In monitoring the depth of anesthesia (DOA), the electroencephalography (EEG) signals of patients have been utilized during surgeries to diagnose their level of consciousness. Different entropy methods were applied to analyze the EEG signal and measure its complexity, such as spectral entropy, approximate entropy (ApEn) and sample entropy (SampEn). However, as a weak physiological signal, EEG is easily subject to interference from external sources such as the electric power, electric knives and other electrophysiological signal sources, which lead to a reduction in the accuracy of DOA determination. In this study, we adopt the multivariate empirical mode decomposition (MEMD) to decompose and reconstruct the EEG recorded from clinical surgeries according to its best performance among the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complementary EEMD (CEEMD) and the MEMD. Moreover, according to the comparison between SampEn and ApEn in measuring DOA, the SampEn is a practical and efficient method to monitor the DOA during surgeries at real time.

Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (S_E) on different scales. In this study, the adaptive resampling procedure is employed to replace the process of coarse-graining in MSE. According to the analysis of various signals and practical EEG signals, it is feasible to calculate the S_E from the adaptive resampled signals, and it has the highly similar results with the original MSE at small scales. The distribution of the MSE of EEG during the whole surgery based on adaptive resampling process is able to show the detailed variation of S_E in small scales and complexity of EEG, which could help anesthesiologists evaluate the status of patients.

The glutamatergic system may be involved in the effects of neuroprotectant therapies. Echinacoside, a phenylethanoid glycoside extracted from the medicinal Chinese herb Herba Cistanche, has neuroprotective effects. This study investigated the effects of echinacoside on 4-aminopyridine-evoked glutamate release in rat cerebrocortical nerve terminals (synaptosomes). Echinacoside inhibited Ca²⁺-dependent, but not Ca²⁺-independent, 4-aminopyridine-evoked glutamate release in a concentration-dependent manner. Echinacoside also reduced the 4-aminopyridine-evoked increase in cytoplasmic free Ca²⁺ concentration but did not alter the synaptosomal membrane potential. The inhibitory effect of echinacoside on 4-aminopyridine-evoked glutamate release was prevented by ω-conotoxin MVIIC, a wide-spectrum blocker of Cav2.2 (N-type) and Cav2.1 (P/Q-type) channels, but was insensitive to the intracellular Ca²⁺ release-inhibitors dantrolene and 7-chloro-5-(2-chloropheny)-1,5-dihydro-4,1-benzothiazepin-2(3H)-one (CGP37157). Furthermore, echinacoside decreased the 4-aminopyridine-induced phosphorylation of protein kinase C, and protein kinase C inhibitors abolished the effect of echinacoside on glutamate release. According to these results, we suggest that the inhibitory effect of echinacoside on evoked glutamate release is associated with reduced voltage-dependent Ca²⁺ entry and subsequent suppression of protein kinase C activity.

Cognitive impairment is not only associated with seizures but also reported as an adverse effect of antiepileptic drugs. Thus, new molecules that can ameliorate seizures and maintain satisfactory cognitive function should be developed. The antiepileptic potential of asiatic acid, a triterpene derived from the medicinal herb Centella asiatica, has already been demonstrated; however, its role in epilepsy-related cognitive deficits is yet to be determined. In this study, we evaluated the effects of asiatic acid on cognitive deficits in rats with kainic acid (KA)-induced seizure and explored the potential mechanisms underlying these effects. Our results revealed that asiatic acid administrated intraperitoneally 30 min prior to KA (15 mg/kg) injection ameliorated seizures and significantly improved KA-induced memory deficits, as demonstrated by the results of the Morris water maze test. In addition, asiatic acid ameliorated neuronal damage, inhibited calpain activation, and increased protein kinase B (AKT) activation in the hippocampus of KA-treated rats. Asiatic acid also increased the levels of synaptic proteins and the number of synaptic vesicles as well as attenuated mitochondrial morphology damage in the hippocampus of KA-treated rats. Furthermore, proteomic and Western blot analyses of hippocampal synaptosomes revealed that asiatic acid reversed KA-induced changes in mitochondria function-associated proteins, including lipoamide dehydrogenase, glutamate dehydrogenase 1 (GLUD1), ATP synthase (ATP5A), and mitochondrial deacetylase sirtuin-3 (SIRT3). Our data suggest that asiatic acid can prevent seizures and improve cognitive impairment in KA-treated rats by reducing hippocampal neuronal damage through the inhibition of calpain activation and the elevation of activated AKT, coupled with an increase in synaptic and mitochondrial function.

Silymarin, a polyphenoic flavonoid derived from the seeds of milk thistle (Silybum marianum), exhibits neuroprotective effects. In this study, we used a model of rat cerebrocortical synaptosomes to investigate whether silymarin affects the release of glutamate, an essential neurotransmitter involved in excitotoxicity. Its possible neuroprotective effect on a rat model of kainic acid (KA)-induced excitotoxicity was also investigated. In rat cortical synaptosomes, silymarin reduced glutamate release and calcium elevation evoked by the K⁺ channel blocker 4-aminopyridine but did not affect glutamate release caused by the Na⁺ channel activator veratridine or the synaptosomal membrane potential. Decreased glutamate release by silymarin was prevented by removal of extracellular calcium and blocking of N- and P/Q-type Ca²⁺ channel or extracellular signal-regulated kinase 1/2 (ERK1/2) but not by blocking of intracellular Ca²⁺ release. Immunoblotting assay results revealed that silymarin reduced 4-aminopyridine-induced phosphorylation of ERK1/2. Moreover, systemic treatment of rats with silymarin (50 or 100 mg/kg) 30 min before systemic KA (15 mg/kg) administration attenuated KA-induced seizures, glutamate concentration elevation, neuronal damage, glial activation, and heat shock protein 70 expression as well as upregulated KA-induced decrease in Akt phosphorylation in the rat hippocampus. Taken together, the present study demonstrated that silymarin depressed synaptosomal glutamate release by suppressing voltage-dependent Ca²⁺ entry and ERK1/2 activity and effectively prevented KA-induced in vivo excitotoxicity.

Background: Machine learning (ML) achieves better predictions of postoperative mortality than previous prediction tools. Free-text descriptions of the preoperative diagnosis and the planned procedure are available preoperatively. Because reading these descriptions helps anesthesiologists evaluate the risk of the surgery, we hypothesized that deep learning (DL) models with unstructured text could improve postoperative mortality prediction. However, it is challenging to extract meaningful concept embeddings from this unstructured clinical text. Objective: This study aims to develop a fusion DL model containing structured and unstructured features to predict the in-hospital 30-day postoperative mortality before surgery. ML models for predicting postoperative mortality using preoperative data with or without free clinical text were assessed. Methods: We retrospectively collected preoperative anesthesia assessments, surgical information, and discharge summaries of patients undergoing general and neuraxial anesthesia from electronic health records (EHRs) from 2016 to 2020. We first compared the deep neural network (DNN) with other models using the same input features to demonstrate effectiveness. Then, we combined the DNN model with bidirectional encoder representations from transformers (BERT) to extract information from clinical texts. The effects of adding text information on the model performance were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Statistical significance was evaluated using P<.05. Results: The final cohort contained 121,313 patients who underwent surgeries. A total of 1562 (1.29%) patients died within 30 days of surgery. Our BERT-DNN model achieved the highest AUROC (0.964, 95% CI 0.961-0.967) and AUPRC (0.336, 95% CI 0.276-0.402). The AUROC of the BERT-DNN was significantly higher compared to logistic regression (AUROC=0.952, 95% CI 0.949-0.955) and the American Society of Anesthesiologist Physical Status (ASAPS AUROC=0.892, 95% CI 0.887-0.896) but not significantly higher compared to the DNN (AUROC=0.959, 95% CI 0.956-0.962) and the random forest (AUROC=0.961, 95% CI 0.958-0.964). The AUPRC of the BERT-DNN was significantly higher compared to the DNN (AUPRC=0.319, 95% CI 0.260-0.384), the random forest (AUPRC=0.296, 95% CI 0.239-0.360), logistic regression (AUPRC=0.276, 95% CI 0.220-0.339), and the ASAPS (AUPRC=0.149, 95% CI 0.107-0.203). Conclusions: Our BERT-DNN model has an AUPRC significantly higher compared to previously proposed models using no text and an AUROC significantly higher compared to logistic regression and the ASAPS. This technique helps identify patients with higher risk from the surgical description text in EHRs.