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Sonwal S, Gupta VK, Shukla S, Umapathi R, Ghoreishian SM, Han S, Bajpai VK, Cho Y, Huh YS. Panoramic view of artificial fruit ripening agents sensing technologies and the exigency of developing smart, rapid, and portable detection devices: A review. Adv Colloid Interface Sci 2024; 331:103199. [PMID: 38909548 DOI: 10.1016/j.cis.2024.103199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/22/2024] [Accepted: 05/18/2024] [Indexed: 06/25/2024]
Abstract
Recently, the availability of point-of-care sensor systems has led to the rapid development of smart and portable devices for the detection of hazardous analytes. The rapid flow of artificially ripened fruits into the market is associated with an elevated risk to human life, agriculture, and the ecosystem due to the use of artificial fruit ripening agents (AFRAs). Accordingly, there is a need for the development of "Point-of-care Sensors" to detect AFRAs due to several advantages, such as simple operation, promising detection mechanism, higher selectivity and sensitivity, compact, and portable. Traditional detection approaches are time-consuming and inappropriate for on-the-spot analyses. Presented comprehensive review aimed to reveal how such technology has systematically evolved over time (through conventional, advanced, and portable smart techniques) detection detect AFRA, till date. Moreover, focuses and highlights a framework of initiatives undertaken for technological advancements in the development of smart the portable detection techniques (kits) for the onsite detection of AFRAs in fruits with in-depth discussion over sensing mechanism and analytical performance of the sensing technology. Notably, colorimetric detection methods have the greatest potential for real-time monitoring of AFRA and its residues because they are easy to assemble, have a high level of selectivity and sensitivity, and can be read by the human eye independently. This study sought to differentiate between traditional credible strategies by presenting new prospects, perceptions, and challenges related to portable devices. This review provides systematic framework of advances in portable field recognition strategies for the on-spot AFRA detection in fruits and critical information for development of new paper-based portable sensors for fruit diagnostic sectors.
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Affiliation(s)
- Sonam Sonwal
- NanoBio High-Tech Materials Research Center, Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea
| | - Vivek Kumar Gupta
- NanoBio High-Tech Materials Research Center, Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea
| | - Shruti Shukla
- Department of Nanotechnology, North-Eastern Hill University (NEHU), East Khasi Hills, Shillong, Meghalaya 793022, India
| | - Reddicherla Umapathi
- NanoBio High-Tech Materials Research Center, Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea
| | | | - Soobin Han
- NanoBio High-Tech Materials Research Center, Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea
| | - Vivek Kumar Bajpai
- Department of Energy and Materials Engineering, Dongguk University, Seoul 04620, Republic of Korea
| | - Youngjin Cho
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju 55365, Republic of korea.
| | - Yun Suk Huh
- NanoBio High-Tech Materials Research Center, Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea.
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Hatamnejad A, Higham A, Somani S, Tam ES, Lim E, Khavandi S, de Pennington N, Chiu HH. Feasibility of an artificial intelligence phone call for postoperative care following cataract surgery in a diverse population: two phase prospective study protocol. BMJ Open Ophthalmol 2024; 9:e001475. [PMID: 38199790 PMCID: PMC10806655 DOI: 10.1136/bmjophth-2023-001475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
INTRODUCTION Artificial intelligence (AI) development has led to improvements in many areas of medicine. Canada has workforce pressures in delivering cataract care. A potential solution is using AI technology that can automate care delivery, increase effectiveness and decrease burdens placed on patients and the healthcare system. This study assesses the use of 'Dora', an example of an AI assistant that is able to deliver a regulated autonomous, voice-based, natural-language consultation with patients over the telephone. Dora is used in routine practice in the UK, but this study seeks to assess the safety, usability, acceptability and cost-effectiveness of using the technology in Canada. METHODS AND ANALYSIS This is a two-phase prospective single-centred trial. An expected 250 patients will be recruited for each phase of the study. For Phase I of the study, Dora will phone patients at postoperative week 1 and for Phase II of the study, Dora will phone patients within 24hours of their cataract surgery and again at postoperative week 1. We will evaluate the agreement between Dora and a supervising clinician regarding the need for further review based on the patients' symptoms. A random sample of patients will undergo the System Usability Scale followed by an extended semi-structured interview. The primary outcome of agreement between Dora and the supervisor will be assessed using the kappa statistic. Qualitative data from the interviews will further gauge patient opinions about Dora's usability, appropriateness and level of satisfaction. ETHICS AND DISSEMINATION Research Ethics Board William Osler Health System (ID: 22-0044) has approved this study and will be conducted by guidelines of Declaration of Helsinki. Master-linking sheet will contain the patient chart identification (ID), full name, date of birth and study ID. Results will be shared through peer-reviewed journals and presentations at conferences.
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Affiliation(s)
- Amin Hatamnejad
- McMaster University Michael G DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Aisling Higham
- Ufonia Limited, Oxford, UK
- Royal Berkshire Hospital NHS Foundation Trust, Reading, UK
| | - Sohel Somani
- Department of Opthalmology and Vision Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- William Osler Health System, Brampton, Ontario, Canada
| | - Eric S Tam
- Department of Opthalmology and Vision Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- William Osler Health System, Brampton, Ontario, Canada
| | - Ernest Lim
- Ufonia Limited, Oxford, UK
- Department of Computer Science, University of York, York, UK
| | | | | | - Hannah H Chiu
- McMaster University Michael G DeGroote School of Medicine, Hamilton, Ontario, Canada
- Department of Opthalmology and Vision Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- William Osler Health System, Brampton, Ontario, Canada
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