Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia.
Department of Laser and Optoelectronics Engineering, Dijlah University College, 00964 Baghdad, Iraq.
ACS Synth Biol. 2024 Jun 21;13(6):1600-1620. doi: 10.1021/acssynbio.4c00070. Epub 2024 Jun 6.
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.
抗微生物药物耐药性（AMR）是一个全球性的关乎人类健康的重要问题，它源自于抗生素的非故意和持续暴露，以及准确传染诊断方面的挑战。解决 AMR 需要采取一种战略方法，通过在临床、环境、农业和畜牧业环境中进行早期筛查，强调通过筛选来预防 AMR，以识别非脆弱的抗菌剂和相关基因。传统的 AMR 诊断方法，如抗生素药敏试验，存在一些缺点，包括高成本、耗时的过程和大量人力需求，这突显了需要智能、快速和现场诊断技术的必要性。基于人工智能（AI）的纳米智能光学生物传感器通过提供实时、敏感和便携的能力，为快速现场 AMR 检测提供了一种潜在的解决方案。本综述全面探讨了各种类型的光学纳米生物传感器，如表面等离子体共振传感器、声子晶体 whispering-gallery 模式传感器、光学相干断层扫描、干涉反射成像传感器、表面增强拉曼光谱、荧光光谱、微环谐振传感器和光学镊子生物传感器，用于 AMR 诊断。通过利用这些纳米智能生物传感器的独特优势，可以实现 AMR 诊断的革命性范式转变，其特点是快速结果、高灵敏度、便携性以及与物联网（IoT）技术的集成。此外，纳米智能光学生物传感器能够实现个性化监测和现场检测，大大缩短了周转时间，并消除了对样品保存和运输所需的人力资源。它们在整体环境监测方面的潜力进一步增强了在不同环境中的监测能力，从而改善了现代医疗保健实践，并更有效地管理抗菌治疗。采用这些先进的诊断工具有望增强全球医疗保健能力，以对抗 AMR 并维护人类健康。
