Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants

Bakr Ahmed Taha; Yousif Al Mashhadany; Qussay Al-Jubouri; Affa Rozana Bt Abdul Rashid; Yunhan Luo; Zhe Chen; Sarvesh Rustagi; Vishal Chaudhary; Norhana Arsad

doi:10.1016/j.scitotenv.2023.163333

Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants

Sci Total Environ. 2023 Jul 1:880:163333. doi: 10.1016/j.scitotenv.2023.163333. Epub 2023 Apr 6.

Authors

Bakr Ahmed Taha¹, Yousif Al Mashhadany², Qussay Al-Jubouri³, Affa Rozana Bt Abdul Rashid⁴, Yunhan Luo⁵, Zhe Chen⁶, Sarvesh Rustagi⁷, Vishal Chaudhary⁸, Norhana Arsad⁹

Affiliations

¹ Photonics Technology Laboratory, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia. Electronic address: p103537@siswa.ukm.edu.my.
² Department of Electrical Engineering, College of Engineering, University of Anbar, Anbar 00964, Iraq.
³ Department of Communication Engineering, University of Technology, Baghdad, Iraq.
⁴ Faculty of Science and Technology, University Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, Malaysia.
⁵ Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Department of Optoelectronic Engineering, College of Science and Engineering, Jinan University, Guangzhou 510632, China.
⁶ Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, Jinan University Guangzhou, 510632, China.
⁷ School of Applied and Life Sciences, Uttaranchal University, Dehradun, Uttarakhand, India.
⁸ Department of Physics, Bhagini Nivedita College, University of Delhi, New Delhi 110045, India. Electronic address: drvishal@bn.du.ac.in.
⁹ Photonics Technology Laboratory, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia. Electronic address: noa@ukm.edu.my.

Abstract

Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.

Keywords: Artificial intelligence; Biosensors; Mutations evolution; Nanophononics; SARS-COV-2 variants.

Publication types

Review

MeSH terms

Artificial Intelligence
Biosensing Techniques*
COVID-19 Testing
COVID-19* / diagnosis
Humans
Intelligence
SARS-CoV-2 / genetics

Supplementary concepts

SARS-CoV-2 variants