Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
Division of Biomedical Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8LT, UK.
Adv Sci (Weinh). 2023 Oct;10(30):e2302146. doi: 10.1002/advs.202302146. Epub 2023 Aug 31.
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by debilitating fatigue that profoundly impacts patients' lives. Diagnosis of ME/CFS remains challenging, with most patients relying on self-report, questionnaires, and subjective measures to receive a diagnosis, and many never receiving a clear diagnosis at all. In this study, a single-cell Raman platform and artificial intelligence are utilized to analyze blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls. These results demonstrate that Raman profiles of blood cells can distinguish between healthy individuals, disease controls, and ME/CFS patients with high accuracy (91%), and can further differentiate between mild, moderate, and severe ME/CFS patients (84%). Additionally, specific Raman peaks that correlate with ME/CFS phenotypes and have the potential to provide insights into biological changes and support the development of new therapeutics are identified. This study presents a promising approach for aiding in the diagnosis and management of ME/CFS and can be extended to other unexplained chronic diseases such as long COVID and post-treatment Lyme disease syndrome, which share many of the same symptoms as ME/CFS.
肌痛性脑脊髓炎/慢性疲劳综合征(ME/CFS)的特征是使人虚弱的疲劳,严重影响患者的生活。ME/CFS 的诊断仍然具有挑战性,大多数患者依赖自我报告、问卷和主观措施来获得诊断,许多患者根本无法得到明确的诊断。在这项研究中,使用单细胞拉曼平台和人工智能来分析来自 98 个人体样本的血细胞,其中包括 61 名不同疾病严重程度的 ME/CFS 患者和 37 名健康和疾病对照者。这些结果表明,血细胞的拉曼图谱可以高精度(91%)区分健康个体、疾病对照者和 ME/CFS 患者,并且可以进一步区分轻度、中度和重度 ME/CFS 患者(84%)。此外,还确定了与 ME/CFS 表型相关的特定拉曼峰,这些峰有可能提供对生物学变化的深入了解,并支持新疗法的开发。这项研究为 ME/CFS 的诊断和管理提供了一种有前途的方法,并且可以扩展到其他未明原因的慢性疾病,如长新冠和治疗后莱姆病综合征,这些疾病与 ME/CFS 有许多相同的症状。