1
|
Taha BA, Ahmed NM, Talreja RK, Haider AJ, Al Mashhadany Y, Al-Jubouri Q, Huddin AB, Mokhtar MHH, Rustagi S, Kaushik A, Chaudhary V, Arsad N. Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis. ACS Synth Biol 2024; 13:1600-1620. [PMID: 38842483 DOI: 10.1021/acssynbio.4c00070] [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] [Indexed: 06/07/2024]
Abstract
Antimicrobial resistance (AMR) poses a critical global One Health concern, ensuing from unintentional and continuous exposure to antibiotics, as well as challenges in accurate contagion diagnostics. Addressing AMR requires a strategic approach that emphasizes early stage prevention through screening in clinical, environmental, farming, and livestock settings to identify nonvulnerable antimicrobial agents and the associated genes. Conventional AMR diagnostics, like antibiotic susceptibility testing, possess drawbacks, including high costs, time-consuming processes, and significant manpower requirements, underscoring the need for intelligent, prompt, and on-site diagnostic techniques. Nanoenabled artificial intelligence (AI)-supported smart optical biosensors present a potential solution by facilitating rapid point-of-care AMR detection with real-time, sensitive, and portable capabilities. This Review comprehensively explores various types of optical nanobiosensors, such as surface plasmon resonance sensors, whispering-gallery mode sensors, optical coherence tomography, interference reflection imaging sensors, surface-enhanced Raman spectroscopy, fluorescence spectroscopy, microring resonance sensors, and optical tweezer biosensors, for AMR diagnostics. By harnessing the unique advantages of these nanoenabled smart biosensors, a revolutionary paradigm shift in AMR diagnostics can be achieved, characterized by rapid results, high sensitivity, portability, and integration with Internet-of-Things (IoT) technologies. Moreover, nanoenabled optical biosensors enable personalized monitoring and on-site detection, significantly reducing turnaround time and eliminating the human resources needed for sample preservation and transportation. Their potential for holistic environmental surveillance further enhances monitoring capabilities in diverse settings, leading to improved modern-age healthcare practices and more effective management of antimicrobial treatments. Embracing these advanced diagnostic tools promises to bolster global healthcare capacity to combat AMR and safeguard One Health.
Collapse
Affiliation(s)
- Bakr Ahmed Taha
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia
| | - Naser M Ahmed
- Department of Laser and Optoelectronics Engineering, Dijlah University College, 00964 Baghdad, Iraq
| | - Rishi Kumar Talreja
- Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi 110029, India
| | - Adawiya J Haider
- Applied Sciences Department/Laser Science and Technology Branch, University of Technology, 00964 Baghdad, Iraq
| | - Yousif Al Mashhadany
- Department of Electrical Engineering, College of Engineering, University of Anbar, Anbar 00964, Iraq
| | - Qussay Al-Jubouri
- Department of Communication Engineering, University of Technology, 00964 Baghdad, Iraq
| | - Aqilah Baseri Huddin
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia
| | - Mohd Hadri Hafiz Mokhtar
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia
| | - Sarvesh Rustagi
- School of Applied and Life Sciences, Uttaranchal University, Dehradun, Uttrakhand 248007, India
| | - Ajeet Kaushik
- NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, Florida 33805, United States
| | - Vishal Chaudhary
- Physics Department, Bhagini Nivedita College, University of Delhi, New Delhi 110045, India
| | - Norhana Arsad
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia
| |
Collapse
|
2
|
Meng Z, Tayyab M, Lin Z, Raji H, Javanmard M. A computer vision enhanced smart phone platform for microfluidic urine glucometry. Analyst 2024; 149:1719-1726. [PMID: 38334484 DOI: 10.1039/d3an01356a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Glucose is an important biomarker for diagnosing and prognosing various diseases, including diabetes and hypoglycemia, which can have severe side effects, symptoms, and even lead to death in patients. As a result, there is a need for quick and economical glucose level measurements to help identify those at potential risk. With the increase in smartphone users, portable smartphone glucose sensors are becoming popular. In this paper, we present a disposable microfluidic glucose sensor that accurately and rapidly quantifies glucose levels in human urine using a combination of colorimetric analysis and computer vision. This glucose sensor implements a disposable microfluidic device based on medical-grade tapes and glucose analysis strips on a glass slide integrated with a custom-made polydimethylsiloxane (PDMS) micropump that accelerates capillary flow, making it economical, convenient, rapid, and equipment-free. After absorbing the target solution, the disposable device is slid into the 3D-printed main chassis and illuminated exclusively with Light Emitting Diode (LED) illumination, which is pivotal to color-sensitive experiments. After collecting images, the images are imported into the algorithm to measure the glucose levels using computer vision and average RGB values measurements. This article illustrates the impressive accuracy and consistency of the glucose sensor in quantifying glucose in sucrose water. This is evidenced by the close agreement between the computer vision method used by the sensor and the traditional method of measuring in the biology field, as well as the small variation observed between different sensor performances. The exponential regression curve used in the study further confirms the strong relationship between glucose concentrations and average RGB values, with an R-square value of 0.997 indicating a high degree of correlation between these variables. The article also emphasizes the potential transferability of the solution described to other types of assays and smartphone-based sensors.
Collapse
Affiliation(s)
- Zhuolun Meng
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, NJ, USA.
| | - Muhammad Tayyab
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, NJ, USA.
| | - Zhongtian Lin
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, NJ, USA.
| | - Hassan Raji
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, NJ, USA.
| | - Mehdi Javanmard
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, NJ, USA.
| |
Collapse
|
3
|
Meng Z, Raji H, Tayyab M, Javanmard M. Cell phone microscopy enabled low-cost manufacturable colorimetric urine glucose test. Biomed Microdevices 2023; 25:43. [PMID: 37930426 DOI: 10.1007/s10544-023-00682-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 11/07/2023]
Abstract
Glucose serves as a pivotal biomarker crucial for the monitoring and diagnosis of a spectrum of medical conditions, encompassing hypoglycemia, hyperglycemia, and diabetes, all of which may precipitate severe clinical manifestations in individuals. As a result, there is a growing demand within the medical domain for the development of rapid, cost-effective, and user-friendly diagnostic tools. In this research article, we introduce an innovative glucose sensor that relies on microfluidic devices meticulously crafted from disposable, medical-grade tapes. These devices incorporate glucose urine analysis strips securely affixed to microscope glass slides. The microfluidic channels are intricately created through laser cutting, representing a departure from traditional cleanroom techniques. This approach streamlines production processes, enhances cost-efficiency, and obviates the need for specialized equipment. Subsequent to the absorption of the target solution, the disposable device is enclosed within a 3D-printed housing. Image capture is seamlessly facilitated through the use of a smartphone camera for subsequent colorimetric analysis. Our study adeptly demonstrates the glucose sensor's capability to accurately quantify glucose concentrations within sucrose solutions. This is achieved by employing an exponential regression model, elucidating the intricate relationship between glucose concentrations and average RGB (Red-Green-Blue) values. Furthermore, our comprehensive analysis reveals minimal variation in sensor performance across different instances. Significantly, this study underscores the potential adaptability and versatility of our solution for a wide array of assay types and smartphone-based sensor systems, making it particularly promising for deployment in resource-constrained settings and undeveloped countries. The robust correlation established between glucose concentrations and average RGB values, substantiated by an impressive R-square value of 0.98709, underscores the effectiveness and reliability of our pioneering approach within the medical field.
Collapse
Affiliation(s)
- Zhuolun Meng
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, 08854, New Jersey, USA
| | - Hassan Raji
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, 08854, New Jersey, USA
| | - Muhammad Tayyab
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, 08854, New Jersey, USA
| | - Mehdi Javanmard
- Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, 08854, New Jersey, USA.
| |
Collapse
|