Abstract. Paranasal meningiomas were diagnosed in ten dogs based on gross and light microscopic examinations of tissue specimens, and, in one case, electron microscopic examination. Seven of ten dogs were female (average age was 13 years). Most dogs (7/10) had seizures on examination. Two dogs with meningioma located in the nasal cavity had nasal discharge, and one had epistaxis. Tumors originated in the nasoparanasal region (eight) and frontal region of the cranial cavity (two). The histologic types of meningioma included psammomatous (two), transitional (three), meningotheliomatous (two), fibroblastic (two), and angioblastic (one). Tumors were malignant and extended to the brain in eight cases. These tumors differed from intracranial meningiomas mainly in their more anaplastic nature and aggressive behavior.

Purpose. To investigate the factors contributing to mortality in coronavirus disease 2019 (COVID-19) patients admitted in the intensive care unit (ICU) and design a model to predict the mortality rate. Method. We retrospectively evaluated the medical records and CT images of the ICU-admitted COVID-19 patients who had an on-admission chest CT scan. We analyzed the patients’ demographic, clinical, laboratory, and radiologic findings and compared them between survivors and nonsurvivors. Results. Among the 121 enrolled patients (mean age, 62.2 ± 14.0 years; male, 82 (67.8%)), 41 (33.9%) survived, and the rest succumbed to death. The most frequent radiologic findings were ground-glass opacity (GGO) (71.9%) with peripheral (38.8%) and bilateral (98.3%) involvement, with lower lobes (94.2%) predominancy. The most common additional findings were cardiomegaly (63.6%), parenchymal band (47.9%), and crazy-paving pattern (44.4%). Univariable analysis of radiologic findings showed that cardiomegaly p : 0.04 , pleural effusion p : 0.02 , and pericardial effusion p : 0.03 were significantly more prevalent in nonsurvivors. However, the extension of pulmonary involvement was not significantly different between the two subgroups (11.4 ± 4.1 in survivors vs. 11.9 ± 5.1 in nonsurvivors, p : 0.59 ). Among nonradiologic factors, advanced age p : 0.002 , lower O2 saturation p : 0.01 , diastolic blood pressure p : 0.02 , and hypertension p : 0.03 were more commonly found in nonsurvivors. There was no significant difference between survivors and nonsurvivors in terms of laboratory findings. Three following factors remained significant in the backward logistic regression model: O2 saturation (OR: 0.91 (95% CI: 0.84–0.97), p : 0.006 ), pericardial effusion (6.56 (0.17–59.3), p : 0.09 ), and hypertension (4.11 (1.39–12.2), p : 0.01 ). This model had 78.7% sensitivity, 61.1% specificity, 90.0% positive predictive value, and 75.5% accuracy in predicting in-ICU mortality. Conclusion. A combination of underlying diseases, vital signs, and radiologic factors might have prognostic value for mortality rate prediction in ICU-admitted COVID-19 patients.

Background: It is increasingly common to collect and store specimens for future unspecified research. However, the effects of prolonged storage on the stability and quality of analytes in serum have not been well investigated. We aimed to determine whether the stability of liver enzymes extracted from frozen bio-samples stored at the baseline is affected by storage conditions. Methods: A total of four liver enzymes in the sera of 400 patients were examined following storage. After deter-mining the baseline measurements, the serum of each patient was aliquoted and stored at −70°C for three and six months, as well as one, two, and five years after collecting the original sample. The percent change from baseline measurements was calculated both statistically and clinically. Linear models were also used to correct the results of the samples based on the time they were frozen. Results: In almost all samples, liver enzymes were detectable until two years after the baseline, while in a signifi-cant proportion of samples, enzymes were not ultimately detectable five years after the baseline. Linear regression analysis on log-transformed levels of enzymes shows that the performance is acceptable until one year after the baseline. The performance of the prediction model declines substantially two and five years after the baseline, except for GGT. Conclusion: Long-term storage of serum samples significantly decreases the concentration of the liver enzymes from the baseline, except for GGT. It is not recommended to store samples for more than two years, as liver en-zymes are not detectable afterwards.