UC Davis

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a global health concern,impacting millions of individuals, yet diagnosis remains a challenge due to the absence of consistent laboratory testing methods. Currently, diagnosis is solely dependent on doctors’ experience and in depth understanding on patients’ medical histories. This thesis aims to develop an accessible, rapid, and cost-effective laboratory-based diagnostic method for ME/CFS, while also exploring potential applications in treatment monitoring and evaluation using microfluidic approaches. We achieved this by creating a microfluidic platform to measure capillary velocity of red blood cells (RBCs) at controlled oxygen tensions (PO2). Our research revealed that RBCs from ME/CFS patients exhibit impaired responses to changes in PO2, as compared to healthy controls. Such PO2 - regulated RBC capillary velocity was thus used for ME/CFS diagnosis and exhibited an approximately 80% accuracy using machine learning methods. Additionally, our investigation identified two potential drug candidates, Salmeterol Xinafoate and Xanomeline, which showed promise in improving PO2 - regulated capillary velocity of RBCs from ME/CFS patients. Moreover, we found that a simple phosphate buffered saline (PBS) wash can recover RBC’s sensitivity to deoxygenation, implying that RBC-cytokine interaction in ME/CFS contributes to PO2 - regulated RBC capillary velocity. In conclusion, we have developed a diagnostic platform for ME/CFS using a combination of microfluidic techniques and machine learning, offering a cost-effective and simple approach to ME/CFS diagnosis. It also opens doors for in-depth exploration of the underlying mechanisms of the condition.