Attack-resilient fault detection for interconnected systems under DoS attack.

In light of the expanding cyber-space applications, the imperative consideration of cyber-attack ramifications on system security is evident. This paper presents a resilient dynamic event-triggered fault detection scheme for a class of nonlinear interconnected systems subjected to denial of service (DoS) attacks. To counteract multifaceted threats, the co-design challenge involving switched-type fault detection filters and a resilient dynamic event-triggered transmission mechanism is addressed. In the design phase of the filters, the frequency information of the signal is considered comprehensively and linear solvable conditions ensuring desired augment system performance are delineated. Through a series of comparative simulation experiments, the findings support the conclusion that the proposed attack-tolerant fault detection mechanism not only conserves network resources but also demonstrates superior detection capabilities for specific frequency fault signals.

Potential CSF biomarkers of postpartum depression following delivery via caesarian section.

BACKGROUND: Postpartum depression (PPD), the depressive episodes following delivery, is a serious and frequent psychiatric disorder. While numerous screening tools existed for depressive episodes, e.g., the Edinburgh Postnatal Depression Scale (EPDS), there are no objective biological measures for predicting PPD. Despite several studies done to identify biomarkers in PPD, there has been limited exploration into cerebrospinal fluid (CSF) which directly interfaces with the brain. Consequently, novel potential biomarkers of CSF are required to predict PPD, so as to target specific preventive interventions. METHODS: Seventy-five parturients undergoing caesarean delivery were enrolled for CSF collection at delivery. Of the twenty-eight subjects who didn't meet any exclusion criteria, the number of the healthy parturients whose score of EPDS 6-weeks postpartum (6-wpp) < 5 and PPD patients whose EPDS 6-wpp ≥ 13 was ten respectively. Gas chromatography-mass spectrometry (GC-MS) analysis of CSF was used for metabolomic assessments. RESULTS: We found that capric acid, dodecanoic acid, arachidic acid and behenic acid in CSF were significantly negatively correlated with PPD symptoms, meanwhile L-tryptophan had an obvious positive correlation. Moreover, these five biomarkers can be used as effective predictive biomarkers for PPD. LIMITATIONS: The main limitations are the inclusion of only parturients who underwent caesarean sections and a small sample size. CONCLUSIONS: This study innovatively investigated potential predictive biomarkers of PPD before the onset through intrapartum maternal CSF metabolomics, which offered a more objective approach to predict and diagnose PPD, leading to help identify high-risk parturients for early initiation of secondary prevention to reduce global PPD burden.

FDNet: An end-to-end fusion decomposition network for infrared and visible images.

Infrared and visible image fusion can generate a fusion image with clear texture and prominent goals under extreme conditions. This capability is important for all-day climate detection and other tasks. However, most existing fusion methods for extracting features from infrared and visible images are based on convolutional neural networks (CNNs). These methods often fail to make full use of the salient objects and texture features in the raw image, leading to problems such as insufficient texture details and low contrast in the fused images. To this end, we propose an unsupervised end-to-end Fusion Decomposition Network (FDNet) for infrared and visible image fusion. Firstly, we construct a fusion network that extracts gradient and intensity information from raw images, using multi-scale layers, depthwise separable convolution, and improved convolution block attention module (I-CBAM). Secondly, as the FDNet network is based on the gradient and intensity information of the image for feature extraction, gradient and intensity loss are designed accordingly. Intensity loss adopts the improved Frobenius norm to adjust the weighing values between the fused image and the two raw to select more effective information. The gradient loss introduces an adaptive weight block that determines the optimized objective based on the richness of texture information at the pixel scale, ultimately guiding the fused image to generate more abundant texture information. Finally, we design a single and dual channel convolutional layer decomposition network, which keeps the decomposed image as possible with the input raw image, forcing the fused image to contain richer detail information. Compared with various other representative image fusion methods, our proposed method not only has good subjective vision, but also achieves advanced fusion performance in objective evaluation.

Identification of potential hub genes linked to immune and metabolic alterations in postoperative systemic inflammatory dysregulation.

Background: Postoperative systemic inflammatory dysregulation (PSID) is characterised by strongly interlinked immune and metabolic abnormalities. However, the hub genes responsible for the interconnections between these two systemic alterations remain to be identified. Methods: We analysed differentially expressed genes (DEGs) of individual peripheral blood nucleated cells in patients with PSID (n = 21, CRP > 250 mg/L) and control patients (n = 25, CRP < 75 mg/L) following major abdominal surgery, along with their biological functions. Correlation analyses were conducted to explore the interconnections of immune-related DEGs (irDEGs) and metabolism-related DEGs (mrDEGs). Two methods were used to screen hub genes for irDEGs and mrDEGs: we screened for hub genes among DEGs via 12 algorithms using CytoHubba in Cytoscape, and also screened for hub immune-related and metabolic-related genes using weighted gene co-expression network analysis. The hub genes selected were involved in the interaction between changes in immunity and metabolism in PSID. Finally, we validated our results in mice with PSID to confirm the findings. Results: We identified 512 upregulated and 254 downregulated DEGs in patients with PSID compared with controls. Gene enrichment analysis revealed that DEGs were significantly associated with immune- and metabolism-related biological processes and pathways. Correlation analyses revealed a close association between irDEGs and mrDEGs. Fourteen unique hub genes were identified via 12 screening algorithms using CytoHubba in Cytoscape and via weighted gene co-expression network analysis. Among these, CD28, CD40LG, MAPK14, and S100A12 were identified as hub genes among both immune- and metabolism-related genes; these genes play a critical role in the interaction between alterations in immunity and metabolism in PSID. The experimental results also showed that the expression of these genes was significantly altered in PSID mice. Conclusion: This study identified hub genes associated with immune and metabolic alterations in patients with PSID and hub genes that link these alterations. These findings provide novel insights into the mechanisms underlying immune and metabolic interactions and new targets for clinical treatment can be proposed on this basis.

TiN/Ti3C2 heterojunction-based photonic device for optical Kerr switch.

As one of the new nanomaterials, TiN/Ti3C2 shows excellent optoelectronic characteristics, thus it has been widely used in many applications, such as biomedicine, optical sensors, image processing, and optical switching. With the advancement of communication capabilities and communication networks, optical fiber communication has put a higher demand on signal processing. In order to overcome the limitations of the electronic transfer rate bottleneck, the concept of all-optical signal processing has been proposed. Utilizing the excellent optical nonlinear effect of the TiN/Ti3C2 heterojunction-coated microfiber (THM), a novel THM-based optical Kerr switch has been proposed. Injecting a strong control light and a signal light into the device simultaneously, and controlling the state of turn on or off of the control light, can adjust the intensity of the signal light. Based on this, the amplitude modulation of the signal light can be achieved. With a control light power of 200 mW, the maximum extinction ratio of the signal light reaches 27 dB. We believe that this type of compact device can demonstrate great potential for integration with current high-speed fiber communication networks, providing a possible method for all-optical signal processing through nonlinear effects, and has broad prospects in the field of all-optical signal processing, robots, and high-speed communication.

Alterations in smell or taste in individuals infected with SARS-CoV-2 during periods of Omicron variant dominance.

OBJECTIVES: To characterize the prevalence, severity, correlation with initial symptoms, and role of vaccination in patients with COVID-19 with smell or taste alterations (STAs). METHODS: We conducted an observational study of patients infected with SARS-CoV-2 Omicron admitted to three hospitals between May 17 and June 16, 2022. The olfactory and gustatory functions were evaluated using the taste and smell survey and the numerical visual analog scale at two time points. RESULTS: The T1 and T2 time point assessments were completed by 688 and 385 participants, respectively. The prevalence of STAs at two time points was 41.3% vs 42.6%. Furthermore, no difference existed in the severity distribution of taste and smell survey, smell, or taste visual analog scale scores between the groups. Patients with initial symptoms of headache (P = 0.03) and muscle pain (P = 0.04) were more likely to develop STAs, whereas higher education; three-dose vaccination; no symptoms yet; or initial symptoms of cough, throat discomfort, and fever demonstrated protective effects, and the results were statistically significant. CONCLUSION: The prevalence of STAs did not decrease significantly during the Omicron dominance, but the severity was reduced, and vaccination demonstrated a protective effect. In addition, the findings suggest that the presence of STAs is likely to be an important indicator of viral invasion of the nervous system.

Predictor-Based Neural Dynamic Surface Control for Strict-Feedback Nonlinear Systems With Unknown Control Gains.

Neural dynamic surface control (NDSC) is an effective technique for the tracking control of nonlinear systems. The objective of this article is to improve closed-loop transient performance and reduce the number of learning parameters for a strict-feedback nonlinear system with unknown control gains. For this purpose, a predictor-based NDSC (PNDSC) approach is presented. It introduces Nussbaum functions and predictors into the traditional NDSC for nonlinear systems with unknown control gains. Unlike NDSC that uses surface errors to update the learning parameters of neural networks (NNs), the PNDSC employs prediction errors for the same purpose, leading to improved transient performance of closed-loop control systems. To reduce the number of learning parameters, the PNDSC is further embedded with the technique of the minimal number of learning parameters (MNLPs). This avoids the problem of the "explosion of learning parameters" as the order of the system increases. A Lyapunov-based stability analysis shows that all signals are bounded in the closed-loop systems under PNDSC embedded with MNLPs. Simulations are conducted to demonstrate the effectiveness of the PNDSC approach presented in this article.

Nomogram to predict the incidence of delirium in elderly patients with non-severe SARS-CoV-2 infection.

Objective: To construct and validate nomogram models that predict the incidence of delirium in elderly patients with non-severe SARS-CoV-2 infection. Methods: Elderly patients (≥65y) tested positive for SARS-CoV-2 infection at the hospital were included. We used the 3-min diagnostic Confusion Assessment Method for delirium diagnosis. Least absolute shrinkage and selection operator (LASSO) logistical regression analysis was performed to explore potential independent influencing factors of delirium. A predict model visualized by nomogram was constructed based on the confirmed variables. The predictive accuracy and clinical value of the model were evaluated using receiver operating characteristic (ROC) curves. Results: The data of 311 elderly patients were analyzed, of whom 73 (23.47%) patients were diagnosed with delirium. Three independent influencing factors of delirium were confirmed: age (OR1.16,1.11-1.22), Glomerular filtration rate (OR 0.98,0.97-0.99), platelet-large cell ratio (1.06,1.02-1.10). These parameters were used to create a nomogram to predict the development of delirium, which showed good predictive accuracy confirmed by the ROC curves (AUC 0.82,0.76-0.88). Conclusion: We construct a credible nomogram to predict the development of delirium in elderly patients with Non-severe SARS-CoV-2 infection. Our finding may be useful to physicians in early prevention and treatment of delirium.

Growth modes of single-walled carbon nanotubes on catalysts.

Understanding the growth mechanism of single-walled carbon nanotubes (SWCNTs) and achieving selective growth requires insights into the catalyst structure-function relationship. Using an in situ aberration-corrected environmental transmission electron microscope, we reveal the effects of the state and structure of catalysts on the growth modes of SWCNTs. SWCNTs grown from molten catalysts via a vapor-liquid-solid process generally present similar diameters to those of the catalysts, indicating a size correlation between nanotubes and catalysts. However, SWCNTs grown from solid catalysts via a vapor-solid-solid process always have smaller diameters than the catalysts, namely, an independent relationship between their sizes. The diameter distribution of SWCNTs grown from crystalline Co7W6, which has a unique atomic arrangement, is discrete. In contrast, nanotubes obtained from crystalline Co are randomly dispersed. The different growth modes are linked to the distinct chiral selectivity of SWCNTs grown on intermetallic and monometallic catalysts. These findings will enable rational design of catalysts for chirality-controlled SWCNTs growth.

Aspect-Aware Graph Attention Network for Heterogeneous Information Networks.

Graph Convolutional Networks (GCNs) derive inspiration from recent advances in computer vision, by stacking layers of first-order filters followed by a nonlinear activation function to learn entity or graph embeddings. Although GCNs have been shown to boost the performance of many network analysis tasks, they still face tremendous challenges in learning from Heterogeneous Information Networks (HINs), where relations play a decisive role in knowledge reasoning. What's more, there are multiaspect representations of entities in HINs, and a filter learned in one aspect do not necessarily apply to another. We address these challenges by proposing the Aspect-Aware Graph Attention Network (AGAT), a model that extends GCNs with alternative learnable filters to incorporate entity and relational information. Instead of focusing on learning the general entity embeddings, AGAT learns the adaptive entity embeddings based on prediction scenario. Experiments of link prediction and semi-supervised classification verify the effectiveness of our algorithm.

Nanoantidote for repression of acidosis pH promoting COVID-19 infection.

Acidosis, such as respiratory acidosis and metabolic acidosis, can be induced by coronavirus disease 2019 (COVID-19) infection and is associated with increased mortality in critically ill COVID-19 patients. It remains unclear whether acidosis further promotes SARS-CoV-2 infection in patients, making virus removal difficult. For antacid therapy, sodium bicarbonate poses great risks caused by sodium overload, bicarbonate side effects, and hypocalcemia. Therefore, new antacid antidote is urgently needed. Our study showed that an acidosis-related pH of 6.8 increases SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2) expression on the cell membrane by regulating intracellular microfilament polymerization, promoting SARS-CoV-2 pseudovirus infection. Based on this, we synthesized polyglutamic acid-PEG materials, used complexation of calcium ions and carboxyl groups to form the core, and adopted biomineralization methods to form a calcium carbonate nanoparticles (CaCO3-NPs) nanoantidote to neutralize excess hydrogen ions (H+), and restored the pH from 6.8 to approximately 7.4 (normal blood pH). CaCO3-NPs effectively prevented the heightened SARS-CoV-2 infection efficiency due to pH 6.8. Our study reveals that acidosis-related pH promotes SARS-CoV-2 infection, which suggests the existence of a positive feedback loop in which SARS-CoV-2 infection-induced acidosis enhances SARS-CoV-2 infection. Therefore, antacid therapy for acidosis COVID-19 patients is necessary. CaCO3-NPs may become an effective antacid nanoantidote superior to sodium bicarbonate.

Corrigendum: ATPase Inhibitory Factor 1 Is Critical for Regulating Sevoflurane-Induced Microglial Inflammatory Responses and Caspase-3 Activation.

[This corrects the article DOI: 10.3389/fncel.2021.770666.].

Fentanyl induces autism-like behaviours in mice by hypermethylation of the glutamate receptor gene Grin2b.

BACKGROUND: Environmental factors contribute to autism spectrum disorder. Fentanyl, one of the most widely used opioid analgesics in anaesthesia, can induce neurotoxicity, but its role in autism remains unknown. We determined whether fentanyl induced autism-like behaviours in young mice and the underlying mechanisms. METHODS: Young male and female mice received fentanyl at postnatal days 6, 8, and 10, and performed behavioural tests, including three-chamber social preference, elevated plus maze, grooming behaviour, and open-field test, from postnatal days 30-32. Expression of Grin2b, the gene encoding the GluN2B subunit of the N-methyl-d-aspartate receptor, was assessed in the anterior cingulate cortex of male mice using fluorescence in situ hybridisation histochemistry. We used bisulfite target sequencing to determine Grin2b hypermethylation sites after fentanyl treatment. In the specific activation and rescue experiments, we injected the mu opioid receptor agonist [D-Ala,2 N-MePhe,4 Gly-ol]-enkephalin (DAMGO) or Grin2b overexpression lentivirus into the anterior cingulate cortex of male mice. RESULTS: Fentanyl induced autism-like behaviours in both young male and female mice, and downregulated Grin2b expression (0.49-fold [0.08] vs 1.00-fold [0.09]; P<0.01) and GluN2B protein amounts (0.38-fold [0.07] vs 1.00-fold [0.12]; P<0.01) in the anterior cingulate cortex through hypermethylation of Grin2b. The mu-opioid receptor antagonist naloxone and overexpression of Grin2b in anterior cingulate cortex attenuated the fentanyl-induced effects, whereas DAMGO injection into the anterior cingulate cortex induced autism-like behaviours. CONCLUSIONS: These data suggest that fentanyl induces autism-like behaviours in young mice via an epigenetic mechanism. Further research is required to determine possible clinical relevance to autism risk.

Secure predictor-based neural dynamic surface control of nonlinear cyber-physical systems against sensor and actuator attacks.

This paper addresses a secure predictor-based neural dynamic surface control (SPNDSC) issue for a cyber-physical system in a nontriangular form suffering from both sensor and actuator deception attacks. To avoid the algebraic loop problem, only partial states are employed as input vectors of neural networks (NNs) for approximating unknown dynamics, and compensation terms are further developed to offset approximation errors from NNs. With introduction of nonlinear gain functions and attack compensators, adverse effects of an intelligent adversary are alleviated effectively. Furthermore, we present stability analysis and prove the ultimate boundedness of all signals in the closed-loop system. The effectiveness of the proposed control strategy is illustrated by two examples.

Predictor-Based Neural Dynamic Surface Control for Bipartite Tracking of a Class of Nonlinear Multiagent Systems.

This article is concerned with bipartite tracking for a class of nonlinear multiagent systems under a signed directed graph, where the followers are with unknown virtual control gains. In the predictor-based neural dynamic surface control (NDSC) framework, a bipartite tracking control strategy is proposed by the introduction of predictors and the minimal number of learning parameters (MNLPs) technology along with the graph theory. Different from the traditional NDSC, the predictor-based NDSC utilizes prediction errors to update the neural network for improving system transient performance. The MNLPs technology is employed to avoid the problem of "explosion of learning parameters". It is proved that all closed-loop signals steered by the proposed control strategy are bounded, and the system achieves bipartite consensus. Simulation results verify the efficiency and effectiveness of the strategy.

TriATNE: Tripartite Adversarial Training for Network Embeddings.

Existing network embedding algorithms based on generative adversarial networks (GANs) improve the robustness of node embeddings by selecting high-quality negative samples with the generator to play against the discriminator. Since most of the negative samples can be easily discriminated from positive samples in graphs, their poor competitiveness weakens the function of the generator. Inspired by the sales skills in the market, in this article, we present tripartite adversarial training for network embeddings (TriATNE), a novel adversarial learning framework for learning stable and robust node embeddings. TriATNE consists of three players: 1) producer; 2) seller; and 3) customer. The producer strives to learn the representation of each sample (node pair), making it easy for the customer to differentiate between the positive and the negative, while the seller tries to confuse the customer by selecting realistic-looking samples. The customer, a biased evaluation metric, provides feedback for training the producer and the seller. To further enhance the robustness of node embedding, we model the customer as a two-layer neural network, where each unit in the hidden layer can be regarded as a customer with different preferences. TriATNE also plays against the producer by adjusting the weight of each customer. We test the performance of TriATNE on two common tasks: classification as well as link prediction. The experimental results on various publicly available datasets show that TriATNE can exploit the network structure well.

Correlation analysis between TSP2, MMP-9 and perihematoma edema, as well as the short-term prognosis of patients with hypertensive intracerebral hemorrhage.

BACKGROUND: To analyze the correlation between thrombospondin-2 (TSP2), matrix metalloproteinase (MMP)-9, and perihematomal edema, as well as the short-term prognosis of patients with hypertensive intracerebral hemorrhage. METHODS: The clinical data of 114 patients with hypertensive intracerebral hemorrhage admitted to our hospital from January 2018 to February 2020 were collected and divided into groups according to the levels of TSP2 and MMP-9. We compared edema indexes in patients with different levels of TSP2 and MMP-9, and analyzed the correlation between TSP2, MMP-9 and relative edema volume index (REI), edema change index (AEI). We also assessed the TSP2 and MMP-9 levels in patients with different prognoses, and analyzed the predictive value of TSP2 and MMP-9 for poor prognosis of patients. RESULTS: (I) There was no difference in the REI and AEI values between the low and high TSP2 groups at admission and 24 h after admission (P>0.05), while the REI and AEI values of the high TSP2 group at 5 and 15 d after admission were significantly lower than those of the low TSP2 group (P<0.05); (II) the REI and AEI values of patients with different MMP-9 levels were not different between admission and 24 h after admission (P>0.05), while the REI and AEI values of the high MMP-9 group were significantly higher than those of the low MMP-9 group at 5 and 15 d after admission (P<0.05); (III) Pearson correlation analysis showed that MMP-9 was positively correlated with REI and AEI, while TSP2 was negatively correlated (P<0.05); (IV) among 114 patients, 39 had poor prognosis, 75 had good prognosis The MMP-9 levels of patients with a poor prognosis were significantly higher than those of patients with a good prognosis, and the TSP2 level was the opposite (P<0.05); (V) receiver operating characteristic (ROC) curve showed that the sensitivity, specificity and the area under the curve (AUC) of the TSP2 + MMP-9 combination in the diagnosis of hypertensive cerebral hemorrhage were significantly higher than when TSP2 and MMP-9 were tested separately (P<0.05). CONCLUSIONS: In patients with hypertensive intracerebral hemorrhage, TSP2 is negatively correlated with edema around the hematoma, while MMP-9 is positively correlated.

Systematic review and meta-analysis of the effect of continuous cerebrospinal fluid drainage on keyhole surgery during the perioperative period.

BACKGROUND: Lumbar continuous drainage of fluid (LCDF) has become more widely used in the diagnosis and treatment of neurological diseases in recent years. The use of LCDF can enable a better understanding of the patient's condition and reduce the incidence of related complications. LCDF can also affect complications of perforation surgery, including mortality during hospitalization, cerebral vasospasm (CVS), bleeding, and aneurysmal subarachnoid hemorrhage (aSAH). METHODS: Articles published from library construction to April 2021 were searched for in the English-language databases PubMed, Cochrane Library, and Embase. All randomized controlled trials (RCTs) with LCDF and hole locking surgery were meta-analyzed using the Cochrane Collaboration's RevMan 5.3 software. RESULTS: Ten RCTs involving 1,092 patients (continuous drainage group, n=585; control group, n=507) were included in the meta-analysis. For the statistical different in incidence of perioperative cerebral infarction in the two groups, the odds ratio (OR) was 5.42 [95% confidence interval (CI): (2.71, 10.83); P<0.00001], and for the statistical difference in the incidence of cerebral hemorrhage, the OR was 4.76 [95% CI: (2.11, 10.76); P=0.0002]. Perioperative complications were fewer in the LCDF-treated drainage group than in the conventional group. DISCUSSION: This meta-analysis of 10 RCTs confirmed that LCDF compared with other treatments is associated with a lower incidence of perioperative complications, such as cerebral hemorrhage, hydrocephalus, and cerebral infarction, as well as increased Glasgow Outcome Scale (GOS).

Anesthetic Propofol Promotes Tumor Metastasis in Lungs via GABAA R-Dependent TRIM21 Modulation of Src Expression.

Generation of circulating tumor cells (CTCs), a key step in tumor metastasis, occurs during surgical tumor resection, often performed under general anesthesia. Propofol is the commonly used anesthetic, but its effects on CTCs and tumor metastasis remain largely unknown. Propofol effects are investigated in an experimental metastasis model by injecting tumor cells and, subsequently, low- or standard-dose propofol to nude mice through tail vein. Propofol- or vehicle-treated tumor cells are also injected to the mice. An in vitro tumor cell-vascular endothelial cell adhesion assay, immunofluorescence, and other methods are employed to assess how propofol affects tumor cell adhesion and extension. Propofol induces more lung tumor metastasis in mice than control. Mechanistically, propofol enhances tumor cell adhesion and extension through GABAA R to downregulate TRIM21 expression, leading to upregulation of Src, a protein associated with cell adhesion. These results demonstrate that propofol may promote tumor metastasis through GABAA R-TRIM21-Src mechanism.

ATPase Inhibitory Factor 1 Is Critical for Regulating Sevoflurane-Induced Microglial Inflammatory Responses and Caspase-3 Activation.

Postoperative delirium (POD) is one of the most important complications after surgery with general anesthesia, for which the neurotoxicity of general anesthetics is a high-risk factor. However, the mechanism remains largely unknown, which also hinders the effective treatment of POD. Here, we confirmed that a clinical concentration of the general anesthetic sevoflurane increased the expression of inflammatory factors and activated the caspase-3 by upregulating ATPase inhibitory factor 1 (ATPIF1) expression in microglia. Upregulation of ATPIF1 decreased the synthesis of ATP which is an important signaling molecule secreted by microglia. Extracellular supplementation with ATP attenuated the microglial inflammatory response and caspase-3 activation caused by sevoflurane or overexpression of ATPIF1. Additionally, the microglial inflammatory response further upregulated ATPIF1 expression, resulting in a positive feedback loop. Animal experiments further indicated that intraperitoneal injection of ATP significantly alleviated sevoflurane anesthesia-induced POD-related anxiety behavior and memory damage in mice. This study reveals that ATPIF1, an important protein regulating ATP synthesis, mediates sevoflurane-induced neurotoxicity in microglia. ATP supplementation may be a potential clinical treatment to alleviate sevoflurane-induced POD.