALDI-TOF (Matrix Assistated Laser Desorption-Ionization–Time Of Flight) mass spectrometry for the specific identification of cercariae in human and non-human Schistosoma and for the detection of hybridization between S. bovis and S. haematobium. Spectra were collected from laboratory reared molluscs infested with strains of S. haematobium, S. mansoni, S. bovis, S. rodhaini and S. bovis x S. haematobium natural (Corsican hybrid) and artificial hybrids. Cluster analysis showed a clear separation between S. haematobium, S. bovis, S. mansoni and S. rodhaini. Corsican hybrids are classified with those of the parental strain of S. haematobium whereas other hybrids formed a distinct cluster. In blind test analysis the developed MALDI-TOF spectral database permits identification of Schistosoma cercariae with high accuracy (94%) and good specificity (S. bovis: 99.59%, S. haematobium 99.56%, S. mansoni and S. rodhaini: 100%). Most misidentifications were between S. haematobium and the Corsican hybrids. The use of machine learning permits to improve the discrimination between these last two taxa, with accuracy, F1 score and Sensitivity/Specificity > 97%. In multivariate analysis the factors associated with obtaining a valid identification score (> 1.7) were absence of ethanol preservation (p < 0.001) and a number of 2–3 cercariae deposited per well (p < 0.001). Also, spectra acquired from S. mansoni cercariae are more likely to obtain a valid identification score than those acquired from S. haematobium (p<0.001). MALDI-TOF is a reliable technique for high-throughput identification of Schistosoma cercariae of medical and veterinary importance and could be useful for field survey in endemic areas.
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Affiliation(s)
- Antoine Huguenin
- Université de Reims Champagne Ardenne, EA7510 ESCAPE, Reims, France
- Laboratoire de Parasitologie-Mycologie, pôle de Biopathologie, CHU de Reims, Reims, France
- * E-mail:
| | - Julien Kincaid-Smith
- IHPE, Université de Montpellier, CNRS, Ifremer, Université de Perpignan, Perpignan, France
- CBGP, IRD, CIRAD, INRAE, Institut Agro, Université de Montpellier, Montpellier, France
| | - Jérôme Depaquit
- Université de Reims Champagne Ardenne, EA7510 ESCAPE, Reims, France
- Laboratoire de Parasitologie-Mycologie, pôle de Biopathologie, CHU de Reims, Reims, France
| | - Jérôme Boissier
- IHPE, Université de Montpellier, CNRS, Ifremer, Université de Perpignan, Perpignan, France
| | - Hubert Ferté
- Université de Reims Champagne Ardenne, EA7510 ESCAPE, Reims, France
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Goh HA, Ho CK, Abas FS. Front-end deep learning web apps development and deployment: a review. APPL INTELL 2022; 53:15923-15945. [PMID: 36466774 PMCID: PMC9709375 DOI: 10.1007/s10489-022-04278-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2022] [Indexed: 12/03/2022]
Abstract
Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.
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Affiliation(s)
- Hock-Ann Goh
- Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka Malaysia
| | - Chin-Kuan Ho
- Asia Pacific University of Technology and Innovation, Jalan Teknologi 5, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysia
| | - Fazly Salleh Abas
- Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka Malaysia
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Akram Abdulrazzaq A, Al-Douri AT, Abdullah Hamad A, Musa Jaber M, Meraf Z. Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams. Bioinorg Chem Appl 2022; 2022:2682287. [PMID: 35586785 PMCID: PMC9110249 DOI: 10.1155/2022/2682287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 04/23/2022] [Accepted: 04/27/2022] [Indexed: 11/18/2022] Open
Abstract
Schistosoma mansoni is one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope. The area of pattern recognition in images presents itself as a promising alternative to support and automate image-based exams, and deep learning techniques have been successfully applied for this purpose. In order to automate this process, it is proposed in this work the application of deep learning methods for the detection of schistosomiasis eggs, and a comparison is made between two deep learning techniques, convolutional neural network (CNN) and structured pyramidal neural network (SPNN). The results obtained in a real database indicate that the techniques are effective in the recognition of schistosomiasis eggs, in which both obtained AUC (area under the curve) above 0.90, with the CNN showing superiority in this aspect. . However, the SPNN proved to be faster than the CNN.
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Affiliation(s)
| | - Asaad T. Al-Douri
- Department of Dental Industry, College of Medical Technology, Al-Kitab University, Alton Kopru, Iraq
| | - Abdulsattar Abdullah Hamad
- Department of Medical Laboratory Techniques, Dijlah University College, Baghdad 10021, Iraq
- Department of Medical Laboratory Techniques, Al-Turath University College, Baghdad 10021, Iraq
| | - Mustafa Musa Jaber
- Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10021, Iraq
| | - Zelalem Meraf
- Department of Statistics, Injibara University, Injibara, Ethiopia
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Shi L, Zhang JF, Li W, Yang K. Development of New Technologies for Risk Identification of Schistosomiasis Transmission in China. Pathogens 2022; 11:pathogens11020224. [PMID: 35215167 PMCID: PMC8877870 DOI: 10.3390/pathogens11020224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/27/2022] [Accepted: 02/04/2022] [Indexed: 12/07/2022] Open
Abstract
Schistosomiasis is serious parasitic disease with an estimated global prevalence of active infections of more than 190 million. Accurate methods for the assessment of schistosomiasis risk are crucial for schistosomiasis prevention and control in China. Traditional approaches to the identification of epidemiological risk factors include pathogen biology, immunology, imaging, and molecular biology techniques. Identification of schistosomiasis risk has been revolutionized by the advent of computer network communication technologies, including 3S, mathematical modeling, big data, and artificial intelligence (AI). In this review, we analyze the development of traditional and new technologies for risk identification of schistosomiasis transmission in China. New technologies allow for the integration of environmental and socio-economic factors for accurate prediction of the risk population and regions. The combination of traditional and new techniques provides a foundation for the development of more effective approaches to accelerate the process of schistosomiasis elimination.
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Affiliation(s)
- Liang Shi
- National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Wuxi 214064, China; (L.S.); (J.-F.Z.); (W.L.)
- Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Wuxi 214064, China
- Jiangsu Institute of Parasitic Diseases, Wuxi 214064, China
- Public Health Research Center, Jiangnan University, Wuxi 214064, China
| | - Jian-Feng Zhang
- National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Wuxi 214064, China; (L.S.); (J.-F.Z.); (W.L.)
- Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Wuxi 214064, China
- Jiangsu Institute of Parasitic Diseases, Wuxi 214064, China
- Public Health Research Center, Jiangnan University, Wuxi 214064, China
| | - Wei Li
- National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Wuxi 214064, China; (L.S.); (J.-F.Z.); (W.L.)
- Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Wuxi 214064, China
- Jiangsu Institute of Parasitic Diseases, Wuxi 214064, China
- Public Health Research Center, Jiangnan University, Wuxi 214064, China
| | - Kun Yang
- National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Wuxi 214064, China; (L.S.); (J.-F.Z.); (W.L.)
- Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Wuxi 214064, China
- Jiangsu Institute of Parasitic Diseases, Wuxi 214064, China
- Public Health Research Center, Jiangnan University, Wuxi 214064, China
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Correspondence:
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