疟疾检测的革命:揭示生物标志物创新的当前突破和未来可能

E. Obeagu, G. Okoroiwu, N. I. Ubosi, G. U. Obeagu, H. Onohuean, Tukur Muhammad, T. C. Adias
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引用次数: 0

摘要

在抗击疟疾的斗争中,诊断方法取得了重大进展,特别是通过发现和应用新型生物标记物。显微镜和快速诊断检测(RDTs)等传统诊断技术在灵敏度、特异性和检测低水平感染的能力方面存在局限性。生物标志物研究的最新突破有望克服这些挑战,提供更准确、快速和无创的检测方法。这些进展对于加强早期检测、指导有效治疗以及最终减轻全球疟疾负担至关重要。生物标志物检测的创新方法正在利用下一代测序(NGS)、蛋白质组学和代谢组学等尖端技术。这些技术已经发现了可以在血液、唾液或尿液中检测的新生物标志物,为广泛筛查提供了侵入性更小、可扩展性更强的选择。例如,在受感染者的呼吸中发现特定的挥发性有机化合物(VOC),就是一种革命性的非侵入性诊断工具。此外,机器学习算法与生物标志物数据的整合正在提高疟疾诊断的精确度和预测能力,使区分不同感染阶段和识别耐药菌株成为可能。展望未来,疟疾检测的未来在于继续探索多生物标志物面板和开发便携式护理点诊断设备。基于智能手机的技术和可穿戴生物传感器的结合有望为资源最有限的环境带来实时监测和远程诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Revolution in malaria detection: unveiling current breakthroughs and tomorrow’s possibilities in biomarker innovation
The ongoing battle against malaria has seen significant advancements in diagnostic methodologies, particularly through the discovery and application of novel biomarkers. Traditional diagnostic techniques, such as microscopy and rapid diagnostic tests (RDTs), have their limitations in terms of sensitivity, specificity, and the ability to detect low-level infections. Recent breakthroughs in biomarker research promise to overcome these challenges, providing more accurate, rapid, and non-invasive detection methods. These advancements are critical in enhancing early detection, guiding effective treatment, and ultimately reducing the global malaria burden. Innovative approaches in biomarker detection are leveraging cutting-edge technologies like next-generation sequencing (NGS), proteomics, and metabolomics. These techniques have led to the identification of new biomarkers that can be detected in blood, saliva, or urine, offering less invasive and more scalable options for widespread screening. For instance, the discovery of specific volatile organic compounds (VOCs) in the breath of infected individuals presents a revolutionary non-invasive diagnostic tool. Additionally, the integration of machine learning algorithms with biomarker data is enhancing the precision and predictive power of malaria diagnostics, making it possible to distinguish between different stages of infection and identify drug-resistant strains. Looking ahead, the future of malaria detection lies in the continued exploration of multi-biomarker panels and the development of portable, point-of-care diagnostic devices. The incorporation of smartphone-based technologies and wearable biosensors promises to bring real-time monitoring and remote diagnostics to even the most resource-limited settings.
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