Pan-immune-inflammation value predict delayed cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage.

Inflammatory reaction and immune dysregulation are known as components contributing to delayed cerebral ischemia (DCI) in patients with following aneurysmal subarachnoid hemorrhage (aSAH). The objective of this study was to investigate the role of pan-immune-inflammation value (PIV) as a novel comprehensive inflammatory marker in predicting the DCI development following aSAH. A total of 1028 participants with aSAH were enrolled. There were 296 patients with DCI and 732 patients without DCI. Various inflammatory markers were analyzed using peripheral blood sample obtained at admission. Receiver operating characteristic (ROC) analysis was performed to identify the optimal cutoff value of PIV for distinguishing DCI. Multivariate analysis was used to determine independent predictors for DCI. Mean PIV was significantly higher in the DCI (+) group than in the DCI (-) group (437.6 ± 214.7 vs 242.1 ± 154.7, P = 0.007). In ROC analysis, the optimal cutoff value of PIV was 356.7 for predicting DCI (area under the curve [AUC] 0.772, 95 % confidence interval [CI] 0.718-0.816; P < 0.001). Multivariate analysis showed that high Hunt-Hess grade (odds ratio [OR] 1.70, 95 % CI 1.38-2.22; P = 0.007), thick SAH (OR 1.82, 95 % CI 1.44-2.32; P = 0.005), and elevated PIV (≥356.7) (OR 1.42, 95 % CI 1.10-1.74; P = 0.013) were independent predictors of DCI after aSAH. PIV is a potent predictor of DCI in patients with aSAH. Elevated PIV is associated with more DCI development. Thus, PIV has predictive value for DCI development.

Tracking blobs in the turbulent edge plasma of a tokamak fusion device.

The analysis of turbulence in plasmas is fundamental in fusion research. Despite extensive progress in theoretical modeling in the past 15 years, we still lack a complete and consistent understanding of turbulence in magnetic confinement devices, such as tokamaks. Experimental studies are challenging due to the diverse processes that drive the high-speed dynamics of turbulent phenomena. This work presents a novel application of motion tracking to identify and track turbulent filaments in fusion plasmas, called blobs, in a high-frequency video obtained from Gas Puff Imaging diagnostics. We compare four baseline methods (RAFT, Mask R-CNN, GMA, and Flow Walk) trained on synthetic data and then test on synthetic and real-world data obtained from plasmas in the Tokamak à Configuration Variable (TCV). The blob regime identified from an analysis of blob trajectories agrees with state-of-the-art conditional averaging methods for each of the baseline methods employed, giving confidence in the accuracy of these techniques. By making a dataset and benchmark publicly available, we aim to lower the entry barrier to tokamak plasma research, thereby greatly broadening the community of scientists and engineers who might apply their talents to this endeavor.