Innovance试剂检测D-二聚体的应用分析

目的分析Innovance试剂检测D-：-聚体（D-D）的临床应用。方法（1）同时用Innovance试剂和Plus试剂检测298例患者血浆D-D水平，比较检测结果及分析相关性。（2）查阅患者病案，回顾性研究这298例患者是否应用抗凝药肝素或低分子肝素等进行临床干预的情况，评估Innovance试剂界值的应用价值。结果Innovance试剂和Plus试剂检测D-D的结果有相关性（r=0．53，P〈O．01），Innovance试剂检测结果的阳性率大于Plus试剂，Innovance试剂检测界值为1．0mg／L时，临床干预例数为0例。结论取1．0mg／L作为界值时，可提高Innovance试剂检测D-D的临床应用价值。

PFA-100和INNOVANCE PFA-200血小板功能分析仪的临床应用及研究进展

PFA-100和INNOVANCE PFA-200血小板功能分析仪已被国外临床和科研工作者广泛使用。INNOVANCE PFA-200血小板功能分析仪是PFA-100血小板功能分析仪的升级版,在用户操作界面和电子智能化方面进行了改进,提高了使用者的便捷性,其机械工程学原理和检测结果的报告模式仍保持不变。目前,有关PFA-100和INNOVANCE PFA-200在血小板功能方面的研究很多,但在临床实验室主要应用于抗血小板药物的监测,血管性血友病(v WD)的筛查和手术出血风险的管理。通过检索同行评议期刊中发表的600篇相关文献,分析文献中有关PFA-100和INNOVANCE PFA-200血小板功能分析仪在检测原理、参考范围、影响因素和临床应用的相关内容,对其应用前景进行展望,为从事血小板功能及相关研究的临床和科研工作者提供参考。

Innovance D-dimer试剂在血栓形成早期的筛查价值

目的评价Innovance D-dimer试剂性能及其在血栓形成早期的临床筛查价值。方法采用Innovance D-dimer检测试剂,分别对86例疑似血栓患者(血栓组)和40例门诊健康体检者(对照组)进行血浆D-二聚体水平测定,并进行统计学t检验,受试者工作特征曲线(ROC)分析和筛检评价。结果血栓组D-二聚体水平[(4.14±7.29)mg/L]显著高于对照组[(0.17±0.12)mg/L],差异有统计学意义(P=0.000)。ROC曲线分析结果显示,D-二聚体水平用于血栓的临床筛查有显著统计学意义(AUC=0.938,P=0.000),D-二聚体水平越高,发生血栓的可能性越大;以D-二聚体水平为0.50mg/L为截断值,其灵敏度、特异度和阴性预测值表现良好,分别为98.08%和100.00%和98.67%。结论 Innovance D-dimer试剂适用于排除血栓形成,但诊断价值有限。

INNOVANCE~D-Dimer检测血浆D-二聚体参考范围的探讨

目的对健康人群的血清D-二聚体水平和分布做初步探讨。方法应用西门子医疗诊断公司的INNOVANCED-Dimer和日本Sysmex公司的CA1500全自动凝血分析仪检测583例健康体检者血浆D-二聚体,所得数据应用SPSS10.0统计软件包中有关处理方法进行处理分析。结果 583例健康人的血浆D-二聚体水平,x=373.6μg/LFEU,95%的参考范围120.4～626.8μg/LFEU,呈正态分布;其中,男331例,x=341.2μg/LFEU,95%的参考范围104.4～578μg/LFEU;女性252例,x=450.2μg/LFEU,95%的参考范围161.9～738.5μg/LFEU;男女血清D-二聚体水平比较,P<0.01,差异有统计学意义。并随年龄增加而升高。结论初步界定健康人血浆D-二聚体浓度(INNOVANCE!D-Dimer)95%的参考范围为120.4～626.8μg/LFEU,呈正态分布。

Innovance试剂检测D-二聚体在肾脏疾病中的应用价值

目的探讨Innovance试剂检测D-二聚体(D-D)在肾脏疾病中的应用价值。方法分别采用Innovance、Plus和Nycocard三种试剂检测149例肾脏疾病患者血清D-D水平,检测阳性临界值分别为0.55、0.25和0.30mg/L,比较三种试剂检测结果及分析其相关性。结果 Innovance、Plus及Nycocard试剂检测D-D水平分别为(2.19±3.04)mg/L、(2.78±0.56)mg/L和(0.26±0.41)mg/L,组间比较均有统计学差异(P<0.01)。Innovance试剂与Plus和Nycocard试剂的检测结果呈正相关(r=0.52、0.64,P<0.01),Nycocard试剂与Plus试剂的检测结果呈正相关(r=0.49,P<0.01)。Innovance试剂检测阳性率高于Plus、Nycocard试剂(77.85%vs.26.17%和24.83%)(P<0.01)。当Innovance试剂检测阳性临界值为1.80mg/L时,检测阳性率与Plus和Nycocard试剂相仿(P>0.05)。结论取1.80mg/L作为界值时,采用Innovance试剂可提高D-D检测在肾脏疾病患者的应用价值。

Assessment of challenges and strategies for driving energy transitions in emerging markets:A socio-technological systems perspective

The pursuit of improved quality of life standards has significantly influenced the contemporary mining model in the 21st century.This era is witnessing an unprecedented transformation driven by pressing concerns related to sustainability,climate change,the just energy transition,dynamic operating environments,and complex social challenges.Such transitions present both opportunities and obstacles.The aim of this study is to provide an extensive literature review on energy transition to identify the challenges and strategies associated with navigating transformations in energy systems.Understanding these transformations is particularly critical in the face of the severe consequences of global warming,where an accelerated energy transition is viewed as a universal remedy.Adopting a socio-technological systems perspective,specifically through the application of Actor Network Theory(ANT),this research provides a theoretical foundation while categorising challenges into five distinct domains and outlining strategies across these different dimensions.These insights are specifically tailored for emerging market countries to effectively navigate energy transition while fostering the development of resilient societies.Furthermore,our findings highlight that energy transition encompasses more than a mere technological shift;it entails fundamental changes in various systemic socio-economic imperatives.Through focusing on the role of social structures in transitions,this study makes a significant and innovative contribution to ANT,which has historically been criticised for its limited acknowledgement of social structures.Consequently,we propose an emerging market energy transition framework,which not only addresses technological aspects,but also integrates social considerations.This framework paves the way for future research and exploration of energy transition dynamics.The outcomes of this study offer valuable insights to policymakers,researchers,and practitioners engaged in the mining industry,enabling them to comprehend the multifaceted challenges involved and providing practical strategies for effective resolution.Through incorporating the social dimension into the analysis,we enhance the understanding of the complex nature of energy system transformations,facilitating a more holistic approach towards achieving sustainable and resilient energy transitions in emerging markets and beyond.

Metabolic dysfunction-associated steatotic liver disease:Navigating terminological evolution,diagnostic frontiers and therapeutic horizon-an editorial exploration

Metabolic dysfunction-associated steatotic liver disease(MASLD),once known as non-alcoholic fatty liver disease(NAFLD),represents a spectrum of liver disorders characterized by lipid accumulation within hepatocytes.The redefinition of NAFLD in 2023 marked a significant reposition in terminology,emphasizing a broader understanding of liver steatosis and its associated risks.MASLD is now recognized as a major risk factor for liver cirrhosis,hepatocellular carcinoma,and systemic complications such as cardiovascular diseases or systemic inflammation.Diagnostic challenges arise,particularly in identifying MASLD in lean individuals,necessitating updated diagnostic protocols and investing in non-invasive diagnostic tools.Therapeutically,there is an urgent need for effective treatments targeting MASLD,with emerging pharmacological options focusing on,among others,carbohydrate and lipid metabolism.Additionally,understanding the roles of bile acid metabolism,the microbiome,and dietary interventions in MASLD pathogenesis and management holds promise for innovative therapeutic approaches.There is a strong need to emphasize the importance of collaborative efforts in understanding,diagnosing,and managing MASLD to improve physicians’approaches and patient outcomes.

Improving innovation and entrepreneurship education in higher institutions: the impact of "Mass Entrepreneurship and Innovation" education on undergraduates' entrepreneurial intentions

The exploration of the way"mass entrepreneurship and innovation"(MEI)education influences students'aspirations to become entrepreneurs has grown into an important area of analysis in studies related to higher education.This research intends to examine the consequences of MEI education on students'tendency towards entrepreneurship,and to put forward methods for augmenting the teaching of innovation and entrepreneurship in private higher educational establishments.To achieve this objective,questionnaires and semi-structured interviews were employed in the study,which involved a total of 197 students and five education experts.The statistical analysis of the questionnaire data revealed that MEI education was positively related to students'entrepreneurial intentions,and that both entrepreneurial experience and family entrepreneurial background played moderating roles in this relationship.The interview findings indicated that private universities could enhance educational reforms by designing talent training programs,developing diversified curricula,and developing more professional entrepreneurial platforms to encourage students'entrepreneurial intentions.This study offers fresh insights for improving and perfecting the mechanism of innovation and entrepreneurship education in private universities.

Influence mechanism of government subsidy on the green transformation of coal company in China

The optimization of government subsidies to enhance the efficiency of coal companies’green transformation constitutes a critical component in the pursuit of global sustainability.We investigate the influence mechanism of government subsidies on the green transformation using data from the listed coal companies in China from 2007 to 2022.According to our findings and hypothesis testing,previous government subsidies did not have a significant direct impact on coal companies’green transformation.Nevertheless,government subsidies can help coal companies transition to greener practices by promoting innovative green initiatives.Furthermore,we confirmed an indirect route:that government subsidies enable the adoption of low-carbon initiatives,which in turn could facilitate the transition of coal companies towards green practices.In addition,we discovered that the coal company’s digitization will improve this indirect route.Thus,we propose increasing the effectiveness of government subsidies in facilitating coal companies’transition to green practices by focusing on technological advancements and enhancing company digitalization.

Addressing inclusion,innovation,and sustainability challenges through the lens of economic geography:Introducing the hierarchical regional innovation system

The world is facing dramatic challenges related to environmental sustainability at an accelerating pace.In this context,the field of economic geography(EG)has been playing an important role in understanding both the socioeconomic and technological dimensions of these challenges,as it deals with a variety of complementary notions and perspectives.Departing from this lens,our aim is to explore a conceptual framework that can help us to understand environmental changes relating to multi-dimensional territorial development,notably in eco-nomic contexts where inequality is high,and stratification based on hierarchies regulate social and economic life.Based on the territory concept,we propose the original notion of a hierarchical regional innovation system(HRIS)that emphasises the pervasive role of hierarchies(powers)in regional innovation systems and illustrate its value with evidence and case studies from extant literature on sustainability transitions.The HRIS can help us understand and promote development paths considering the contribution of inclusive eco-innovations(another original conceptual amalgam).Through some empirical cases from other studies in low-carbon transitions,we show the application of the HRIS(and inclusive eco-innovation)framework.In conclusion,we provide incen-tives to explore new regional innovation systems,alongside the HRIS,adapted to different regions worldwide and centred on the inclusiveness of people and places.

Editorial for the Special Issue on Natural Medicine

As the world continues to search for innovative therapeutic solutions,the field of natural medicines has emerged as a vibrant area of research.The potential of natural products to treat a wide range of diseases,coupled with the relatively low sideeffects of such products,has captured the attention of scientists,clinicians,and the general public alike.This Special Issue on Natural Medicine aims to present the latest advancements in this exciting field,highlighting both fundamental research and clinical applications.The collection of articles in this Special Issue covers a broad spectrum of topics,ranging from medicinal chemistry to biological mechanisms,and from the study of single botanic compounds to complex plant formulas.We are particularly pleased to include a comprehensive review by Yi Wang et al.on the bioactivities,mechanisms,production,and potential application of bile acids in preventing and treating infectious diseases.This review not only summarizes the current landscape of bile acids research but also highlights several promising compounds for future investigations,such as the recent interest in ursodeoxycholic acid.In a related topic,Shishan Yu’s team has developed artificial bear bile using synthetic chemistry and enzyme engineering,providing a sustainable substitute for natural bear bile.This innovation meets medical needs while addressing animal welfare issues,representing a notable progression in the field.

Callfor Papersfor Special Issue Innovative Pharmaceutical Research Facilitated by Ai

Al is revolutionizing the current paradigm of pharmaceutical research,addressing the challenges encountered at all stages of the process.Al driven drug discovery is based on biomedical big data and new algorithms to identify drug targets,screen and optimize active compounds,analyze drug properties,and facilitate drug production and quality control.On this basis,we are organize this special issue entitled“Innovative Pharmaceutical Research Driven by Al”,to collect Research articles,Review papers,and Short communications,facilitating pharmaceutical research.

Innovation and Firm Co-ownership Network in China’s Electric Vehicle Industry

Firms are embedded in complex networks,where diverse ideas combine and generate new ideas.Shareholders of firms are of-ten seen as critical external resources that have significant influence on firm innovation.The current literature tends to focus on the rela-tionship between firms and their shareholders,while paying less attention to the connections between firms with the same shareholders.This article identifies two types of network spillover effects,intra-city network effect and inter-city network effect,by visualizing the co-ownership networks in China’s electric vehicle(EV)industry.We find that firms with the same shareholders,which are defined as co-owned EV firms,are more innovative than non-co-owned ones.Furthermore,there are two dominant types of firm co-ownership ties formed by corporate and financial institution shareholders.While corporate shareholders help exploiting local tacit knowledge,financial institutions are more active in bridging inter-city connections.The conclusion is confirmed at both firm and city levels.This paper theor-izes the firm co-ownership network as a new form of institutional proximity and tested the result empirically.For policy consideration,we have emphasized the importance of building formal or informal inter-firm network,and the government should further enhance the knowledge flow channel by institutional construction.

Message from animal models and experimental medicine for 2024-Striving for excellence with distinctive features

Since its inception,Animal Models and Experimental Medicine(AMEM)has received 632 articles in total from 52 countries and regions includ-ing China,Iran,the United States,India,Nigeria,Israel,Germany,Iraq,Italy,Japan,Australia,Bangladesh,Belgium,Brazil,and Canada,among others.AMEM has become an important international exchange plat-form for innovative research achievements in the field of laboratory animal science and basic medicine.

The Future of Environmental Engineering Technology:A Disruptive Innovation Perspective

Scientific and technological revolutions and industrial transformations have accelerated the rate of innovation in environmental engineering technologies.However,few researchers have evaluated the current status and future trends of technologies.This paper summarizes the current research status in eight major subfields of environmental engineering—water treatment,air pollution control,soil/solid waste management,environmental biotechnology,environmental engineering equipment,emerging contaminants,synergistic reduction of pollution and carbon emissions,and environmental risk and intelligent management—based on bibliometric analysis and future trends in greenization,low carbonization,and intelligentization.Disruptive technologies are further identified based on discontinuous transformation,and ten such technologies are proposed,covering general and specific fields,technical links,and value sources.Additionally,the background and key innovations in disruptive technologies are elucidated in detail.This study not only provides a scientific basis for strategic decision-making,planning,and implementation in the environmental engineering field but also offers methodological guidance for the research and determination of breakthrough technologies in other areas.

Towards cultivar-oriented gene discovery for better crops

The continued expansion of the world population,increasingly inconsistent climate and shrinking agricultural resources present major challenges to crop breeding.Fortunately,the increasing ability to discover and manipulate genes creates new opportunities to develop more productive and resilient cultivars.Many genes have been described in papers as being beneficial for yield increase.However,few of them have been translated into increased yield on farms.In contrast,commercial breeders are facing gene decidophobia,i.e.,puzzled about which gene to choose for breeding among the many identified,a huge chasm between gene discovery and cultivar innovation.The purpose of this paper is to draw attention to the shortfalls in current gene discovery research and to emphasise the need to align with cultivar innovation.The methodology dictates that genetic studies not only focus on gene discovery but also pay good attention to the genetic backgrounds,experimental validation in relevant environments,appropriate crop management,and data reusability.The close of the gaps should accelerate the application of molecular study in breeding and contribute to future global food security.

Kinematic-mapping-model-guided analysis and optimization of 2-PSS&1-RR circular-rail parallel mechanism for fully steerable phased array antennas

This paper presents a systematic methodology for analyzing and optimizing an innovative antenna mount designed for phased array antennas, implemented through a novel 2-PSS&1-RR circular-rail parallel mechanism. Initially, a comparative motion analysis between the 3D model of the mount and its full-scale prototype is conducted to validate effectiveness. Given the inherent complexity, a kinematic mapping model is established between the mount and the crank-slider linkage, providing a guiding framework for subsequent analysis and optimization. Guided by this model, feasible inverse and forward solutions are derived, enabling precise identification of stiffness singularities. The concept of singularity distance is thus introduced to reflect the structural stiffness of the mount. Subsequently, also guided by the mapping model, a heuristic algorithm incorporating two backtracking procedures is developed to reduce the mount's mass. Additionally, a parametric finite-element model is employed to explore the relation between singularity distance and structural stiffness. The results indicate a significant reduction(about 16%) in the antenna mount's mass through the developed algorithm, while highlighting the singularity distance as an effective stiffness indicator for this type of antenna mount.

A novel multi-channel porous structure facilitating mass transport towards highly efficient alkaline water electrolysis

An advantageous porous architecture of electrodes is pivotal in significantly enhancing alkaline water electrolysis(AWE)efficiency by optimizing the mass transport mechanisms.This effect becomes even more pronounced when aiming to achieve elevated current densities.Herein,we employed a rapid and scalable laser texturing process to craft novel multi-channel porous electrodes.Particularly,the obtained electrodes exhibit the lowest Tafel slope of 79 mV dec^(-1)(HER)and 49 mV dec^(-1)(OER).As anticipated,the alkaline electrolyzer(AEL)cell incorporating multi-channel porous electrodes(NP-LT30)exhibited a remarkable improvement in cell efficiency,with voltage drops(from 2.28 to 1.97 V)exceeding 300 mV under 1 A cm^(-1),compared to conventional perforated Ni plate electrodes.This enhancement mainly stemmed from the employed multi-channel porous structure,facilitating mass transport and bubble dynamics through an innovative convection mode,surpassing the traditional convection mode.Furthermore,the NP-LT30-based AEL cell demonstrated exceptional durability for 300 h under 1.0 A cm^(-2).This study underscores the capability of the novel multi-channel porous electrodes to expedite mass transport in practical AWE applications.

How does network intermediary affect collaborative innovation?Evidence from Chinese listed companies

Purpose:This study aims to explore how network intermediaries influence collaborative innovation performance within inter-organizational technological collaboration networks.Design/methodology/approach:This study employs a mixed-method approach,combining quantitative social network analysis with regression techniques to investigate the role of network intermediaries in collaborative innovation performance.Using a patent dataset of Chinese industrial enterprises,the research constructs the collaboration networks and analyzes their structural positions,particularly focusing on their role as intermediaries,characterized by betweenness centrality.Negative binomial regression analysis is employed to assess how these network characteristics shape innovation outcomes.Findings:The study reveals that firms in intermediary positions enhance collaborative innovation performance,but this effect is nuanced.A key finding is that network clustering negatively moderates the intermediary-innovation relationship.Highly clustered networks,while fostering local collaboration,may limit the innovation potential of intermediaries.On the other hand,relationship strength,measured by collaboration intensity and trust among firms,positively moderates the intermediary-innovation link.Research limitations:This study has several limitations that present opportunities for further research.The reliance on quantitative social network analysis may overlook the complexity of intermediaries’roles,and future studies could benefit from incorporating qualitative methods to better understand cultural and institutional factors.Additionally,cross-country comparisons are needed to assess the consistency of these dynamics in different contexts.Practical implications:The study offers practical insights for firms and policymakers.Organizations should strategically position themselves as network intermediaries to access diverse information and resources,thereby improving innovation performance.Building strong trust helps using network intermediary advantages.For firms in highly clustered networks,it is important to seek external partners to avoid limiting their exposure to new ideas and technologies.This research emphasizes the need to balance network diversity with relationship strength for sustained innovation.Originality/value:This research contributes to the literature by offering new insights into the role of network intermediaries,presenting a comprehensive framework for understanding the interaction between network dynamics and firm innovation.

Image Fusion Using Wavelet Transformation and XGboost Algorithm

Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same item.Image fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual perception.Image fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on application.The research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet transform.Image fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient manner.More precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain characteristics.To tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is proposed.The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice.The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images.This study enhances the performance of the eXtreme Gradient Boosting(XGBoost)algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection.The performance of images is improved by segmenting them employing the K-Means algorithm.The segmentation method aids in identifying specific regions of interest,using Particle Swarm Optimization(PCA)for trait selection and XGBoost for data classification.Extensive trials confirm the model’s exceptional visual performance,achieving an accuracy of up to 97.067%and providing good objective indicators.