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- Creators: College of Liberal Arts and Sciences
As the return to normality in the wake of the COVID-19 pandemic enters its early stages, the necessity for accurate, quick, and community-wide surveillance of SARS-CoV-2 has been emphasized. Wastewater-based epidemiology (WBE) has been used across the world as a tool for monitoring the pandemic, but studies of its efficacy in comparison to the best-known method for surveillance, randomly selected COVID-19 testing, has limited research. This study evaluated the trends and correlations present between SARS-CoV-2 in the effluent wastewater of a large university campus and random COVID-19 testing results published by the university. A moderately strong positive correlation was found between the random testing and WBE surveillance methods (r = 0.63), and this correlation was strengthened when accommodating for lost samples during the experiment (r = 0.74).
Diagnosing psychogenic non-epileptic seizures (PNES) requires admission to an epilepsy monitoring unit, which is a lengthy and expensive process. Despite the cost of and time commitment to this inpatient evaluation, a definitive diagnosis at the end isn’t always guaranteed. Therefore, predictor variables such as demographic information and psychological testing scores can help improve the accuracy of diagnosing PNES or epilepsy at the end of a patient’s EMU admission. Locke et al. have demonstrated that the SOM scale and SOM-C subscale on the Personality Assessment Inventory (PAI) are the best indicators for predicting PNES diagnosis, with an optimal cut score of T≥70 on both of these scales. The aim of the current study was to determine whether evaluating male and female performance separately on these relevant PAI scales improves the accuracy of diagnosing PNES. The results support the hypothesis, such that male optimal cut scores on the SOM and SOM C scales are T=80 and T=75, respectively, and female optimal cut scores on the SOM and SOM C scales are T=71 and T=72, respectively. Utilizing the results of this study can help clinicians diagnose patients with PNES or epilepsy at the end of EMU evaluation with more certainty.