Objectives
The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results.
Methods
We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription–polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil.
Results
The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study’s data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%).
Conclusions
ML models presented in this study can be used as clinical decision support tools to contribute to physicians’ clinical judgment for COVID-19 diagnoses.
ObjectivesThe aim of this study is to investigate the effects of ovariectomy on bone mineral density (BMD) and oxidative state in rats, and the alterations in these effects that vitamin C supplementation may produce.Materials and methodsTwenty female Wistar albino rats were randomly divided into three groups: control (C, n=6); ovariectomy (O, n=7); and ovariectomy+vitamin C supplement (OV, n=7). Oxidative stress (OS) was assessed 100 days postovariectomy by measuring the activity of several enzymes, including catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase, as well as the concentrations of malondialdehyde (MDA), nitric oxide (NO), and total sulfhydryl groups in plasma and bone homogenates.ResultsA significant decrease in BMD was observed in O group compared with C group (p=0.015), and a significant increase was observed in OV compared with O group (p=0.003). When groups were compared with respect to parameters of OS, MDA and NO levels in bone tissue were significantly higher in O than in C (p=0.032, p=0.022) and were significantly lower in OV than in O (p=0.025, p=0.018). SOD activity was significantly higher in O than in C (p=0.032). In plasma, MDA activity was significantly higher in O than in C (p=0.022) and NO level was significantly higher in O than in C and OV (p=0.017, p=0.018).ConclusionsOur results suggest that ovariectomy may produce osteoporosis and OS in females, and vitamin C supplementation may provide alterations regarding improvement in OS and BMD values. We assume that studies including more subjects are needed to make a decisive conclusion about OS–BMD relation.
Our results indicate that increased prolidase seems to be related to increased oxidative stress along with decreased antioxidant levels in renal cancer.
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