蒲金口服液对宫内感染/炎症致早产脑损伤大鼠脑组织OL、OPC细胞凋亡和Nogo-A、OMgp表达的影响

目的探讨蒲金口服液对宫内感染/炎症致早产脑损伤大鼠神经保护作用的机制。方法将80只胎龄<22 d的早产仔鼠随机分为8组(每组10只):早期药物干预组(蒲金口服液高剂量组、蒲金口服液低剂量组、银杏叶提取物组、模型组)和晚期药物干预组。生后21 d龄进行神经行为学检测,并采用qRT-PCR和Western blot方法检测勿动蛋白-A(Nogo-A)和少突胶质细胞髓磷脂糖蛋白(OMgp)的表达,TUNEL法和A2B5、CNPase双标免疫荧光染色法检测少突胶质细胞(OL)、前体细胞(OPC)凋亡情况。结果LPS组早产仔鼠脑组织Nogo-A和OMgpmRNA和蛋白的表达均明显高于正常对照组(P<0.05),蒲金口服液高剂量、低剂量和银杏叶提取物干预组大鼠的神经行为学评分均优于模型组(P<0.05),且早期干预组高于晚期干预组(P<0.05)。蒲金口服液高剂量、低剂量和银杏叶提取物均能降低早产脑损伤大鼠脑组织Nogo-A、OMgp mRNA和蛋白的表达,减少OL和OPC的凋亡(P<0.05),其中蒲金口服液高剂量早期干预效果更显著(P<0.05)。结论蒲金口服液可以改善早产脑损伤大鼠的神经行为学表现,减少脑组织Nogo-A、OMgp的表达和OL、OPC的凋亡,具有神经保护的作用。

Nogo-A在神经系统疾病中的研究进展

神经生长抑制因子A(neurite outgrowth inhibitor A,Nogo-A)在哺乳动物中是一种能够抑制轴突生长的蛋白质。研究发现Nogo-A在神经系统中对轴突生长和突触可塑性起到调节作用,并与阿尔兹海默症、肌萎缩性侧索硬化症、多发性硬化症、脊髓损伤等密切相关。本文综述了Nogo-A及其受体NgR的基本结构、功能以及在神经系统疾病等方面的研究进展。

Nogo-A蛋白及调控轴突再生作用研究进展

中枢神经系统损伤产生的抑制性微环境是神经再生困难的重要原因,其中Nogo-A蛋白是抑制轴突再生的关键因子,其结构域以片段形式通过不同途径释放到细胞外,与受体NgR1、PirB、S1PR2或HSPG相结合,激活多种信号通路调控细胞凋亡、运动以及细胞骨架蛋白聚合,引起细胞骨架解聚、生长锥塌陷,抑制神经元轴突生长。本文对Nogo-A蛋白的结构、受体和抑制轴突再生作用机制的研究进展进行综述。

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Task Offloading in Edge Computing Using GNNs and DQN

In a network environment composed of different types of computing centers that can be divided into different layers(clod,edge layer,and others),the interconnection between them offers the possibility of peer-to-peer task offloading.For many resource-constrained devices,the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity.In this scenario,it is worth considering transferring these tasks to resource-rich platforms,such as Edge Data Centers or remote cloud servers.For different reasons,it is more exciting and appropriate to download various tasks to specific download destinations depending on the properties and state of the environment and the nature of the functions.At the same time,establishing an optimal offloading policy,which ensures that all tasks are executed within the required latency and avoids excessive workload on specific computing centers is not easy.This study presents two alternatives to solve the offloading decision paradigm by introducing two well-known algorithms,Graph Neural Networks(GNN)and Deep Q-Network(DQN).It applies the alternatives on a well-known Edge Computing simulator called PureEdgeSimand compares them with the two defaultmethods,Trade-Off and Round Robin.Experiments showed that variants offer a slight improvement in task success rate and workload distribution.In terms of energy efficiency,they provided similar results.Finally,the success rates of different computing centers are tested,and the lack of capacity of remote cloud servers to respond to applications in real-time is demonstrated.These novel ways of finding a download strategy in a local networking environment are unique as they emulate the state and structure of the environment innovatively,considering the quality of its connections and constant updates.The download score defined in this research is a crucial feature for determining the quality of a download path in the GNN training process and has not previously been proposed.Simultaneously,the suitability of Reinforcement Learning(RL)techniques is demonstrated due to the dynamism of the network environment,considering all the key factors that affect the decision to offload a given task,including the actual state of all devices.

Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

局部应用Nogo-B蛋白促进糖尿病鼠创面愈合的作用研究

目的探究Nogo-B蛋白对糖尿病小鼠创面愈合的影响。方法选择24只糖尿病小鼠制备背部全层皮肤缺损创面模型,随机分为PBS对照组及Nogo-B蛋白处理组两组,每组各12只。Nogo-B蛋白处理组在创面局部注射浓度为250 ng/ml的Nogo-B蛋白溶液,对照组注射等量PBS溶液。第0天、3天、7天、14天分别创面拍照比较创面愈合率。第7天、第14天每组各处死3只小鼠并创面取材,采用HE染色评估创面上皮化及组织学形态;免疫组织化学及荧光比较创面炎症细胞浸润、干细胞表达及血管化情况差异。结果造模后第7天、第14天,Nogo-B蛋白处理组创面愈合率均高于对照组(P<0.01);HE染色显示Nogo-B蛋白处理组创面组织上皮化距离显著多于对照组;免疫荧光染色显示愈合后期第14天Nogo-B蛋白组创面内CD45表达较对照组减少,而两个时间点,BrdU和α-SMA阳性表达细胞数目均较对照组显著增多。结论局部应用Nogo-B蛋白能够通过持续加速表皮干细胞增殖、创面血管化以及减轻愈合后期创面炎症反应等作用机理来促进糖尿病小鼠创面愈合。

Optimizing Spatial Crowdsourcing:A Quality-Aware Task Assignment Approach for Mobile Communication

The widespread use of advanced electronic devices has led to the emergence of spatial crowdsourcing,a method that taps into collective efforts to perform real-world tasks like environmental monitoring and traffic surveillance.Our research focuses on a specific type of spatial crowdsourcing that involves ongoing,collaborative efforts for continuous spatial data acquisition.However,due to limited budgets and workforce availability,the collected data often lacks completeness,posing a data deficiency problem.To address this,we propose a reciprocal framework to optimize task assignments by leveraging the mutual benefits of spatiotemporal subtask execution.We introduce an entropy-based quality metric to capture the combined effects of incomplete data acquisition and interpolation imprecision.Building on this,we explore a quality-aware task assignment method,corresponding to spatiotemporal assignment strategies.Since the assignment problem is NP-hard,we develop a polynomial-time algorithm with the guaranteed approximation ratio.Novel indexing and pruning techniques are proposed to further enhance performance.Extensive experiments conducted on datasets validate the effectiveness of our methods.

A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.

支气管哮喘儿童GO/NOGO范式实验执行功能及其与肺功能的相关性分析

背景哮喘儿童的执行功能方面存有缺陷会影响患儿的生活质量和心理健康,执行功能与肺功能之间关系的研究较少。目的研究支气管哮喘儿童是否存有执行功能障碍及其具体表现形式;探讨支气管哮喘儿童执行功能与肺功能检测指标的关系。方法选取2020年6月—2022年4月就诊于苏州大学附属第三医院(常州市第一人民医院)儿科门诊且被诊断为支气管哮喘的儿童35例为哮喘儿童组,同时纳入苏州大学附属第三医院(常州市第一人民医院)儿科门诊就诊的健康儿童35例为健康对照组,采用GO/NOGO范式实验收集执行功能指标,并检测其肺功能,采用Pearson相关分析与Spearman秩相关分析探讨执行功能指标与肺功能检测指标的关系。结果哮喘儿童组击中数低于健康对照组,反应时间、反应时间变异性、漏报错误数高于健康对照组(P<0.05)。Pearson相关分析/Spearman秩相关分析结果显示,哮喘儿童反应时间与最大肺活量(VCmax)、用力肺活量(FVC)呈负相关,反应时间变异性与VCmax、FVC、第1秒用力呼气容积呈负相关,漏报错误数与用力肺活量占预计值百分比呈正相关(P<0.05)。结论与健康儿童相比,哮喘儿童持续注意力、反应速度存在障碍,哮喘儿童的执行功能障碍与肺功能检测指标具有相关性,主要表现在持续注意力、反应速度降低。

针刺阳陵泉穴对缺血性脑卒中大鼠神经功能及血管再生因子Nogo-A、VEGF表达影响

目的 观察针刺阳陵泉穴对缺血性脑卒中大鼠神经功能及血管再生因子轴突生长抑制因子(Nogo-A)、血管内皮生长因子(VEGF)表达的影响,探讨针刺促进脑重塑的作用机制。方法 将雄性SD大鼠随机分为假手术组、模型组、针刺组,采用改良线栓法制备大脑中动脉阻塞(middle cerebral artery occlusion,MCAO)大鼠模型,其中假手术组与模型组无治疗措施,针刺组给予电针双侧阳陵泉穴治疗14 d,各组基于造模术后1 d、术后14 d分为2个亚组,每个亚组各8只,采用TTC染色法检测造模情况,观察各组在不同时点神经功能缺损评分变化,通过ELISA法、RT-PCR技术、免疫组化法分别检测各组大鼠血清、脑组织中Nogo-A、VEGF表达水平。结果 与模型组比较,治疗14 d后针刺组大鼠神经功能缺损评分有所降低(P<0.01);针刺组能明显下调MCAO大鼠血清及脑组织中Nogo-A表达(P<0.05),同时可显著上调VEGF表达(P<0.05)。结论 针刺阳陵泉穴可改善MCAO大鼠神经功能缺损,且其机制可能与抑制Nogo-A表达、上调VEGF表达、促进血管再生有关。

Coordinated Planning Transmission Tasks in Heterogeneous Space Networks:A Semi-Distributed Approach

This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite systems.Specifically,we first formulate the coordinated planning problem into a mixed integer liner programming(MILP)problem based on time expanded graph.Then,the problem is transferred and reformulated into a consensus optimization framework which can be solved by satellite systems parallelly.With alternating direction method of multipliers(ADMM),a semi-distributed coordinated transmission task planning algorithm is proposed,in which each satellite system plans its own tasks based on local information and limited communication with the coordination center.Simulation results demonstrate that compared with the centralized and fully-distributed methods,the proposed semi-distributed coordinated method can strike a better balance among task complete rate,complexity,and the amount of information required to be exchanged.

低雌激素状态下大鼠海马及纹状体Nogo-A的表达变化

目的通过实验观察低雌激素状态下大鼠记忆功能的改变以及海马和纹状体Nogo-A的表达变化,阐明Nogo-A在更年期神经退行性变如记忆功能减退中可能发挥的重要作用。方法45只雌性SD大鼠分为假手术组,去卵巢组和去卵巢后雌激素治疗组,每组15只。去卵巢手术后2周开始给予药物治疗。雌激素治疗组于腹股沟皮下注射溶于无菌芝麻油的雌激素[25μg/(kg·d)]。假手术组和去卵巢组给予等量的无菌芝麻油。药物治疗6周后,通过条件恐惧训练实验观察不同组大鼠记忆功能差异,通过免疫组织化学和免疫印迹法观察Nogo-A在3组大鼠海马和纹状体的表达变化。结果与假手术组和雌激素治疗组相比,去卵巢组大鼠出现记忆功能明显减退,其海马和纹状体Nogo-A阳性神经元数量显著增加(P<0.05)。雌激素治疗组大鼠记忆功能明显改善,海马和纹状体Nogo-A阳性神经元数量显著减少(P<0.05),与假手术组差异不明显(P>0.05)。免疫印迹实验结果与上述免疫组织化学结果变化趋势一致。结论低雌激素状态下海马及纹状体Nogo-A表达上调,这可能是更年期妇女记忆功能减退的重要原因之一。

UAVs cooperative task assignment and trajectory optimization with safety and time constraints

This paper proposes new methods and strategies for Multi-UAVs cooperative attacks with safety and time constraints in a complex environment.Delaunay triangle is designed to construct a map of the complex flight environment for aerial vehicles.Delaunay-Map,Safe Flight Corridor(SFC),and Relative Safe Flight Corridor(RSFC)are applied to ensure each UAV flight trajectory's safety.By using such techniques,it is possible to avoid the collision with obstacles and collision between UAVs.Bezier-curve is further developed to ensure that multi-UAVs can simultaneously reach the target at the specified time,and the trajectory is within the flight corridor.The trajectory tracking controller is also designed based on model predictive control to track the planned trajectory accurately.The simulation and experiment results are presented to verifying developed strategies of Multi-UAV cooperative attacks.

Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud

Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of resources.In this paper,we study the task scheduling problem in the hierarchically deployed edge cloud.We first formulate the minimization of the service time of scheduled tasks in edge cloud as a combinatorial optimization problem,blue and then prove the NP-hardness of the problem.Different from the existing work that mostly designs heuristic approximation-based algorithms or policies to make scheduling decision,we propose a newly designed scheduling policy,named Joint Neural Network and Heuristic Scheduling(JNNHSP),which combines a neural network-based method with a heuristic based solution.JNNHSP takes the Sequence-to-Sequence(Seq2Seq)model trained by Reinforcement Learning(RL)as the primary policy and adopts the heuristic algorithm as the auxiliary policy to obtain the scheduling solution,thereby achieving a good balance between the quality and the efficiency of the scheduling solution.In-depth experiments show that compared with a variety of related policies and optimization solvers,JNNHSP can achieve better performance in terms of scheduling error ratio,the degree to which the policy is affected by re-sources limitations,average service latency,and execution efficiency in a typical hierarchical edge cloud.

Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

Well organized datacentres with interconnected servers constitute the cloud computing infrastructure.User requests are submitted through an interface to these servers that provide service to them in an on-demand basis.The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category.Task scheduling in cloud poses numerous challenges impacting the cloud performance.If not handled properly,user satisfaction becomes questionable.More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment.The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution.An improvised seagull optimization algorithm which combines the features of the Cuckoo search(CS)and seagull optimization algorithm(SOA)had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment.The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment.Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization(MO-ACO),ACO and Min-Min algorithms.The proposed SOA-CS technique had produced an improvement of 1.06%,4.2%,and 2.4%for makespan and had reduced the overall cost to the extent of 1.74%,3.93%and 2.77%when compared with PSO,ACO,IDEA algorithms respectively when 300 vms are considered.The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

Task offloading mechanism based on federated reinforcement learning in mobile edge computing

With the arrival of 5G,latency-sensitive applications are becoming increasingly diverse.Mobile Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much attention among researchers.To improve the Quality of Service(QoS),this study focuses on computation offloading in MEC.We consider the QoS from the perspective of computational cost,dimensional disaster,user privacy and catastrophic forgetting of new users.The QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning(FL)adaptive task offloading algorithm in MEC.The proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing delay.To solve the problems of privacy and catastrophic forgetting,we use FL to make distributed use of multiple users’data to obtain the decision model,protect data privacy and improve the model universality.In the process of FL iteration,the communication delay of individual devices is too large,which affects the overall delay cost.Therefore,we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of FL.The simulation results indicate that compared with existing schemes,the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.

lncRNA Miat通过miR-182/Nogo-A调控氧化应激所致心肌细胞凋亡的机制

目的 探究lncRNA Miat/miR-182/Nogo-A轴在心肌细胞因氧化应激发生凋亡或损伤过程中的调控机制。方法 运用H_(2)O_(2)处理模拟心肌细胞氧化应激损伤,CCK-8实验检测细胞活力;si-Miat与miR-182共转染、miR-182 mimic与上调Nogo-A共转染;qRT-PCR检测Miat、miR-182、Nogo-A mRNA的表达;Western blot检测Bax、Caspase-3、BCL-2、Nogo-A的表达;Hoechst凋亡染色检测凋亡率;生物预测软件RNAhybrid预测Miat与miR-182可能存在靶向结合关系,生物信息网站Targescan预测miR-182与Nogo-A之间的结合关系。结果 lncRNA Miat在H_(2)O_(2)处理后表达升高;抑制lncRNA Miat可减轻H_(2)O_(2)处理导致的细胞凋亡;生物预测软件RNAhybrid预测Miat与miR-182存在靶向结合位点;下调lncRNA Miat可以减轻下调miR-182在氧化应激过程中对心肌细胞凋亡的抑制作用;生物预测软件RNAhybrid预测Miat与miR-182存在靶向结合位点;上调miR-182可使Nogo-A表达下降进而抑制氧化应激导致的心肌细胞凋亡;上调miR-182可以减轻上调Nogo-A在氧化应激过程中对心肌细胞凋亡的促进作用。结论 lncRNA Miat可以通过miR-182/Nogo-A轴促进心肌细胞氧化应激所致的凋亡。Miat可作为氧化应激过程中心肌细胞凋亡或损伤的标志物。