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Ischemic stroke is a disabling and fatal disease caused by the insufficient blood supply to the brain. Stellate ganglion block (SGB) is a type of anesthesia commonly used to relieve pain. Here, we sought to identify the effects of SGB on cerebral ischemia-reperfusion (I/R) injury. The middle cerebral artery occlusion (MCAO) model was established in rats. The brain injury was assessed using the 2,3,5-triphenyl-tetrazolium-chloride (TTC) staining assay and neurological score. Ferroptosis was analyzed by detecting cell death, Fe2+ content, glutathione (GSH), malonic dialdehyde (MDA), superoxide dismutase (SOD), and ferroptosis-related factors. The mechanisms of SGB were assessed using the western blot. The results showed that I/R increased brain infarction and damaged neurological function. SGB decreased I/R-induced infarction and improved neurological function. Meantime, SGB inhibited ferroptosis of the hippocampus induced by I/R via the Hippo pathway. and the Yes1 associated transcriptional regulator (YAP) of this pathway was positively correlated with the ferroptosis-related solute carrier family 7 member 11 (SLC7A11). Inhibition of the Hippo pathway reversed the effects of SGB on brain injury and ferroptosis. In conclusion, SGB inhibited ferroptosis of hippocampal neurons via activating the Hippo pathway and thereby alleviated I/R injury. The data provide a novel insight into the treatment of ischemic stroke and even other ischemic encephalopathies. [ABSTRACT FROM AUTHOR]

Epilepsy is the most common neurological disorder in humans. Electroencephalogram is a prevalent tool for diagnosing the epileptic seizure activity in clinical, which provides valuable information for understanding the physiological mechanisms behind epileptic disorders. Approximate entropy and recurrence quantification analysis are nonlinear analysis tools to quantify the complexity and recurrence behaviors of non-stationary signals, respectively. Convolutional neural networks are powerful class of models. In this paper, a new method for automatic epileptic electroencephalogram recordings based on the approximate entropy and recurrence quantification analysis combined with a convolutional neural network were proposed. The Bonn dataset was used to assess the proposed approach. The results indicated that the performance of the epileptic seizure detection by approximate entropy and recurrence quantification analysis is good (all of the sensitivities, specificities and accuracies are greater than 80%); especially the sensitivity, specificity and accuracy of the recurrence rate achieved 92.17%, 91.75% and 92.00%. When combines the approximate entropy and recurrence quantification analysis features with convolutional neural networks to automatically differentiate seizure electroencephalogram from normal recordings, the classification result can reach to 98.84%, 99.35% and 99.26%. Thus, this makes automatic detection of epileptic recordings become possible and it would be a valuable tool for the clinical diagnosis and treatment of epilepsy. [ABSTRACT FROM AUTHOR]