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Objective Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), remains the world's largest public health concern in 2021. A history of close contact with infectious patients is a factor that predicts a positive SARS-CoV-2 test result. Meanwhile, the precise predictive value of symptoms suggestive of COVID-19 has not been fully elucidated. This study aimed to clarify the predictive and discriminatory value of each clinical symptom suggestive of COVID-19. Methods This study enrolled participants who were tested for SARS-CoV-2 by reverse transcription polymerase chain reaction using a nasopharyngeal swab between November 2020 and January 2021. All enrolled patients were evaluated for data regarding the presence and closeness of contact with infectious patients and comprehensive clinical features (i.e., fever, cough, dyspnea, fatigue, dysosmia, and dysgeusia). Results Among the 1,744 tested participants, 144 tested positive for SARS-CoV-2. In the test-positive group, self-reported cough, fatigue, dysosmia, and dysgeusia were significant predictors of COVID-19, independent from a history of close contact. In particular, the presence of dysosmia was the strongest predictor of COVID-19 in both univariate and multivariate analyses. Among the 42 patients with self-reported dysosmia, 25 (59.5%) were SARS-CoV-2 test-positive. Self-reported dysosmia was reported by 25 (17.4%) of the 144 patients who tested positive for SARS-CoV-2, and 15 (60.0%) of the 25 COVID-19 patients with dysosmia had accompanying dysgeusia. Conclusion The presence of dysosmia was reported by 10-25% of patients with COVID-19, and is a significant predictor of COVID-19 infection, independent from a history of close contact.

This aim of this study was to assess the discriminatory value of fractal and grey level co-occurrence matrix (GLCM) analysis methods in standard microscopy analysis of two histologically similar brain white mass regions that have different nerve fiber orientation. A total of 160 digital micrographs of thionine-stained rat brain white mass were acquired using a Pro-MicroScan DEM-200 instrument. Eighty micrographs from the anterior corpus callosum and eighty from the anterior cingulum areas of the brain were analyzed. The micrographs were evaluated using the National Institutes of Health ImageJ software and its plugins. For each micrograph, seven parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, GLCM variance, fractal dimension, and lacunarity. Using the Receiver operating characteristic analysis, the highest discriminatory value was determined for inverse difference moment (IDM) (area under the receiver operating characteristic (ROC) curve equaled 0.925, and for the criterion IDM

Asymptotic confidence bounds on the location parameters of the linear growth curve, asymptotic distribution of the canonical correlations and asymptotic confidence bounds on the discriminatory value for the linear discriminant function are established when a set of independent observations are taken from an elliptical distribution (or from a distribution possessing some properties on the moments).