General anesthetic agents induce neurotoxicity through oligodendrocytes in the developing brain

General anesthetic agents can impact brain function through interactions with neurons and their effects on glial cells.Oligodendrocytes perform essential roles in the central nervous system,including myelin sheath formation,axonal metabolism,and neuroplasticity regulation.They are particularly vulnerable to the effects of general anesthetic agents resulting in impaired proliferation,differentiation,and apoptosis.Neurologists are increasingly interested in the effects of general anesthetic agents on oligodendrocytes.These agents not only act on the surface receptors of oligodendrocytes to elicit neuroinflammation through modulation of signaling pathways,but also disrupt metabolic processes and alter the expression of genes involved in oligodendrocyte development and function.In this review,we summarize the effects of general anesthetic agents on oligodendrocytes.We anticipate that future research will continue to explore these effects and develop strategies to decrease the incidence of adverse reactions associated with the use of general anesthetic agents.

General anesthetic agents induce neurotoxicity through astrocytes

Neuroscientists have recognized the importance of astrocytes in regulating neurological function and their influence on the release of glial transmitters.Few studies,however,have focused on the effects of general anesthetic agents on neuroglia or astrocytes.Astrocytes can also be an important target of general anesthetic agents as they exert not only sedative,analgesic,and amnesic effects but also mediate general anesthetic-induced neurotoxicity and postoperative cognitive dysfunction.Here,we analyzed recent advances in understanding the mechanism of general anesthetic agents on astrocytes,and found that exposure to general anesthetic agents will destroy the morphology and proliferation of astrocytes,in addition to acting on the receptors on their surface,which not only affect Ca^(2+)signaling,inhibit the release of brain-derived neurotrophic factor and lactate from astrocytes,but are even involved in the regulation of the pro-and anti-inflammatory processes of astrocytes.These would obviously affect the communication between astrocytes as well as between astrocytes and neighboring neurons,other neuroglia,and vascular cells.In this review,we summarize how general anesthetic agents act on neurons via astrocytes,and explore potential mechanisms of action of general anesthetic agents on the nervous system.We hope that this review will provide a new direction for mitigating the neurotoxicity of general anesthetic agents.

Improvement Mechanism of Adhesion Performance of Anti-stripping Agents and Coupling Agents on Asphalt-Aggregate Interface Based on Molecular Dynamics

This study examined the mechanisms for improving the adhesion performance of the asphalt-aggregate interface with two anti-stripping agents and two coupling agents.The investigation of contact behavior between various asphalt-aggregate surfaces was conducted using molecular dynamics(MD)simulations.The interaction energy and the relative concentration distribution were employed as the parameters to analyze the enhancement mechanisms of anti-stripping agents and coupling agents on the asphalt-aggregate interface.Results indicated that the adhesion at the asphalt-aggregate interface could be strengthened by both anti-stripping agents and coupling agents.Anti-stripping agents primarily improve adhesion through the reinforcement of electrostatic attraction,while coupling agents primarily upgrade adhesion by strengthening the van der Waals.Hence,the molecular dynamics modeling and calculation techniques presented in this study can be utilized to elucidate the development mechanism of the asphalt-aggregate interface through the use of anti-stripping agents and coupling agents.

Progress in the Use of Glucocorticoids and Biological Agents in Non-Infectious Uveitis

One of the main immune-mediated diseases that lead to avoidable blindness is non-infectious uveitis. Glucocorticoids are the first-line therapy choice for noninfectious uveitis;however, biologics are also showing promise in the management of this condition. The description of glucocorticoid and biologic usage in non-infectious uveitis is the main topic of this paper.

Near-Infrared Fluorescence Imaging Contrast Agents for Clinical Research: Limitations and Alternatives

Introduction: Near-infrared fluorescence imaging is a technique that will establish itself in the short term at the international level because it is recognized for its potential to improve the performance of surgical interventions, its moderate investment and operating costs and its portability. Although the technology is now mature, there is currently the problem of the availability of contrast agents to be injected IV. The aim of this methodology article is to propose an alternative solution to the need for contrast agents for clinical research, particularly in oncology. Methodology: They consist of coupling a fluorescent marker in the form of an NHS derivative, such as IR DYE manufactured in compliance with GMP, with therapeutic monoclonal antibodies having marketing authorization for molecular imaging. For a given antibody, the marking procedure must be the subject of a validation file on the final preparation filtered on a sterilizing membrane at 0.22 μm. Once the procedure has been validated, it would be unnecessary to repeat the tests before each clinical research examination. A check of the marking by thin-layer chromatography (TLC) and place it in a sample bank at +4˚C for 1 month of each injected formulation would be sufficient for additional tests if necessary. Conclusion: Molecular near-infrared fluorescence imaging is experiencing development, the process of which could be accelerated by greater availability of clinical contrast agents. Alternative solutions are therefore necessary to promote clinical research in this area. These methods must be shared to make it easier for researchers.

Effects of virtual agents on interaction efficiency and environmental immersion in MR environments

Background Physical entity interactions in mixed reality(MR)environments aim to harness human capabilities in manipulating physical objects,thereby enhancing virtual environment(VEs)functionality.In MR,a common strategy is to use virtual agents as substitutes for physical entities,balancing interaction efficiency with environmental immersion.However,the impact of virtual agent size and form on interaction performance remains unclear.Methods Two experiments were conducted to explore how virtual agent size and form affect interaction performance,immersion,and preference in MR environments.The first experiment assessed five virtual agent sizes(25%,50%,75%,100%,and 125%of physical size).The second experiment tested four types of frames(no frame,consistent frame,half frame,and surrounding frame)across all agent sizes.Participants,utilizing a head mounted display,performed tasks involving moving cups,typing words,and using a mouse.They completed questionnaires assessing aspects such as the virtual environment effects,interaction effects,collision concerns,and preferences.Results Results from the first experiment revealed that agents matching physical object size produced the best overall performance.The second experiment demonstrated that consistent framing notably enhances interaction accuracy and speed but reduces immersion.To balance efficiency and immersion,frameless agents matching physical object sizes were deemed optimal.Conclusions Virtual agents matching physical entity sizes enhance user experience and interaction performance.Conversely,familiar frames from 2D interfaces detrimentally affect interaction and immersion in virtual spaces.This study provides valuable insights for the future development of MR systems.

Effectiveness of Chromated Copper Arsenate against Deteriorating Agents of Eucalyptus Poles and Posts in Four Eco-Regions in Kenya

Previous studies done elsewhere have shown that Eucalyptus poles treated with chromated copper arsenate (CCA) can last over 30 years. Kenya is exceptional because in some eco-regions, the Eucalyptus poles’ life span has greatly reduced to 5 years. The current study was designed to evaluate wood deteriorating agents of CCA-treated Eucalyptus poles and variability in four eco-regions of Kenya, namely, dryland, coastal, highland and humid lake. A total of 360 Eucalyptus pole samples were used for this experiment. Three CCA treatments were used to treat transmission poles at 20 kg/cm3 fencing posts samples at 6 kg/cm3, and a control group. Results indicated that termites and wood-decay fungi attacks caused wood deterioration in the four eco-regions. The proportion of power transmission pole degradation by wood deteriorating agents varied across eco-regions, between treatments and control and between time after treatments. Dryland eco-regions had the highest termite-related degradation (41.82%) while wood-decay fungi attack was highest in the highland eco-regions (9.20%). Samples treated with 6 kg/cm3 recorded the lowest level of wood deterioration, manifested by minimal superficial termite and wood-decay fungi attack. Samples treated with 20 kg/cm3 were characterized by moderate termite and wood-decay fungi attacks observed around the heartwood region, unlike sapwood. This study concluded that the deterioration of Eucalyptus CCA-treated poles is a question of climatic variability and hence, to increase the poles’ lifespan, CCA treatment should be tailored according to the characteristics of the ecoregion of use. Further investigations will inform the diversity of termites and decay-fungi across different eco-regions.

Effects of Water-retaining Agents on Growth, Development, Yield and Quality of Soybean under Drought Stress

[Objectives]The paper was to explore the effects of different doses of water-retaining agent on the growth and development indicators,yield and quality of soybean plants subjected to drought stress.[Methods]The effects of drought stress(MDS)and drought stress with low(MDS-L),medium(MDS-M)and high doses(MDS-H)of the water-retaining agent on the growth and development indicators,root activity,MDA content,antioxidant enzyme activity,yield and quality of soybean were studied by field plot test,with the normal water supply serving as the control(CK).[Results]In response to drought stress,the plant height,stem diameter,and yield of soybean exhibited a notable decline.Additionally,the contents of protein,fat,linoleic acid,and linolenic acid in seeds demonstrated a significant reduction.Conversely,the root activity and antioxidant enzyme activity exhibited a noticeable decline,while the MDA content exhibited an increase.The application of varying doses of the water-retaining agent was found to significantly enhance soybean growth,stimulate root activity,and elevate antioxidant enzyme activity,while concurrently reducing MDA content.The observed effects were found to be dose-dependent,with the greatest effects observed at the highest dose.In comparison to MDS,the yields of soybean in the MDS-L,MDS-M,and MDS-H treatments exhibited a 18.38%,25.58%,and 46.26%increase,respectively.Additionally,the content of protein,fat,linoleic acid,and linolenic acid in seeds demonstrated a notable enhancement.[Conclusions]The application of the water-retaining agent has been demonstrated to significantly promote the growth of soybean plants under conditions of drought stress,resulting in an improvement in both the yield and the quality of the soybean crop.The recommended dosage of the water-retaining agent is 3.3 kg/667 m 2.

Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

Factors Influencing Compliance with Safe Handling Requirements of Anticancer Agents by Nurses in a Tertiary Hospital-A Secondary Publication

Purpose:This research aimed to identify leading factors that affect nurses’compliance with the safe handling of anticancer agents.Methods:Data were collected from 114 nurses working in the university hospital and were analyzed through independent t-test,one-way ANOVA,Pearson’s correlation coefficient,and multiple regression analysis using SPSS25.0.Results:The average level of compliance with the safe handling of anticancer agents was 3.73±0.33 out of 5 points.Workplace safety culture(β=0.40,P<0.001)and knowledge of safe handling(β=0.18,P=0.030)had significant influences on nurses’compliance with the safe handling of anticancer agents.The explained variance for compliance was 28.3%.Conclusion:To enhance the implementation of safety management for anticancer agents,each institution should strive to support human and material resources and enhance specialization in the workplace safety culture through system improvement.Based on the results of this study,we suggest research for the development of a training program for anticancer agent safety management.

Anti-Cancer Agents Associated Diarrhea:Current Status and Prospects

Cancer stands as one of the major threats to human life.Ensuring the safety of drugs is paramount,and the impact of adverse reactions on patients’quality of life and prognosis should not be underestimated.Diarrhea is a common clinical adverse event,and despite the absence of specific anti-diarrhea drugs,there is a pressing need for improvement.This article aims to provide a valuable reference for researchers in clinical drug use and scientific tumor treatment.It summarizes recent advancements in drug mechanisms and adverse reactions,whether in preclinical research or clinical diagnosis and therapy.

流程型生产安全数据流的多Agent节点协同分流优化方法

为实现流程型生产安全监测系统的及时、准确决策,结合数据流采集与隐患识别过程,分析非关键数据的冗余、关键数据的缺失和数据计算时延较大的问题,提出基于多Agent的流程型生产安全数据流网络分流调度规划方法和节点流量划分识别机制。研究结果表明:相较于分簇传输方法,数据流网络分流调度方法可以实现关键隐患数据更高的传输成功率;相较于常规的复杂事件处理方法,本文提出的流量划分识别机制在2种类型数据集上实现隐患事件识别均有更低的计算时延。研究结果可为流程行业安全生产数字化管控模式和数据高质量获取提供参考。

基于多Agent的服装可持续消费行为建模与仿真

“双碳”目标下,随着服装消费引起的气候变化和环境污染等生态问题日益严重,迫切需要重新思考和重塑服装消费模式,推动服装消费向质量型、低碳化转变,从而实现服装产业的绿色转型。文章运用多Agent建模方法,构建服装可持续消费行为仿真模型,采用Netlogo平台进行仿真实验,探求服装可持续消费行为的影响机理。结果表明,消费者可持续消费认知、产品可持续性和设施供应条件均显著促进可持续消费行为;但不同强度下,产品可持续性和设施供应条件的推动作用存在差异。基于此,从可持续消费认知和供应条件两方面提出了引导中国消费者服装可持续消费行为的建议和对策。

基于多Agent深度强化学习的无人机协作规划方法

人机协作控制是多无人机任务规划的重要方式。考虑多无人机任务环境协同解释和策略控制一致性需求,提出基于多Agent深度强化学习的无人机协作规划方法。依据任务知识和行为状态,构建基于任务分配Agent的任务规划器,生成人机交互的相互依赖关系;设计一种深度学习强化方法,解决群体行为最优策略和协同控制方法,并利用混合主动行为选择机制评估学习策略。实验结果表明:作为人机交互实例,所提方法通过深度强化学习使群体全局联合动作表现较好,学习速度和稳定性均能优于确定性策略梯度方法。同时,在跟随、自主和混合主动3种模式比较下,可以较好地控制无人机飞行路径和任务,为无人机集群任务执行提供了智能决策依据。

基于免疫Agent的电力电缆线路故障检测系统

为区别故障信号、非故障信号,实现对电缆线路故障的准确检测,设计了基于免疫Agent的电力电缆线路故障检测系统。利用前端检测电路为故障分析模块、测距方式切换模块提供电量传输信号,完成系统前端检测装置的连接。按照免疫Agent检测原理提取电力电缆线路故障信号的特征,联合已获取信号对象,求解检测插值指标的具体数值。结合各级硬件设备结构,完成系统设计。实验结果表明,该系统可同时检测波频为10~20 Hz、40~50 Hz、60~70 Hz的故障信号与波频为20~30 Hz、30~40 Hz、50~60 Hz的非故障信号,可以在精准辨别故障与非故障信号的同时,实现对电力电缆线路故障的准确检测。

基于Agent人工智能的异构网络多重覆盖节点入侵检测系统设计

异构网络具有结构复杂、多重覆盖面积大等特征,使得网络入侵检测较为隐蔽,威胁网络运行的安全性;为此,对基于Agent人工智能的异构网络多重覆盖节点入侵检测系统进行了研究;通过检测Agent和通信Agent装设主机Agent,以Cisco Stealthwatch流量传感器作为异构网络传感器检测攻击行为,采用STM32L151RDT664位微控制器传输批量数据,由MAX3232芯片实现系统电平转化,实现硬件系统设计;软件部分设计入侵检测标准,采用传感器设备捕获网络实时数据,通过Agent技术解析异构网络协议并提取数据运行特征,综合考虑协议解析结果及与检测标准匹配度,实现异构网络多重覆盖节点入侵检测;经实验测试表明,基于Agent人工智能的异构网络多重覆盖节点入侵检测系统入侵行为的漏检率和入侵类型误检率的平均值仅为6%和5%,能够有效提高检测精度,减小检测误差。

一种基于Agent的任务自生成方法

任务自生成是基于预先设定的业务工作流完成任务执行流程制定的一个过程,任务执行流程制定主要包括任务执行步骤选取与任务执行步骤相应的解决方案选取。提出一种基于Agent的任务自生成方法来解决基于传统的业务工作流的流程制定机制,在面对有差异性需求的不同任务时,因其不能做出任务执行流程制定的自适应性改变而在处理任务时出现处理速度慢、结果不准确等问题。利用Agent的环境感知能力实现对任务需求信息和任务自生成系统环境的感知,利用Agent的自主行为决策能力结合感知的信息实现对任务执行流程制定工作的自主管理,在真实应用场景测试中,通过一系列比较实验验证了这种方法在处理速度和准确度方面具有明显优势。

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

基于多Agent传动关系的股市趋势预测

股市趋势预测是机器学习领域中一个具有挑战性的任务。由于一些因素对于股市的影响是动态且不确定的,导致股市趋势难以预测。针对已有方法在股市预测时存在的灵敏性差、适应力弱等问题,从快变量和慢变量的传动关系出发,利用Agent技术对股市中的快周期和慢周期进行联合建模,提出一种多Agent传动影响图(MATID)股市趋势预测方法。给出股市中快周期和慢周期的划分标准,并引入周期能量的概念;通过对相关趋势指标的特征融合,给出周期能量的量化计算方法;通过分析快周期和慢周期的动态作用过程,给出传动因子的表示方法;将快周期和慢周期分别对应成不同的Agent,利用多Agent影响图模型建模快周期和慢周期的传动过程;利用股市振子模型表示快Agent和慢Agent之间的传动效用,利用联合树的自动推理技术对股市趋势进行预测。在不同样本数量和不同股市趋势下进行实验,结果表明,与门控循环单元、S-LSTM和Hybrid-RNN预测方法相比,MATID方法预测精确率提升1.5%~7.0%,召回率提升5.4%~6.7%,F1值提升3.7%~6.2%,具有良好的灵敏性和适应力。