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Ganesan M, Selvan Christyraj JRS, Venkatachalam S, Yesudhason BV, Chelladurai KS, Mohan M, Kalimuthu K, Narkhede YB, Christyraj JDS. Foldscope microscope, an inexpensive alternative tool to conventional microscopy-Applications in research and education: A review. Microsc Res Tech 2022; 85:3484-3494. [PMID: 35876424 DOI: 10.1002/jemt.24205] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/07/2022] [Accepted: 07/09/2022] [Indexed: 11/10/2022]
Abstract
Microscope is a device used for the visualization of tiny objects which are not visible to the naked eye. Traditional microscopes have been crucial for the advancement of contemporary science and medicine. Recent advancements in the field of microscopy have fueled its exponential growth rate. However, due to their expensive cost and complicated structure, modern microscopes remain inaccessible to the majority of the public. Nonetheless, the foldscope paper microscope has made it possible for anyone to explore and understand the world of microbes and organisms. In this review, we have listed foldscope-based research projects in various domains, as well as their key properties when compared to traditional research microscopes. In addition, we have briefly explored the impact of a foldscope microscope on public health, clinical diagnostics, forensic science, agriculture, basic science, developmental biology, and education. Moreover, the major drawbacks of paper microscopes and the current steps being taken to upgrade foldscope and its features are discussed in this review. Finally, we have concluded with our perspective that the microscope may be updated to imitate the advancement of a conventional microscope. RESEARCH HIGHLIGHTS: The foldscope, a low-cost instrument for studying the microscopic world. Foldscope applications were compared to conventional microscopes in many sectors. The foldscope microscope's existing limitations and potential prospects are highlighted.
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Affiliation(s)
- Mijithra Ganesan
- Regeneration and Stem Cell Biology Lab, Centre for Molecular and Nanomedical Sciences, International Research Centre, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
| | - Johnson Retnaraj Samuel Selvan Christyraj
- Regeneration and Stem Cell Biology Lab, Centre for Molecular and Nanomedical Sciences, International Research Centre, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
| | - Saravanakumar Venkatachalam
- Regeneration and Stem Cell Biology Lab, Centre for Molecular and Nanomedical Sciences, International Research Centre, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
| | - Beryl Vedha Yesudhason
- Regeneration and Stem Cell Biology Lab, Centre for Molecular and Nanomedical Sciences, International Research Centre, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
| | - Karthikeyan Subbiahanadar Chelladurai
- Regeneration and Stem Cell Biology Lab, Centre for Molecular and Nanomedical Sciences, International Research Centre, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
| | - Manikandan Mohan
- College of Pharmacy, University of Georgia, Athens, Georgia, USA.,VAXIGEN International Research Center Private Limited, Coimbatore, Tamilnadu, India
| | - Kalishwaralal Kalimuthu
- Division of Cancer Research, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India
| | - Yogesh B Narkhede
- Department of Chemistry and Biochemistry, University of Notre Dame du Lac, Notre Dame, Indiana, USA
| | - Jackson Durairaj Selvan Christyraj
- Regeneration and Stem Cell Biology Lab, Centre for Molecular and Nanomedical Sciences, International Research Centre, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
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Hall-Clifford R, Arzu A, Contreras S, Croissert Muguercia MG, de Leon Figueroa DX, Ochoa Elias MV, Soto Fernández AY, Tariq A, Banerjee I, Pennington P. Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000918. [PMID: 36962801 PMCID: PMC10021207 DOI: 10.1371/journal.pgph.0000918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/22/2022] [Indexed: 11/18/2022]
Abstract
Despite successes on the Sustainable Development Goals for access to improved water sources and sanitation, many low and middle-income countries (LMICs) continue to struggle with high rates of diarrheal disease. In Guatemala, 98% of water sources are estimated to have E. coli contamination. This project moves toward a novel low-cost approach to bridge the gap between the microbiologic identification of E. coli and the vast impact that this pathogen has on human health within marginalized communities using co-designed community-based tools, low-cost technology, and AI. An agile co-design process was followed with water quality stakeholders, community staff, and local graphic design artists to develop a community water quality education mobile app. A series of alpha- and beta-testers completed interactive demonstration, feedback, and in-depth interview sessions. A microbiology lab in Guatemala developed and piloted field protocols with lay community workers to collect and process water samples. A preliminary artificial intelligence (AI) algorithm was developed to detect the presence of E. coli in images generated from community-derived water samples. The mobile app emerged as a pictorial and audio-driven community-facing tool. The field protocol for water sampling and testing was successfully implemented by lay community workers. Feedback from the community workers indicated both desire and ability to conduct the water sampling and testing protocol under field conditions. However, images derived from the low-cost $2 microscope in field conditions were not of a suitable quality for AI object detection of E. coli, and additional low-cost technologies are being considered. The preliminary AI object detection algorithm from lab-derived images performed at 94% accuracy in identifying E. coli in comparison to the Chromocult gold-standard.
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Affiliation(s)
- Rachel Hall-Clifford
- Departments of Sociology and Global Health, Center for the Study of Human Health, Emory University, Decatur, GA, United States of America
| | - Alejandro Arzu
- Center for the Study of Human Health, Emory University, Decatur, GA, United States of America
| | - Saul Contreras
- Department of Computer Sciences, Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | | | | | | | | | - Amara Tariq
- Machine Intelligence in Medicine and Imaging (MI-2) Lab, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Imon Banerjee
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, United States of America
| | - Pamela Pennington
- Center for Biotechnology Studies, Universidad del Valle de Guatemala, Guatemala City, Guatemala
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