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https://www.selleckchem.com/products/af353.html Current multimodal approaches for the prognostication of out-of-hospital cardiac arrest (OHCA) are based mainly on the prediction of poor neurological outcomes; however, it is challenging to identify patients expected to have a favorable outcome, especially before the return of spontaneous circulation (ROSC). We developed and validated a machine learning-based system to predict good outcome in OHCA patients before ROSC. This prospective, multicenter, registry-based study analyzed non-traumatic OHCA data collected between October 2015 and June 2017. We used information available before ROSC as predictor variables, and the primary outcome was neurologically intact survival at discharge, defined as cerebral performance category 1 or 2. The developed models' robustness were evaluated and compared with various score metrics to confirm their performance. The model using a voting classifier had the best performance in predicting good neurological outcome (area under the curve = 0.926). We confirmed that the six top-weighted variables predicting neurological outcomes, such as several duration variables after the instant of OHCA and several electrocardiogram variables in the voting classifier model, showed significant differences between the two neurological outcome groups. These findings demonstrate the potential utility of a machine learning model to predict good neurological outcome of OHCA patients before ROSC.Background and Objectives In patients with diabetes mellitus (DM), the neural retina is starting to degenerate before the development of vascular lesions. Our purpose was to investigate the correlation between the retinal arterial morphometric parameters and structural neurodegeneration in patients with type 2 DM with no or mild diabetic retinopathy (DR). Materials and Methods This is a prospective study including 53 eyes of patients with type 2 DM and 32 eyes of healthy controls. Based on SD-OCT (spectral domain-opti

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