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Dongare PR, Nille OS, Bhavsar PS, Devre PV, Kolekar GB, Prajapat AL, Gore AH. Analytical applications of graphene oxide-based hydrogels. COMPREHENSIVE ANALYTICAL CHEMISTRY 2024:391-434. [DOI: 10.1016/bs.coac.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
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Ramalingam M, Jaisankar A, Cheng L, Krishnan S, Lan L, Hassan A, Sasmazel HT, Kaji H, Deigner HP, Pedraz JL, Kim HW, Shi Z, Marrazza G. Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses. DISCOVER NANO 2023; 18:58. [PMID: 37032711 PMCID: PMC10066940 DOI: 10.1186/s11671-023-03842-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023]
Abstract
Recent years have witnessed the emergence of several viruses and other pathogens. Some of these infectious diseases have spread globally, resulting in pandemics. Although biosensors of various types have been utilized for virus detection, their limited sensitivity remains an issue. Therefore, the development of better diagnostic tools that facilitate the more efficient detection of viruses and other pathogens has become important. Nanotechnology has been recognized as a powerful tool for the detection of viruses, and it is expected to change the landscape of virus detection and analysis. Recently, nanomaterials have gained enormous attention for their value in improving biosensor performance owing to their high surface-to-volume ratio and quantum size effects. This article reviews the impact of nanotechnology on the design, development, and performance of sensors for the detection of viruses. Special attention has been paid to nanoscale materials, various types of nanobiosensors, the internet of medical things, and artificial intelligence-based viral diagnostic techniques.
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Affiliation(s)
- Murugan Ramalingam
- School of Basic Medical Sciences, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, 610106 China
- Institute of Tissue Regeneration Engineering, Dankook University, Cheonan, 31116 Republic of Korea
- Department of Nanobiomedical Science, Dankook University, Cheonan, 31116 Republic of Korea
- BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan, 31116 Republic of Korea
- Mechanobiology Dental Medicine Research Center, Dankook University, Cheonan, 31116 Republic of Korea
- UCL Eastman-Korea Dental Medicine Innovation Centre, Dankook University, Cheonan, 31116 South Korea
- Department of Metallurgical and Materials Engineering, Faculty of Engineering, Atilim University, 06836 Ankara, Turkey
| | - Abinaya Jaisankar
- Centre for Biomaterials, Cellular and Molecular Theranostics, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, 632014 India
| | - Lijia Cheng
- School of Basic Medical Sciences, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, 610106 China
| | - Sasirekha Krishnan
- Centre for Biomaterials, Cellular and Molecular Theranostics, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, 632014 India
| | - Liang Lan
- School of Basic Medical Sciences, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, 610106 China
| | - Anwarul Hassan
- Department of Mechanical and Industrial Engineering, Biomedical Research Center, Qatar University, 2713, Doha, Qatar
| | - Hilal Turkoglu Sasmazel
- Department of Metallurgical and Materials Engineering, Faculty of Engineering, Atilim University, 06836 Ankara, Turkey
| | - Hirokazu Kaji
- Department of Biomechanics, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo, 101-0062 Japan
| | - Hans-Peter Deigner
- Institute of Precision Medicine, Medical and Life Sciences Faculty, Furtwangen University, 78054 Villingen-Schwenningen, Germany
| | - Jose Luis Pedraz
- NanoBioCel Group, Laboratory of Pharmaceutics, School of Pharmacy, University of the Basque Country, 01006 Vitoria-Gasteiz, Spain
- Biomedical Research Networking Centre in Bioengineering, Biomaterials and Nanomedicine, 28029 Madrid, Spain
| | - Hae-Won Kim
- Institute of Tissue Regeneration Engineering, Dankook University, Cheonan, 31116 Republic of Korea
- Department of Nanobiomedical Science, Dankook University, Cheonan, 31116 Republic of Korea
- BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan, 31116 Republic of Korea
- Mechanobiology Dental Medicine Research Center, Dankook University, Cheonan, 31116 Republic of Korea
- UCL Eastman-Korea Dental Medicine Innovation Centre, Dankook University, Cheonan, 31116 South Korea
| | - Zheng Shi
- School of Basic Medical Sciences, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, 610106 China
| | - Giovanna Marrazza
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Florence, Italy
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Alam MM, Alam MM, Mirza H, Sultana N, Sultana N, Pasha AA, Khan AI, Zafar A, Ahmad MT. A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique. Diagnostics (Basel) 2023; 13:diagnostics13111886. [PMID: 37296738 DOI: 10.3390/diagnostics13111886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/29/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023] Open
Abstract
COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods.
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Affiliation(s)
- Md Mottahir Alam
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz, Jeddah 21589, Saudi Arabia
| | - Md Moddassir Alam
- Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al-Batin, Hafr Al-Batin 39524, Saudi Arabia
| | - Hidayath Mirza
- Department of Electrical Engineering, College of Engineering, Jazan University, P.O. Box 706, Jazan 45142, Saudi Arabia
| | - Nishat Sultana
- Department of Business Administration, Applied College, Jazan University, P.O. Box 706, Jazan 45142, Saudi Arabia
| | - Nazia Sultana
- Government Medical College Siddipet, Ensanpalli, Siddipet District, Telangana 502114, India
| | - Amjad Ali Pasha
- Aerospace Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Asif Irshad Khan
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Aasim Zafar
- Department of Computer Science, Aligarh Muslim University, Aligarh 202002, India
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Ionescu RE. Updates on the Biofunctionalization of Gold Nanoparticles for the Rapid and Sensitive Multiplatform Diagnosis of SARS-CoV-2 Virus and Its Proteins: From Computational Models to Validation in Human Samples. Int J Mol Sci 2023; 24:ijms24119249. [PMID: 37298201 DOI: 10.3390/ijms24119249] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Since the outbreak of the pandemic respiratory virus SARS-CoV-2 (COVID-19), academic communities and governments/private companies have used several detection techniques based on gold nanoparticles (AuNPs). In this emergency context, colloidal AuNPs are highly valuable easy-to-synthesize biocompatible materials that can be used for different functionalization strategies and rapid viral immunodiagnosis. In this review, the latest multidisciplinary developments in the bioconjugation of AuNPs for the detection of SARS-CoV-2 virus and its proteins in (spiked) real samples are discussed for the first time, with reference to the optimal parameters provided by three approaches: one theoretical, via computational prediction, and two experimental, using dry and wet chemistry based on single/multistep protocols. Overall, to achieve high specificity and low detection limits for the target viral biomolecules, optimal running buffers for bioreagent dilutions and nanostructure washes should be validated before conducting optical, electrochemical, and acoustic biosensing investigations. Indeed, there is plenty of room for improvement in using gold nanomaterials as stable platforms for ultrasensitive and simultaneous "in vitro" detection by the untrained public of the whole SARS-CoV-2 virus, its proteins, and specific developed IgA/IgM/IgG antibodies (Ab) in bodily fluids. Hence, the lateral flow assay (LFA) approach is a quick and judicious solution to combating the pandemic. In this context, the author classifies LFAs according to four generations to guide readers in the future development of multifunctional biosensing platforms. Undoubtedly, the LFA kit market will continue to improve, adapting researchers' multidetection platforms for smartphones with easy-to-analyze results, and establishing user-friendly tools for more effective preventive and medical treatments.
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Affiliation(s)
- Rodica Elena Ionescu
- Light, Nanomaterials and Nanotechnology (L2n) Laboratory, CNRS EMR 7004, University of Technology of Troyes, 12 Rue Marie Curie, CS 42060, CEDEX, 10004 Troyes, France
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Jie Z, Liu C, Xia D, Zhang G. An atmospheric microwave plasma-based distributed system for medical waste treatment. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:51314-51326. [PMID: 36809622 PMCID: PMC9942016 DOI: 10.1007/s11356-023-25793-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 02/03/2023] [Indexed: 04/16/2023]
Abstract
Inadequate handling of infectious medical waste may promote the spread of the virus through secondary transmission during the transfer process. Microwave plasma, an ease-of-use, device-compact, and pollution-free technology, enables the on-site disposal of medical waste, thereby preventing secondary transmission. We developed atmospheric-pressure air-based microwave plasma torches with lengths exceeding 30 cm to rapidly treat various medical wastes in situ with nonhazardous exhaust gas. The gas compositions and temperatures throughout the medical waste treatment process were monitored by gas analyzers and thermocouples in real time. The main organic elements in medical waste and their residues were analyzed by an organic elemental analyzer. The results showed that (i) the weight reduction ratio of medical waste achieved a maximum value of 94%; (ii) a water-waste ratio of 30% was beneficial for enhancing the microwave plasma treatment effect for medical wastes; and (iii) substantial treatment effectiveness was achievable under a high feeding temperature (≥ 600 °C) and a high gas flow rate (≥ 40 L/min). Based on these results, we built a miniaturized and distributed pilot prototype for microwave plasma torch-based on-site medical waste treatment. This innovation could fill the gap in the field of small-scale medical waste treatment facilities and alleviate the existing issue of handling medical waste on-site.
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Affiliation(s)
- Ziyao Jie
- Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China
| | - Cheng Liu
- Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China
| | - Daolu Xia
- Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China
- Suqian Development and Reform Commission, Suqian, 223800, China
| | - Guixin Zhang
- Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China.
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Nneji CC, Urenyere R, Ukhurebor KE, Ajibola S, Onaseso OO. The impacts of COVID-19-induced online lectures on the teaching and learning process: An inquiring study of junior secondary schools in Orlu, Nigeria. Front Public Health 2022; 10:1054536. [PMID: 36478729 PMCID: PMC9720684 DOI: 10.3389/fpubh.2022.1054536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/07/2022] [Indexed: 11/22/2022] Open
Abstract
This study investigated how the sudden shift in the system of learning during the COVID-19 pandemic impacted the students, how the external environment impacted their performance, and the structural barriers encountered, which equally had significant impacts on students at junior secondary schools (JSS) in Orlu, Imo State, Nigeria. The study adopted the descriptive survey research method. The simple random sampling method was adopted with a sample size of 650 students. The data were collected using a structured questionnaire, rated using a four-point Likert scale, and analyzed using descriptive statistics such as frequency counts, percentages, and means. 60.10, 58.80, 59.50, 59.00, and 59.50% of the respondents agreed to research questions respectively. Based on these results, it was concluded that the COVID-19-induced online teaching and learning impacted negatively on the students and on the process of teaching and learning due to inadequate prior preparation for such a system of teaching and learning at the JSS level within the study area. These have serious implications and remain significant for policy and practice in the education sector.
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Affiliation(s)
- Confidence Chioma Nneji
- 1Africa Centre of Excellence on Technology Enhanced Learning (ACETEL), National Open University of Nigeria, Abuja, Nigeria
| | - R. Urenyere
- 1Africa Centre of Excellence on Technology Enhanced Learning (ACETEL), National Open University of Nigeria, Abuja, Nigeria
| | - Kingsley Eghonghon Ukhurebor
- 2Department of Physics, Faculty of Science, Edo State University, Uzairue, Edo State, Nigeria,*Correspondence: Kingsley Eghonghon Ukhurebor ;
| | - Saheed Ajibola
- 1Africa Centre of Excellence on Technology Enhanced Learning (ACETEL), National Open University of Nigeria, Abuja, Nigeria
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