Chibuike Uwakwe

All Publications

• Longitudinal wearable sensor data enhance precision of Long COVID detection. PLOS digital health Uwakwe, C. K., Rangan, E. S., Kumar, S., Gutjahr, G., Miao, X., Brooks, A. W., Maguire, P., Mishra, T., McGuire, L., Snyder, M. P. 2025; 4 (11): e0001093

  Abstract

  Despite the millions of individuals struggling with persistent symptoms, Long COVID has remained difficult to diagnose due to limited objective biomarkers, often leading to underdiagnosis or even misdiagnosis. To bridge this gap, we investigated the potential of utilizing wearable sensor data to aid in the diagnosis of Long COVID. We analyzed longitudinal heart rate (HR) data from 126 individuals with acute SARS-CoV-2 infections to develop machine learning models capable of predicting Long COVID status using derived HR features, symptom features, or a combination of both feature sets. The HR features were derived across six analytical categories, including time-domain, Poincaré nonlinear, raw signal, Kullback-Leibler (KL) divergence, variational mode decomposition (VMD), and the Shannon energy envelope (SEE), enabling the capture of heart rate dynamics over various temporal scales and the quantification of day-to-day shifts in HR distributions. The symptom features used in the final models included chest pain, vomiting, excessive sweating, memory loss, brain fog, heart palpitations, and loss of smell. The combined HR- and symptom-feature model demonstrated robust predictive performance, achieving an area under the Receiver Operating Characteristic curve (ROC-AUC) of 95.1% and an area under the Precision-Recall curve (PR-AUC) of 85.9%. These values represent a significant improvement of approximately 5% in both the ROC-AUC and PR-AUC over the symptoms-only model. At the population level, this improvement in discrimination could lead to clinically meaningful reductions in misclassification and improved patient outcomes, achieved through a non-invasive diagnostic tool. These findings suggest that wearable HR data could be used to derive an objective biomarker for Long COVID, thereby enhancing diagnostic precision.

  View details for DOI 10.1371/journal.pdig.0001093

  View details for PubMedID 41264615

• A bioinspired microfluidic wearable sensor for multiday sweat sampling, transport, and metabolic analysis. Science advances Shin, S., Liu, R., Yang, Y., Lasalde-Ramírez, J. A., Kim, G., Won, C., Min, J., Wang, C., Fan, K., Han, H., Uwakwe, C., Heng, W., Hsiai, T. K., Li, Z., FitzGerald, J. D., Gao, W. 2025; 11 (33): eadw9024

  Abstract

  Wearable sweat sensors enable noninvasive real-time biochemical monitoring, holding immense potential for personalized health care applications. However, achieving prolonged and reliable sweat sampling, along with stable biochemical analysis, remains challenging due to inconsistent secretion, rapid evaporation, and the reliance on external stimulation. Here, we present BMS3, a bioinspired microfluidic wearable sweat sensor system designed for multiday continuous metabolic monitoring. BMS3 integrates hierarchically graded microchannels and superhydrophobic-superhydrophilic Janus membranes, inspired by pitcher plant trichomes and lotus leaves to enable efficient low volume sweat collection, transport, and renewal. A miniaturized carbachol gel-based iontophoresis module autonomously induces localized sweat secretion. Furthermore, the microfluidic design sustains sweat sampling for over 2 days from a single iontophoresis session, eliminating the need for physical exertion. In vitro and in vivo studies in healthy participants and patients with gout demonstrate BMS3's capability for continuous metabolic monitoring. By simultaneously tracking uric acid, xanthine, and alcohol levels, it effectively differentiates normal and pathological states while delivering timely therapeutic feedback.

  View details for DOI 10.1126/sciadv.adw9024

  View details for PubMedID 40802776

  View details for PubMedCentralID PMC12346344

• Nonperfusion Area and Other Vascular Metrics by Wider Field Swept-Source OCT Angiography as Biomarkers of Diabetic Retinopathy Severity. Ophthalmology science Garg, I., Uwakwe, C., Le, R., Lu, E. S., Cui, Y., Wai, K. M., Katz, R., Zhu, Y., Moon, J. Y., Li, C. Y., Laíns, I., Eliott, D., Elze, T., Kim, L. A., Wu, D. M., Miller, J. W., Husain, D., Vavvas, D. G., Miller, J. B. 2022; 2 (2)

  Abstract

  To study the wider field swept-source optical coherence tomography angiography (WF SS-OCTA) metrics, especially non-perfusion area (NPA), in the diagnosing and staging of DR.Cross-sectional observational study (November 2018-September 2020).473 eyes of 286 patients (69 eyes of 49 control patients and 404 eyes of 237 diabetic patients).We imaged using 6mm×6mm and 12mm×12mm angiograms on WF SS-OCTA. Images were analyzed using the ARI Network and FIJI ImageJ. Mixed effects multiple regression models and receiver operator characteristic analysis was used for statistical analyses.Quantitative metrics such as vessel density (VD); vessel skeletonized density (VSD); foveal avascular zone (FAZ) area, circularity, and perimeter; and NPA in DR and their relative performance for its diagnosis and grading.Among patients with diabetes (median age 59 years), 51 eyes had no DR, 185 eyes (88 mild, 97 moderate-severe) had non-proliferative DR (NPDR); and 168 eyes had proliferative DR (PDR). Trend analysis revealed a progressive decline in superficial capillary plexus (SCP) VD and VSD, and increased NPA with increasing DR severity. Additionally, there was a significant reduction in deep capillary plexus (DCP) VD and VSD in early DR (mild NPDR), but the progressive reduction in advanced DR stages was not significant. NPA was the best parameter to diagnose DR (AUC:0.96), whereas all parameters combined on both angiograms efficiently diagnosed (AUC:0.97) and differentiated between DR stages (AUC range:0.83-0.97). The presence of diabetic macular edema was associated with reduced SCP and DCP VD and VSD within mild NPDR eyes, whereas an increased VD and VSD in SCP among moderate-severe NPDR group.Our work highlights the importance of NPA, which can be more readily and easily measured with WF SS-OCTA compared to fluorescein angiography. It is additionally quick and non-invasive, and hence can be an important adjunct for DR diagnosis and management. In our study, a combination of all OCTA metrics on both 6mm×6mm and 12mm×12mm angiograms had the best diagnostic accuracy for DR and its severity. Further longitudinal studies are needed to assess NPA as a biomarker for progression or regression of DR severity.

  View details for DOI 10.1016/j.xops.2022.100144

  View details for PubMedID 35647573

  View details for PubMedCentralID PMC9137369