Brain-derived extracellular vesicles as serologic markers of brain injury following cardiac arrest: A pilot feasibility study.

AIM: Assessment of neurologic injury within the immediate hours following out-of-hospital cardiac arrest (OHCA) resuscitation remains a major clinical challenge. Extracellular vesicles (EVs), small bodies derived from cytosolic contents during injury, may provide the opportunity for "liquid biopsy" within hours following resuscitation, as they contain proteins and RNA linked to cell type of origin. We evaluated whether micro-RNA (miRNA) from serologic EVs were associated with post-arrest neurologic outcome. METHODS: We obtained serial blood samples in an OHCA cohort. Using novel microfluidic techniques to isolate EVs based on EV surface marker GluR2 (present on excitatory neuronal dendrites enriched in hippocampal tissue), we employed reverse transcription quantitative polymerase chain reaction (RT-qPCR) methods to measure a panel of miRNAs and tested association with dichotomized modified Rankin Score (mRS) at discharge. RESULTS: EVs were assessed in 27 post-arrest patients between 7/3/2019 and 7/21/2022; 9 patients experienced good outcomes. Several miRNA species including miR-124 were statistically associated with mRS at discharge when measured within 6 hours of resuscitation (AUC = 0.84 for miR-124, p < 0.05). In a Kendall ranked correlation analysis, miRNA associations with outcome were not strongly correlated with standard serologic marker measurements, or amongst themselves, suggesting that miRNA provide distinct information from common protein biomarkers. CONCLUSIONS: This study explores the associations between miRNAs from neuron-derived EVs (NDEs) and circulating protein biomarkers within 6 hours with neurologic outcome, suggesting a panel of very early biomarker may be useful during clinical care. Future work will be required to test larger cohorts with a broader panel of miRNA species.

Investigating the Use of SARS-CoV-2 (COVID-19) Odor Expression as a Non-Invasive Diagnostic Tool-Pilot Study.

Since the beginning of the COVID-19 pandemic, there has been enormous interest in the development of measures that would allow for the swift detection of the disease. The rapid screening and preliminary diagnosis of SARS-CoV-2 infection allow for the instant identification of possibly infected individuals and the subsequent mitigation of the disease spread. Herein, the detection of SARS-CoV-2-infected individuals was explored using noninvasive sampling and low-preparatory-work analytical instrumentation. Hand odor samples were obtained from SARS-CoV-2-positive and -negative individuals. The volatile organic compounds (VOCs) were extracted from the collected hand odor samples using solid phase microextraction (SPME) and analyzed using gas chromatography coupled with mass spectrometry (GC-MS). Sparse partial least squares discriminant analysis (sPLS-DA) was used to develop predictive models using the suspected variant sample subsets. The developed sPLS-DA models performed moderately (75.8% (±0.4) accuracy, 81.8% sensitivity, 69.7% specificity) at distinguishing between SARS-CoV-2-positive and negative -individuals based on the VOC signatures alone. Potential markers for distinguishing between infection statuses were preliminarily acquired using this multivariate data analysis. This work highlights the potential of using odor signatures as a diagnostic tool and sets the groundwork for the optimization of other rapid screening sensors such as e-noses or detection canines.