RESUMO
Background: Most seasonally circulating enteroviruses result in asymptomatic or mildly symptomatic infections. In rare cases, however, infection with some subtypes can result in paralysis or death. Of the 300 subtypes known, only poliovirus is reportable, limiting our understanding of the distribution of other enteroviruses that can cause clinical disease. Objective: The overarching objectives of this study were to: 1) describe the distribution of enteroviruses in Arizona during the late summer and fall of 2022, the time of year when they are thought to be most abundant, and 2) demonstrate the utility of viral pan-assay approaches for semi-agnostic discovery that can be followed up by more targeted assays and phylogenomics. Methods: This study utilizes pooled nasal samples collected from school-aged children and long-term care facility residents, and wastewater from multiple locations in Arizona during July-October of 2022. We used PCR to amplify and sequence a region common to all enteroviruses, followed by species-level bioinformatic characterization using the QIIME 2 platform. For Enterovirus-D68 (EV-D68), detection was carried out using RT-qPCR, followed by confirmation using near-complete whole EV-D68 genome sequencing using a newly designed tiled amplicon approach. Results: In the late summer and early fall of 2022, multiple enterovirus species were identified in Arizona wastewater, with Coxsackievirus A6, EV-D68, and Coxsackievirus A19 composing 86% of the characterized reads sequenced. While EV-D68 was not identified in pooled human nasal samples, and the only reported acute flaccid myelitis case in Arizona did not test positive for the virus, an in-depth analysis of EV-D68 in wastewater revealed that the virus was circulating from August through mid-October. A phylogenetic analysis on this relatively limited dataset revealed just a few importations into the state, with a single clade indicating local circulation. Significance: This study further supports the utility of wastewater-based epidemiology to identify potential public health threats. Our further investigations into EV-D68 shows how these data might help inform healthcare diagnoses for children presenting with concerning neurological symptoms.
RESUMO
Although a number of algorithms exist for estimating ground contact events (GCEs) from kinematic data during running, they are typically only applicable to heelstrike running, or have only been evaluated at a single running speed. The purpose of this study was to investigate the accuracy of four kinematics-based algorithms to estimate GCEs over a range of running speeds and footstrike types. Subjects ran over a force platform at a range of speeds; kinetic and kinematic data was captured at 1000Hz, and kinematic data was downsampled to 250Hz. A windowing process initially identified reduced time windows containing touchdown and toe-off. Algorithms based on acceleration and jerk signals of the foot markers were used to estimate touchdown (2 algorithms), toe-off (2 algorithms), and ground contact time (GCT) (4 algorithms), and compared to synchronous 'gold standard' force platform data. An algorithm utilising the vertical acceleration peak of either the heel or first metatarsal marker (whichever appeared first) for touchdown, and the vertical jerk peak of the hallux marker for toe-off, resulted in the lowest offsets (+3.1ms, 95% Confidence Interval (CI): -11.8 to +18.1ms; and +2.1ms, CI: -8.1 to +12.2ms respectively). This method also resulted in the smallest offset in GCT (-1.1ms, CI: -18.6 to +16.4ms). Offsets in GCE and GCT estimates from all algorithms were typically negatively correlated to running speed, with offsets decreasing as speed increased. Assessing GCEs and GCT using this method may be useful when a force platform is unavailable or impractical.
