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Drugs administered for anesthesia can trigger seizure attacks in patients with epilepsy. Benzodiazepines have been consistently reported to be anticonvulsants, and a novel benzodiazepine, remimazolam, was recently introduced. We report a case of total intravenous anesthesia maintained with remimazolam in a patient with epilepsy who underwent deep brain stimulation of both anterior thalamic nuclei. Despite the administration of multiple anti-epileptic drugs, no tolerance to remimazolam was observed. Perioperative seizures were also not observed. Remimazolam can be considered the anesthetic of choice in patients with epilepsy.

This paper proposes a deep recurrent neural network (RNN)-based traffic classification scheme (deep RNN-TCS) for classifying applications from traffic patterns in a hybrid edge computing and cloud computing architecture.We can also classify traffic from a cloud server, but there will be a time delay when packets transfer to the server. Therefore, the traffic classification is possible almost in realtime when it performed on edge computing nodes. However, training takes a lot of time and needs a lot of computing resources to learn traffic patterns. Therefore, it is efficient to perform training on cloud server and to perform serving on edge computing node. Here, a cloud server collects and stores output labels corresponding to the application packets. Then, it trains those data and generates inferred functions. An edge computation node receives the inferred functions and executes classification. Compared to deep packet inspection (DPI), which requires the periodic verification of existing signatures and updated application information (e.g., versions adding new features), the proposed scheme can classify the applications in an automated manner. Also, deep learning can automatically make classifiers for traffic classification when there is enough data. Specifically, input features and output labels are defined for classification as traffic packets and target applications, respectively, which are created as two-dimensional images. As our training data, traffic packets measured at Universitat Politecnica de Catalunya Barcelonatech were utilized. Accordingly, the proposed deep RNN-TCS is implemented using a deep long short-term memory system. Through extensive simulation-based experiments, it is verified that the proposed deep RNN-TCS achieves almost 5% improvement in accuracy (96% accuracy) while operating 500 times faster (elapsed time) compared to the conventional scheme.

Edge-cloud computing is an emerging approach in which tasks are offloaded from mobile devices to edge or cloud servers. However, Task offloading may result in increased energy consumption and delays, and the decision to offload the task is dependent on various factors such as time-varying radio channels, available computation resources, and the location of devices. As edge-cloud computing is a dynamic and resource-constrained environment, making optimal offloading decisions is a challenging task. This paper aims to optimize offloading and resource allocation to minimize delay and meet computation and communication needs in edge-cloud computing. Theproblem of optimizing task offloading in the edge-cloud computing environment is a multi-objective problem, for which we employ deep reinforcement learning to find the optimal solution. To accomplish this, we formulate the problem as a Markov decision process and use a Double Deep Q-Network (DDQN) algorithm. Our DDQN-edge-cloud (DDQNEC) scheme dynamically makes offloading decisions by analyzing resource utilization, task constraints, and curret status of the edge-cloud network.. Simulation results demonstrate that DDQNEC outperforms heuristic approaches in terms of resource utilization, task offloading, and task rejection.

Although arginase primarily participates in the last reaction of the urea cycle, we have previously demonstrated that arginase II is an important cytosolic calcium regulator through spermine production in a p32-dependent manner. Here, we demonstrated that rhaponticin (RPT) is a novel medicinal-plant arginase inhibitor and investigated its mechanism of action on Ca2+-dependent endothelial nitric oxide synthase (eNOS) activation. RPT was uncompetitively inhibited for both arginases I and II prepared from mouse liver and kidney. It also inhibited arginase activity in both aorta and human umbilical vein endothelial cells (HUVECs). Using both microscope and FACS analyses, RPT treatments induced increases in cytosolic Ca2+ levels using Fluo-4 AM as a calcium indicator. Increased cytosolic Ca2+ elicited the phosphorylations of both CaMKII and eNOS Ser1177 in a time-dependent manner. RPT incubations also increased intracellular L-arginine (L-Arg) levels and activated the CaMKII/AMPK/Akt/eNOS signaling cascade in HUVECs. Treatment of L-Arg and ABH, arginase inhibitor, increased intracellular Ca2+ concentrations and activated CaMKII-dependent eNOS activation in ECs of WT mice, but, the effects were not observed in ECs of inositol triphosphate receptor type 1 knockout (IP3R1-/-) mice. In the aortic endothelium of WT mice, RPT also augmented nitric oxide (NO) production and attenuated reactive oxygen species (ROS) generation. In a vascular tension assay using RPT-treated aortic tissue, cumulative vasorelaxant responses to acetylcholine (Ach) were enhanced, and phenylephrine (PE)-dependent vasoconstrictive responses were retarded, although sodium nitroprusside and KCl responses were not different. In this study, we present a novel mechanism for RPT, as an arginase inhibitor, to increase cytosolic Ca2+ concentration in a L-Arg-dependent manner and enhance endothelial function through eNOS activation. [BMB Reports 2021; 54(10): 516-521].

Nowadays, Reinforcement Learning (RL) is applied to various real-world tasks and attracts much attention in the fields of games, robotics, and autonomous driving. It is very challenging and devices overwhelming to directly apply RL to real-world environments. Due to the reality gap simulated environment does not match perfectly to the real-world scenario and additional learning cannot be performed. Therefore, an efficient approach is required for RL to find an optimal control policy and get better learning efficacy. In this paper, we propose federated reinforcement learning based on multi agent environment which applying a new federation policy. The new federation policy allows multi agents to perform learning and share their learning experiences with each other e.g., gradient and model parameters to increase their learning level. The Actor-Critic PPO algorithm is used with four types of RL simulation environments, OpenAI Gym’s CartPole, MoutainCar, Acrobot, and Pendulum. In addition, we did real experiments with multiple Rotary Inverted Pendulum (RIP) to evaluate and compare the learning efficiency of the proposed scheme with both environments.

The p32 protein plays a crucial role in the regulation of cytosolic Ca2+ concentrations ([Ca2+]c) that contributes to the Ca2+-dependent signaling cascade. Using an adenovirus and plasmid p32-overexpression system, the aim of the study was to evaluate the role of p32 in the regulation of [Ca2+] and its potential associated with Ca2+-dependent endothelial nitric oxide synthase (eNOS) activation in endothelial cells. Using electron and confocal microscopic analysis, p32 overexpression was observed to be localized to mitochondria and the endoplasmic reticulum and played an important role in Ca2+ translocation, resulting in increased [Ca2+] in these organelles and reducing cytosolic [Ca2+] ([Ca2+]c). This decreased [Ca2+]c following p32 overexpression attenuated the Ca2+-dependent signaling cascade of calcium/calmodulin dependent protein kinase II (CaMKII)/AKT/eNOS phosphorylation. Moreover, in aortic endothelia of wild-type mice intravenously administered adenovirus encoding the p32 gene, increased p32 levels reduced NO production and accelerated reactive oxygen species (ROS) generation. In a vascular tension assay, p32 overexpression decreased acetylcholine (Ach)-induced vasorelaxation and augmented phenylephrine (PE)-dependent vasoconstriction. Notably, decreased levels of arginase II (ArgII) protein using siArgII were associated with downregulation of overexpressed p32 protein, which contributed to CaMKII-dependent eNOS phosphorylation at Ser1177. These results indicated that increased protein levels of p32 caused endothelial dysfunction through attenuation of the Ca2+-dependent signaling cascade and that ArgII protein participated in the stability of p32. Therefore, p32 may be a novel target for the treatment of vascular diseases associated with endothelial disorders.

Background Hysteroscopic surgery has been used in various gynecological fields. However, massive fluid overload can occur as a complication due to persistent infusion of media for uterine cavity distension. We present the case of a woman who developed cardiomyopathy with pulmonary edema and epistaxis during hysteroscopic surgery. Case A 76-year-old female underwent hysteroscopic septectomy. She manifested abrupt, active nasal bleeding and regurgitation in the intravenous line. Heart rate, SpO2, and PETCO2 decreased from 55 beats/min to 29 beats/min, from 100% to 56%, and from 31 mmHg to 9 mmHg, respectively. After the operation, brain CT showed bilateral prominent superior ophthalmic vein dilation. Echocardiography showed left ventricle apical ballooning and global hypokinesia. The patient recovered after two days of conservative management, with no sequelae. Conclusions Although hysteroscopic surgery is a simple procedure, careful monitoring is necessary to prevent complications from absorption of fluid distending media during the procedure.

Dexmedetomidine is used for sedation during spinal anesthesia. The sympatholytic effect of dexmedetomidine may exacerbate hypotension and bradycardia with spinal anesthesia. This study investigated the effects of prophylactic intramuscular injection of ephedrine in preventing hypotension and bradycardia occurring through combined use of spinal anesthesia and dexmedetomidine. One hundred sixteen patients scheduled for lower extremity orthopedic surgery were randomized into two groups receiving either ephedrine 20 mg intramuscularly or equivalent amount of 0.9% NaCl, both with dexmedetomidine and spinal anesthesia. The primary endpoint was the incidence of hemodynamic perturbations (hypotension or bradycardia event). The secondary endpoint was a rescue doses of ephedrine and atropine. The incidence of hemodynamic perturbations was significantly lower in the ephedrine group compared with to the saline group (26.3% versus 55.9%, p = 0.001). The rescue doses of atropine (0.09 ± 0.21 versus 0.28 ± 0.41, p = 0.001) and ephedrine (1.04 ± 2.89 versus 2.03 ± 3.25, p = 0.007) were also significantly lower in the ephedrine group. There was no differences in number of patients with hypertensive (7.0% versus 11.9%, p = 0.375) or tachycardia (1.8% versus 3.4% p = 0.581) episodes. The use of ephedrine intramuscular injections may be a safe and efficacious option in preventing hemodynamic perturbations in patients who received spinal anesthesia and sedation using dexmedetomidine.

Although arginase II (ArgII) is abundant in mitochondria, Ca2+-accumulating organelles, the relationship between ArgII activity and Ca2+ translocation into mitochondria and the regulation of cytosolic Ca2+ signaling are completely unknown. We investigated the effects of ArgII activity on mitochondrial Ca2+ uptake through mitochondrial p32 protein (p32m) and on CaMKII-dependent vascular smooth muscle cell (VSMC) contraction. Native low-density lipoprotein stimulation induced an increase in [Ca2+]m as measured by CoCl2-quenched calcein-AM fluorescence, which was prevented by Arg inhibition in hAoSMCs and reduced in mAoSMCs from ArgII−/− mice. Conversely, [Ca2+]c analyzed with Fluo-4 AM was increased by Arg inhibition and ArgII gene knockout. The increased [Ca2+]c resulted in CaMKII and MLC 20 phosphorylation, which was associated with enhanced vasoconstriction activity to phenylephrine (PE) in the vascular tension assay. Cy5-tagged siRNA against mitochondrial p32 mRNA (sip32m) abolished mitochondrial Ca2+ uptake and induced activation of CaMKII. Spermine, a polyamine, induced mitochondrial Ca2+ uptake and dephosphorylation of CaMKII and was completely inhibited by sip32m incubation. In mAoSMCs from ApoE-null mice fed a high-cholesterol diet (ApoE−/− +HCD), Arg activity was increased, and spermine concentration was higher than that of wild-type mice. Furthermore, [Ca2+]m and p32m levels were elevated, and CaMKII phosphorylation was reduced in mAoSMCs from ApoE−/− +HCD. In vascular tension experiments, an attenuated response to vasoconstrictors in de-endothelialized aorta from ApoE−/− +HCD was recovered by incubation of sip32m. ArgII activity-dependent production of spermine augments Ca2+ transition from the cytosol to the mitochondria in a p32m-dependent manner and regulates CaMKII-dependent constriction in VSMCs. Researchers have illuminated how a protein, arginase II (ArgII), is involved in development of vascular diseases such as atherosclerosis, or narrowing of the arteries by plaque deposits. Blood vessel diameter is regulated by layers of muscle; the balance between constriction and relaxation is critical for blood flow and vascular health. Increased ArgII is known to be a factor in arterial disease; however, the details of regulation, and how they relate to plaque deposition, remain poorly understood. Sungwoo Ryoo at Kangwon National University, Chuncheon, South Korea and co-workers investigated how ArgII levels affect arterial constriction and relaxation in mice. Decreasing ArgII restored the muscle cells’ contraction response by preventing excessive calcium accumulation in the cellular powerhouse, mitochondria. These results may aid in developing treatments for one of the leading causes of death worldwide.

Network Virtualization (NV) technology allows multiple virtual network requests to share resources on the same subtract network. In network virtualization, Virtual Network Embedding (VNE) is one of the main techniques used to map a virtual network to the substrate network. The effectiveness and efficiency of the virtual network are determined by the performance of the embedding algorithm. Hence, an efficient embedding algorithm is required to reduce the rejection rate and embed the maximum number of virtual networks which best fit the subtract network. In this article, we propose Ego Network-based Virtual Network Embedding (EN-ViNE) algorithm which aims to improve the performance of the embedding to accept more VNRs and increase the long-term revenue. We utilize the ego-network technique to search the nearest subtract nodes for embedding virtual nodes and found the shortest path between them for link embedding. The proposed scheme attempts to minimize the rejection of virtual network requests (VNRs) that are intended to maximize the long-term revenue for the substrate network provider. Extensive computer simulation reveals that the proposed scheme considerably outperforms the existing algorithms, topology-aware, and baseline for the long-term average revenue, acceptance ratio, and revenue/cost ratio.

Recently, reinforcement learning has been applied to various fields and shows better performance than humans. In particular, it is attracting attention in the fields of smart factories and robotics that require automatic control without human intervention. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own IoT devices of the same type. There is no guarantee that the reinforcement learning agent that has learned the optimal control policy using one IoT device will perform optimal control of other IoT devices. Therefore, since reinforcement learning must be performed individually for each IoT device, it takes a lot of time and cost. To solve this problem, we propose a new method of federated reinforcement learning. In the proposed federated reinforcement learning, multiple agents have independent IoT devices, perform learning at the same time, and federate with each other to improve learning performance. Therefore, we apply a new gradient sharing method and transfer learning to reinforcement learning. In addition, Actor-Critic PPO, which shows good performance in reinforcement learning algorithms, is used. And, for smooth learning in the IoT environment where numerous devices exist, we propose an architecture based on Software-Defined Networking. Using multiple rotary inverted pendulum devices interconnected via a SDN, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple IoT devices.

Introduction: The maintenance of ventilation through the endotracheal tube during surgery is essential, and failure to do so can cause serious consequences. Case Report: We experienced a case of intraoral kinking of the polyvinyl chloride, single-lumen endotracheal tube (ETT) in a patient with severe tracheal deviation caused by pulmonary tuberculosis. During surgery, the operation table was tilted upward and to the left, which caused change in patient’s head position and led to intraoral kinking. Conclusion: If there is a severe tracheal deviation expected to cause deformity of the ETT, it is recommended to use a reinforced tube.

Acidic electrolyzed water is an innovative sanitizer having a wide-spectrum of applications in food industry, and healthcare industry but little is known on its effect and mechanism in wound healing. The study was conducted to identify the effect and mechanism of slightly acidic electrolyzed water (SAEW) on cutaneous wounds in hairless mice. SAEW (pH: 5-6.5, oxidation reduction potential: 800 mV, chlorine concentration: 25 ppm) was prepared through electrolysis of water and was applied to the wounds of hairless mice three times a day for seven days. Wound size, immune response and oxidative stress were explored and compared to conventional agents such as Betadine and alcohol. We found that SAEW-treated group showed the highest wound reduction percentage (p

BACKGROUND Laparoscopic upper abdominal surgery can cause spontaneous respiration due to diaphragmatic stimulation and intra-abdominal CO2 inflation. Therefore, sufficient muscle relaxation is necessary for a safe surgical environment. METHODS We investigated if the combination of rocuronium and cisatracurium can counteract the delayed onset of cisatracurium's action and delayed recovery of muscle relaxation and whether the dosage of rocuronium, which is metabolized hepatically, can be reduced. A total of 75 patients scheduled for laparoscopic cholecystectomy with an American Society of Anesthesiology physical status I-II, in the age range of 20-60 years, and with a 20-30 kg/m2 body mass index were included in the study. RESULTS The patients were divided into the following groups: combination group (Group RC, rocuronium 0.3 mg/kg and cisatracurium 0.05 mg/kg), rocuronium group (Group R, rocuronium 0.6 mg/kg), and cisatracurium group (Group C, cisatracurium 0.1 mg/kg), and the onset, 25% duration, recovery index, and addition/time ratio were measured. Patients in Group RC exhibited a significantly different addition/time ratio compared with patients in the other two groups (p = 0.003). CONCLUSION During laparoscopic cholecystectomy, the 95% effective dose of rocuronium in combination with cisatracurium is expected to provide a sufficient muscle relaxant effect.

A rotary inverted pendulum is an unstable and highly nonlinear device and is used as a common model for engineering applications in linear and nonlinear control. In this study, we created a cyber physical system (CPS) to demonstrate that a deep reinforcement learning agent using a rotary inverted pendulum can successfully control a remotely located physical device. The device we created is composed of a cyber environment and physical environment using the Message Queuing Telemetry Transport (MQTT) protocol with an Ethernet connection to connect the cyber environment and the physical environment. The reinforcement learning agent controls the physical device, which is located remotely from the controller and a classical proportional integral derivative (PID) controller is utilized to implement imitation and reinforcement learning and facilitate the learning process. In addition, the control and monitoring system is built on the open source EdgeX platform, so that learning tasks performed near the source of data generation and real-time data emitted from the physical device can be observed while reinforcement learning is performed. From our CPS experimental system, we verify that a deep reinforcement learning agent can control a remotely located real-world device successfully.

Halothane, isoflurane and desflurane are metabolized to hepatotoxic trifluoroacetyl proteins. But sevoflurane is metabolized to hexafluoroisopropanol. Hexafluoroisopropanol has a low binding affinity for liver protein and is therefore rapidly converted to glucuronidate that are excreted in the urine. Thus sevoflurane has been considered to have a very low potential for hepatotoxicity. We report a case of a 67 years old woman who developed acute hepatic dysfunction after sevoflurane anesthesia.

Cerebral salt wasting syndrome is a hyponatremic and hypovolemic condition caused by intracranial disorders, such as head injury, subarachnoid hemorrhage, brain tumor, and brain operations. We report a case of a 5-year-old girl that had cerebral salt wasting syndrome with marked polyuria who showed transient oliguria during general anesthesia. The patient had undergone an operation for traumatic intracranial hemorrhage three months prior and has had marked polyuria and hyponatremia since then. After induction of anesthesia for cranioplasty, the patient had oliguria during surgery and then resumed polyuria in the post-operative period.

As smart phones have rapidly proliferated over the past few years, LTE operators endeavor to cope with large mobile data traffic volumes. To solve such problems, we propose a new distributed LTE/EPC network architecture based on SDN, NFV, and cloud computing supporting distributed P-GWs and centralized control plane in LTE/EPC networks. It is designed considering the three requirements: (1) distributing P-GWs closer to the user equipments, (2) virtually centralizing control plane, and (3) control and data plane separation. Next, we present a new SDN-based distributed mobility management (SDMM) which confirms to the proposed architecture. Based on the SDMM, the location and handover management are then presented. For enhancing network performance more, we also propose a route optimization strategy for internal traffic exchanged between LTE UEs. The proposed solutions are compared with the conventional LTE/EPC network's scheme in terms of the gateway data processing volume, handover latency, and the number of valid data sessions. The comparison results show that the proposed solutions can be an efficient way to enhance the scalability of LTE/EPC core networks.

The contractility of vascular smooth muscle cells (VSMCs) controls the lumen diameter of vessels, thus serving a role in regulating blood pressure and organ blood flow. Although arginases are known to have numerous effects in the biological activities of VSMCs, the effects of arginase II on the constriction of VSMCs has not yet been investigated. When conducting a natural products screen for an inhibitor against arginase, the present study identified that a relatively high concentration of resveratrol (RSV) exhibited arginase inhibitory activity. Therefore, the present study investigated whether RSV could regulate VSMCs contractions and the underlying mechanism. Arginase inhibition by RSV led to an increase in the concentration of the substrate L‑Arg and an accompanying increase in the cytosol Ca2+ concentration [(Ca2+)c] in VSMCs. The increased [Ca2+]c induced by RSV and L‑Arg treatments resulted in CaMKII‑dependent MLC20 phosphorylation. The effects of RSV on VSMCs were maintained even when VSMCs were pre‑treated with sirtinol, an inhibitor of Sirt proteins. In a vascular tension assay with de‑endothelialized aortic vessels, vasoconstrictor responses, which were measured using phenylephrine (PE), were significantly enhanced in the RSV‑ and L‑Arg‑treated vessels. Therefore, although arginase inhibition has exhibited beneficial effects in various diseases, care is required when considering administration of an arginase inhibitor to patients with vessels endothelial dysfunction as RSV can induce vessel contraction.