Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

A promising approach for quantifying focal stroke modeling and assessing stroke progression:optical resolution photoacoustic microscopy photothrombosis

To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.

Reduced mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor contributes to neurodegeneration in a model of spinal and bulbar muscular atrophy pathology

Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.

数字化转型背景下高校教师共同体SECI知识模型的构建

建设高质量的教师队伍既是中国式教师教育现代化的基本特征,也是中国式教育现代化发展的必然要求。随着高等教育领域数字技术的广泛应用,高校教师共同体新的知识创生模式正在发生深刻的变化。通过对2005—2022五届国家级教学成果奖的数据分析发现,数字化转型背景下高校教师共同体的合作主体逐渐扩大、合作广度持续拓展、合作层次不断加深,这些变化为高校教师共同体知识模型的构建提供了现实依据。在知识创生螺旋理论视角下,基于对数字化转型背景下高校教师共同体知识创生发展路径的必然性分析,以SECI知识模型为基本框架,构建高校教师共同体知识创生理论模型,并辅以相应内部运行策略和外部技术支持工具,以期为高校教师共同体建设与教师知识创生提供参考。

基于知识创造SECI模型高校数字化学术生态构建研究

知识创造历经社会化、外在化、结合化和内在化四阶段,分析学术生态“四场域”——创始场、会话场、系统场、实践场存在知识创造、知识对话、知识组合、知识转化等壁垒,通过构建系统性数字化高校科研创新体系各组织间知识共享及转化过程的模型,探索以知识创造SECI螺旋提升路径闭环塑造高校数字化学术生态,提升科学研究实用性与品质性,实现高校科研创新水平与区域经济社会耦合发展,从而服务地方经济社会发展。

基于SECI模型的智能会计人才培养模式研究

文章基于SECI模型,研究智能会计人才培养模式。研究发现,SECI模型要求智能会计人才培养中考虑会计知识外化、会计知识内化、会计知识共同化、会计知识链接化等四个因素。SECI模型下智能会计人才培养存在知识学习阶段、实习阶段、应用阶段、反馈阶段。当前,每个阶段均存在问题,本文基于SECI模型提出了相应的解决策略。

采用STAMP-24Model的多组织事故分析

安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。

基于SECI模型的图书馆短视频传播营销策略研究

借鉴SECI模型中知识在创始场、对话场、整合场、练习场4个“场”中转化的理念,分析目前图书馆在短视频传播营销的现状以及存在的问题,从知识涌现的角度提出用以解决相应瓶颈问题的、面向全过程且立体的短视频传播营销策略:建立视频传播制度,优化视频表现手法,扩大传播范围,提高用户对图书馆的黏性,提升馆员的业务能力,实现嵌入式数字服务和知识泛在化传播。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

基于24Model的地铁内涝事故原因分析与评估

为降低地铁内涝事故灾害风险,基于事故致因“2-4”模型,分析了地铁内涝事故致灾因子,采用层次分析法构建了地铁内涝事故原因分析指标体系,确定了各风险因子的权重,并利用模糊综合评价法对地铁内涝事故进行了定量评估,识别出关键的影响因素。结果表明:地铁内涝事故一级指标中不安全动作与物态因素最重要,其中影响最大的指标包括擅自更改建筑设计、未按照要求检查水位情况、未及时排水、出入口不符合防汛标准等因素;习惯性不安全行为的权重位居第二,表明该指标因素较为重要,同时安全管理体系得分位居第二,表明该指标因素较易发生。对关键指标采取防范措施,可有效降低风险,从而减少地铁内涝事故的发生。

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

Application of the SECI Model Using Web Tools to Support Diabetes Self-Management and Education in the Kingdom of Saudi Arabia

The area of knowledge management, the SECI mode in particular, has great value in terms of enriching patients’ knowledge about their diseases and its complications. Despite its effectiveness, the application of knowledge management in the healthcare sector in the Kingdom of Saudi Arabia seems deficient, leading to insufficient practice of self-management and education of different prevalent diseases in the Kingdom. Moreover, the SECI model seems to be only focusing in the conversion of human knowledge and ignore knowledge stored in databases and other technological means. In this paper, we propose a framework to support diabetic patients and healthcare professionals in the Kingdom of Saudi Arabia to self-manage their disease. Data mining and the SECI model can provide effective mechanisms to support people with diabetes mellitus. The area of data mining has long been utilised to discover useful knowledge whereas the SECI model facilitates knowledge conversion between tacit and explicit knowledge among different individuals. The paper also investigates the possibilities of applying the model in the web environment and reviews the tools available in the internet that can apply the four modes of the SECI model. This review helps in providing a new median for knowledge management by addressing several cultural obstacles in the Kingdom.

基于SECI模型的教学模式在手术室护士培训中的应用

目的探讨基于SECI模型的教学模式在手术室护士护理培训中的应用效果。方法纳入2021年3月至2023年2月80例参加手术室业务培训的护士作为研究对象,根据随机数字表法分为观察组和对照组,各40例。对照组接受传统带教模式,观察组接受基于SECI模型的教学模式。比较两组学员手术室护士理论考核成绩、操作考核成绩、岗位胜任力、核心能力及护士对教学的满意度。结果观察组护士理论考核、操作考核成绩、护士胜任力量表评分、注册护士核心能力量表评分、护士对教学实施的满意度均较实施前提高(P<0.05)。结论基于SECI模型的教学模式应用于手术室护士的护理培训,可提高手术室护士理论及操作考核成绩,以及护士岗位胜任力、核心能力,护士对SECI模型的教学模式实施也更为满意。

基于SECI模型的现场工程师工匠精神建构研究

工匠精神的生成涉及认知、情感和行为的获得。目前我国高职院校对学生工匠精神的培育主要限定在认知教育和情感教育,教育策略的哲学基础是行为主义教育理论,实际操作的教学方法是理念灌输,而后果是学生对工匠精神的不理解、不接受、不践行。伴随现场工程师培养计划的实施,国家急需培养具有工匠精神的一线岗位群技能人才。本文探索以干中学、知行合一为主要特征的建构主义工匠精神的培育路径,以学生的学习行为为主体,把工匠精神的生成过程还原到工匠的成长过程。基于隐性知识和显性知识转换创造的SECI模型,把工匠精神的转化分为社会化、外显化、组合化和内隐化四个维度,提出现场工程师在社会化的真实工作场景中主动构建工匠精神的策略,并给出项目化教学的支撑路径。

24Model与LCM原因因素定义对比研究

为探究损失致因模型(LCM)原因因素定义与事故致因“2-4”模型(24Model)存在的异同和优缺点,梳理2个模型各层面原因和结果的定义,对比定义内容及其对事故原因分析等安全实务的指导作用,并以一起瓦斯爆炸事故为例加以实证分析,获得二者分析结果之间的差异。研究结果表明:LCM是首个将管理因素纳入事故致因分析的一维事件序列模型,可明确各层面原因因素的定义和因素间的逻辑关系,但部分定义存在交叉重复的问题,并没有揭示安全工作指导思想等深层次事故致因因素;24Model作为系统性事故致因模型,对各类因素的定义均以组织为主体,描述事件、事故、安全的概念内涵,划分个体安全动作、安全能力和组织安全管理体系的类别并给出含义解析,探究组织安全文化层面的问题并以32个元素体现;2个模型的事故原因分析方法均建立在对各层级原因因素定义的基础上,并适用于模型理论体系本身。

园本教研中教师知识管理的路径探析——基于SECI模型

园本教研是推动幼儿园教师专业发展的重要着力点,为幼儿园教师提供知识流通、资源共享及经验传递的场域。教师在园本教研中,能够借助知识管理实现知识传递、共享、应用与创生。SECI模型描述了知识转化及知识创新的过程,对园本教研中教师的知识管理具有指导意义。因此,本文以SECI模型为基础,从知识的社会化、外显化、综合化、内隐化四个方面,提出园本教研中幼儿园教师知识管理的可能路径:创设浸润式教研氛围,激发教师认知共鸣;打造开放式教研环境,促进教师反思性对话;依托专家理论引领,鼓励教师协同互助;借助实践内化知识,引发教师知识创新。

地域文化融入高职公共英语课程思政SECI模型构建——以湖湘文化为例

将地域优秀传统文化融入公共英语课程,是推动构建公共英语课程思政体系,促进学生思想文化素养和综合能力素质共同提升的一种创新手段。以湖湘文化为例,借助SECI模型中隐性知识和显性知识的相互作用,探索将湖湘文化思政元素融入公共英语课程的教学设计策略。通过社会化、外显化、整合化、内隐化四个知识转化阶段,将湖湘文化思政元素融入课程教学中,从社会化条件建设、外显化条件建设、整合化知识建设、内隐化优化建设、思政教学反思、评价持续推进等方面,构建高职公共英语课程思政新型模型。

A Knowledge Management Framework in Software Requirements Engineering Based on the SECI Model

Software requirements engineering deals with: elicitation, specification, and validation of software requirements. Furthermore there is a need to facilitate collaboration amongst stakeholders and analysts. Fewer efforts were deployed to support them in performing their job on a day to day basis. To solve this problem we use knowledge management for software requirements engineering. This paper proposes a knowledge management framework, based on the SECI model of knowledge creation, aimed at exploiting tacit and explicit knowledge related to software requirements within a given software project. The core part of the proposed framework is a set of four sub systems “Socializer”;“Externalizer”;“Combiner”;and “Internalizer”, attached to a couple of domain ontologies and a set of knowledge assets. Indeed we aim to facilitate a semantic based interpretation of knowledge assets related to software requirements by restricting their interpretation through the application domain and software requirements ontologies. We anticipate that this framework would be very helpful for stakeholders as well as analysts to exchange and manage their knowledge within a given software project. We show in the case study, through a virtual payroll project using the two-step approach: domain level requirements plus design level requirements, how the key elicitation SRE techniques are used during the first phase of domain requirements elicitation through the four subsystems of our framework.

Towards Building Monolithic Computational Platform for SECI Model

SECI Model has been predominantly used by researchers as a framework for analyzing knowledge creation and knowledge sharing. It generalizes the application of the framework such that it is useable for various industries and disciplines such as in education, manufacturing, software development, finance, business and others. Its popularity has gained interest among re-searchers who explored the possibility to implement SECI model in computational environment. This paper presents the monolithic system that was designed and built to allow the SECI model activities to be performed in a full manner in a single platform. SECI model activities are studied and summarized to grasp the perceptions of the researchers of what are the activities that are classified under each SECI model phases. Based on these activities, we identified some selected activities to demonstrate the possibility of implementing the SECI Model in our KFTGA system. KFTGA (Knowledge Flow Tracer and Growth Analyzer) is a knowledge sharing platform that was built to support SECI Model activities.