Key pointsr Carotid body (CB) glomus cells mediate acute oxygen sensing and the initiation of the hypoxic ventilatory response, yet the gene expression profile of these cells is not available.r We demonstrate that the single cell RNA-Seq method is a powerful tool for identifying highly expressed genes in CB glomus cells.r Our single cell RNA-Seq results characterized novel CB glomus cell genes, including members of the G protein-coupled receptor signalling pathway, ion channels and atypical mitochondrial electron transport chain subunits.r A heterologous cell-based screening identified acetate (which is known to affect CB glomus cell activity) as an agonist for the most highly abundant G protein-coupled receptor (Olfr78) in CB glomus cells.r These data established the first transcriptome profile of CB glomus cells, highlighting genes with potential implications in CB chemosensory function. AbstractThe carotid body (CB) is a major arterial chemoreceptor containing glomus cells whose activities are regulated by changes in arterial blood content, including oxygen. Despite significant advancements in the characterization of their physiological properties, our understanding of the underlying molecular machinery and signalling pathway in CB glomus cells is still limited. To overcome this, we employed the single cell RNA-Seq method by performing next-generation sequencing on single glomus cell-derived cDNAs to eliminate contamination of genes derived from other cell types present in the CB. Using this method, we identified a set of genes abundantly expressed in glomus cells, which contained novel glomus cell-specific genes. Transcriptome and subsequent in situ hybridization and immunohistochemistry analyses identified abundant G protein-coupled receptor signalling pathway components and various types of ion channels, as well as members of the hypoxia-inducible factors pathway. A short-chain fatty acid olfactory receptor Olfr78, recently implicated in CB function, was the most abundant G protein-coupled receptor. Two atypical mitochondrial electron transport chain subunits (Ndufa4l2 and Cox4i2) were among the most specifically expressed genes in CB glomus cells, highlighting their potential roles in mitochondria-mediated oxygen sensing. The wealth of information provided by the present study offers a valuable foundation for identifying molecules functioning in the CB.

IMPORTANCESeizure risk stratification is needed to boost inpatient seizure detection and to improve continuous electroencephalogram (cEEG) cost-effectiveness. 2HELPS2B can address this need but requires validation. OBJECTIVE To use an independent cohort to validate the 2HELPS2B score and develop a practical guide for its use. DESIGN, SETTING, AND PARTICIPANTSThis multicenter retrospective medical record review analyzed clinical and EEG data from patients 18 years or older with a clinical indication for cEEG and an EEG duration of 12 hours or longer who were receiving consecutive cEEG at 6 centers from January 2012 to January 2019. 2HELPS2B was evaluated with the validation cohort using the mean calibration error (CAL), a measure of the difference between prediction and actual results. A Kaplan-Meier survival analysis was used to determine the duration of EEG monitoring to achieve a seizure risk of less than 5% based on the 2HELPS2B score calculated on first-hour (screening) EEG. Participants undergoing elective epilepsy monitoring and those who had experienced cardiac arrest were excluded. No participants who met the inclusion criteria were excluded. MAIN OUTCOMES AND MEASURESThe main outcome was a CAL error of less than 5% in the validation cohort. RESULTSThe study included 2111 participants (median age, 51 years; 1113 men [52.7%]; median EEG duration, 48 hours) and the primary outcome was met with a validation cohort CAL error of 4.0% compared with a CAL of 2.7% in the foundational cohort (P = .13). For the 2HELPS2B score calculated on only the first hour of EEG in those without seizures during that hour, the CAL error remained at less than 5.0% at 4.2% and allowed for stratifying patients into low-(2HELPS2B = 0; <5% risk of seizures), medium-(2HELPS2B = 1; 12% risk of seizures), and high-risk (2HELPS2B, Ն2; risk of seizures, >25%) groups. Each of the categories had an associated minimum recommended duration of EEG monitoring to achieve at least a less than 5% risk of seizures, a 2HELPS2B score of 0 at 1-hour screening EEG, a 2HELPS2B score of 1 at 12 hours, and a 2HELPS2B score of 2 or greater at 24 hours.CONCLUSIONS AND RELEVANCE In this study, 2HELPS2B was validated as a clinical tool to aid in seizure detection, clinical communication, and cEEG use in hospitalized patients. In patients without prior clinical seizures, a screening 1-hour EEG that showed no epileptiform findings was an adequate screen. In patients with any highly epileptiform EEG patterns during the first hour of EEG (ie, a 2HELPS2B score of Ն2), at least 24 hours of recording is recommended.

BackgroundPost-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE1).MethodsWe performed a multicentre, retrospective case–control study of patients with TBI. 63 PTE1patients were matched with 63 non-PTE1patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities.ResultsIn the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE1risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)).ConclusionsEpileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE1prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models.

Summary:Traditional review of EEG for seizure detection requires time and the expertise of a trained neurophysiologist; therefore, it is time- and resource-intensive. Quantitative EEG (qEEG) encompasses a variety of methods to make EEG review more efficient and allows for nonexpert review. Literature supports that qEEG is commonly used by neurophysiologists and nonexperts in clinical practice. In this review, the different types of qEEG trends and spectrograms used for seizure detection in adults, from basic concepts to clinical applications, are discussed. The merits and drawbacks of the most common qEEG trends are detailed. The authors detail the retrospective literature on qEEG sensitivity, specificity, and false alarm rate as interpreted by experts and nonexperts alike. Finally, the authors discuss the future of qEEG as a useful screening tool and speculate on the trajectory of future investigations in the field.