Predicting abnormal C-reactive protein level for improving utilization by deep neural network model.

Background: C-reactive protein (CRP) is an inflammatory biomarker frequently used in clinical practice. However, insufficient evidence-based ordering inevitably results in its overuse or underuse. This study aims to predict its normal and abnormal levels using the deep neural network (DNN) models, helping clinicians order this item more appropriately and intelligently.

Predicting thalassemia using deep neural network based on red blood cell indices.

Background And Objective: The traditional statistical screening method for thalassemia based on red blood cell (RBC) indices is being replaced by machine learning. Here, we developed deep neural networks (DNNs) that outperformed the traditional method for predicting thalassemia.

Method: Using a dataset of 8693 records comprising genetic tests and other 11 features we constructed 11 DNN models and 4 traditional statistical models and then compared their performances and analysed feature importance for interpreting DNN models.

The effect of dapagliflozin on myocardial ischemia-reperfusion injury in diabetic rats.

The incidence of ischemic heart disease is 2-3 times higher in diabetic patients. However, the effect of dapagliflozin on ischemia-reperfusion myocardial injury in diabetic rats has not been studied. We examined the effects of dapagliflozin on myocardial IR injury in streptozotocin-nicotinamide-induced diabetic rats.