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A systematic review on the state-of-the-art and research gaps regarding inorganic and carbon-based multicomponent and high-aspect ratio nanomaterials 系统回顾无机和碳基多成分高pect ratio 纳米材料的最新进展和研究空白
IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-10-19 DOI: 10.1016/j.csbj.2024.10.020
This review explores the state-of-the-art with respect to multicomponent nanomaterials (MCNMs) and high aspect ratio nanomaterials (HARNs), with a focus on their physicochemical characterisation, applications, and hazard, fate, and risk assessment. Utilising the PRISMA approach, this study investigates specific MCNMs including cerium-zirconium mixtures (CexZryO2) and ZnO nanomaterials doped with transition metals and rare earth elements, as well as Titanium Carbide (TiC) nanomaterials contained in Ti-6Al-4V alloy powders. HARNs of interest include graphene, carbon-derived nanotubes (CNTs), and metallic nanowires, specifically Ag-based nanowires. The review reveals a significant shift in research and innovation (R&I) efforts towards these advanced nanomaterials due to their unique properties and functionalities that promise enhanced performance across various applications including photocatalysis, antibacterial and biomedical uses, and advanced manufacturing. Despite the commercial potential of MCNMs and HARNs, the review identifies critical gaps in our understanding of their environmental fate and transformations upon exposure to new environments, and their potential adverse effects on organisms and the environment. The findings underscore the necessity for further research focused on the environmental transformations and toxicological profiles of these nanomaterials to inform Safe and Sustainable by Design (SSbD) strategies. This review contributes to the body of knowledge by cataloguing current research, identifying research gaps, and highlighting future directions for the development of MCNMs and HARNs, facilitating their safe and effective integration into industry.
本综述探讨了多组分纳米材料(MCNMs)和高纵横比纳米材料(HARNs)的最新进展,重点是其物理化学特性、应用以及危害、归宿和风险评估。利用 PRISMA 方法,本研究调查了特定的 MCNMs,包括铈锆混合物(CexZryO2)和掺杂过渡金属和稀土元素的 ZnO 纳米材料,以及 Ti-6Al-4V 合金粉末中的碳化钛 (TiC) 纳米材料。值得关注的 HARNs 包括石墨烯、碳衍生纳米管 (CNT) 和金属纳米线,特别是银基纳米线。综述显示,研究与创新(R&I)工作正朝着这些先进纳米材料的方向发生重大转变,因为它们具有独特的性质和功能,有望在光催化、抗菌和生物医学用途以及先进制造等各种应用中提高性能。尽管 MCNMs 和 HARNs 具有巨大的商业潜力,但本综述指出了我们对其环境归宿、暴露于新环境后的转化及其对生物和环境的潜在不利影响的认识存在重大差距。研究结果强调了进一步研究这些纳米材料的环境转化和毒理学特征的必要性,以便为安全和可持续设计 (SSbD) 战略提供信息。本综述通过对当前研究进行编目、确定研究空白点以及强调 MCNMs 和 HARNs 的未来发展方向,促进其安全有效地融入工业领域,从而为知识体系做出贡献。
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引用次数: 0
An AI-driven multiscale methodology to develop transparent wood as sustainable functional material by using the SSbD concept 一种人工智能驱动的多尺度方法,利用 SSbD 概念将透明木材开发为可持续功能材料
IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-10-18 DOI: 10.1016/j.csbj.2024.10.022
Efficient design, production, and optimization of new safe and sustainable by design materials for various industrial sectors is an on-going challenge for our society, poised to escalate in the future. Wood-based composite materials offer an attractive sustainable alternative to high impact materials such as glass and polymers and have been the focus of experimental research and development for years. Computational and AI-based materials design provides significant speed-up the development of these materials compared to traditional methods of development. However, reliable numerical models are essential for achieving this goal. The AI-TranspWood project, recently funded by the European Commission, has the ambition to develop such computational and AI-based tools in the context of transparent wood (TW), a promising composite with potential applications in various industrial fields. In this project we advance the development specifically by using an Artificial Intelligence (AI)-driven multiscale methodology.
高效设计、生产和优化用于各工业领域的安全、可持续的新型材料,是我们社会面临的一项持续挑战,而且在未来还将不断升级。木基复合材料为玻璃和聚合物等高冲击材料提供了极具吸引力的可持续替代品,多年来一直是实验研究和开发的重点。与传统开发方法相比,基于计算和人工智能的材料设计大大加快了这些材料的开发速度。然而,可靠的数值模型对于实现这一目标至关重要。最近由欧盟委员会资助的 AI-TranspWood 项目的目标是在透明木材(TW)的背景下开发这种基于计算和人工智能的工具。在本项目中,我们特别采用了人工智能(AI)驱动的多尺度方法来推进开发工作。
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引用次数: 0
Using patient-generated health data more efficient and effectively to facilitate the implementation of value-based healthcare in the EU – Innovation report 更高效、更有效地利用患者生成的健康数据,促进在欧盟实施以价值为基础的医疗保健--创新报告
IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-10-18 DOI: 10.1016/j.csbj.2024.10.026
Healthcare services and products are rapidly changing due to the development of new technologies, offering relevant solutions to improve patient outcomes. Patient-Generated Health Data and knowledge-sharing across the European Union (EU) has a great potential of making healthcare provision more effective and efficient by putting the patient at the centre of the healthcare process. While such initiatives have been taken before, a uniting and overarching approach is still missing. The EU-funded IMPROVE project will develop an evidence-based and actual framework to effectively leverage the added value of people-centred integrated healthcare solutions, using predominantly PROMs, PPI, PREMs, and other Patient-Generated Health Data (PGHD). As a result, the project facilitates the effective and efficient implementation of Value-Based Healthcare across the EU by putting the patient central in the healthcare process.
由于新技术的发展,医疗保健服务和产品正在迅速发生变化,为改善患者的治疗效果提供了相关的解决方案。患者生成的健康数据和欧盟(EU)范围内的知识共享具有巨大潜力,可通过将患者置于医疗保健过程的中心,使医疗保健服务更加有效和高效。虽然之前已经采取了此类举措,但仍然缺少一个统一的总体方法。欧盟资助的 IMPROVE 项目将开发一个以证据为基础的实际框架,主要利用 PROMs、PPI、PREMs 和其他患者生成的健康数据 (PGHD),有效利用以人为本的综合医疗解决方案的附加值。因此,该项目通过在医疗保健过程中以患者为中心,促进在整个欧盟有效和高效地实施以价值为基础的医疗保健。
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引用次数: 0
Easy-MODA: Simplifying standardised registration of scientific simulation workflows through MODA template guidelines powered by the Enalos Cloud Platform Easy-MODA:通过Enalos云平台提供的MODA模板指南简化科学模拟工作流程的标准化注册
IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-10-18 DOI: 10.1016/j.csbj.2024.10.018
Modelling Data (MODA) reporting guidelines have been proposed for common terminology and for recording metadata for physics-based materials modelling and simulations in a CEN Workshop Agreement (CWA 17284:2018). Their purpose is similar to that of the Quantitative Structure-Activity Relationship (QSAR) model report form (QMRF) that aims to increase industry and regulatory confidence in QSAR models, but for a wider range of model types. Recently, the WorldFAIR project’s nanomaterials case study suggested that both QMRF and MODA templates are an important means to enhance compliance of nanoinformatics models, and their underpinning datasets, with the FAIR principles (Findable, Accessible, Interoperable, Reusable). Despite the advances in computational modelling of materials properties and phenomena, regulatory uptake of predictive models has been slow. This is, in part, due to concerns about lack of validation of complex models and lack of documentation of scientific simulations. The models are often complex, output can be hardware- and software-dependent, and there is a lack of shared standards. Despite advocating for standardised and transparent documentation of simulation protocols through its templates, the MODA guidelines are rarely used in practice by modellers because of a lack of tools for automating their creation, sharing, and storage. They also suffer from a paucity of user guidance on their use to document different types of models and systems. Such tools exist for the more well-established QMRF and have aided widespread implementation of QMRFs. To address this gap, a simplified procedure and online tool, Easy-MODA, has been developed to guide users through MODA creation for physics-based and data-based models, and their various combinations. Easy-MODA is available as a web-tool on the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.com/insight/moda/). The tool streamlines the creation of detailed MODA documentation, even for complex multi-model workflows, and facilitates the registration of MODA workflows and documentation in a database, thereby increasing their Findability and thus Re-usability. This enhances communication, interoperability, and reproducibility in multiscale materials modelling and improves trust in the models through improved documentation. The use of the Easy-MODA tool is exemplified by a case study for nanotoxicity evaluation, involving interlinked models and data transformation, to demonstrate the effectiveness of the tool in integrating complex computational methodologies and its significant role in improving the FAIRness of scientific simulations.
在 CEN 研讨会协议(CWA 17284:2018)中提出了建模数据(MODA)报告指南,用于基于物理的材料建模和模拟的通用术语和元数据记录。其目的与定量结构-活性关系(QSAR)模型报告表(QMRF)类似,旨在提高行业和监管机构对 QSAR 模型的信心,但适用于更广泛的模型类型。最近,WorldFAIR 项目的纳米材料案例研究表明,QMRF 和 MODA 模板都是提高纳米信息模型及其基础数据集符合 FAIR 原则(可查找、可访问、可互操作、可重复使用)的重要手段。尽管在材料特性和现象的计算建模方面取得了进展,但监管机构对预测模型的采用却一直很缓慢。部分原因是人们担心复杂的模型缺乏验证,科学模拟缺乏文件记录。模型通常很复杂,输出结果可能取决于硬件和软件,而且缺乏共享标准。尽管 MODA 准则提倡通过模板对模拟协议进行标准化和透明化记录,但由于缺乏自动创建、共享和存储工具,建模人员在实践中很少使用 MODA 准则。此外,关于如何使用它们来记录不同类型的模型和系统,也缺乏用户指南。这些工具适用于较为成熟的 QMRF,并有助于 QMRF 的广泛实施。为了弥补这一不足,我们开发了一种简化程序和在线工具--Easy-MODA,以指导用户创建基于物理和数据的模型及其各种组合的 MODA。Easy-MODA是Enalos云平台(https://www.enaloscloud.novamechanics.com/insight/moda/)上的一个网络工具。该工具简化了详细MODA文档的创建,即使是复杂的多模型工作流程也不例外,并有助于在数据库中注册MODA工作流程和文档,从而提高其可查找性和可重用性。这增强了多尺度材料建模的交流、互操作性和可重复性,并通过改进文档提高了对模型的信任度。Easy-MODA 工具的使用以纳米毒性评估的案例研究为例,其中涉及相互关联的模型和数据转换,以证明该工具在整合复杂计算方法方面的有效性及其在提高科学模拟的公平性方面的重要作用。
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引用次数: 0
NanoBioAccumulate: Modelling the uptake and bioaccumulation of nanomaterials in soil and aquatic invertebrates via the Enalos DIAGONAL Cloud Platform 纳米生物累积:通过 Enalos DIAGONAL 云平台模拟纳米材料在土壤和水生无脊椎动物中的吸收和生物累积性
IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-10-17 DOI: 10.1016/j.csbj.2024.09.028
NanoBioAccumulate is a free-to-use web-based tool hosted on the Enalos DIAGONAL Cloud Platform (https://www.enaloscloud.novamechanics.com/diagonal/pbpk/) that provides users with the capability to model and predict the uptake and bioaccumulation of nanomaterials (NMs) by soil and aquatic invertebrates using two common first-order one-compartment biokinetic models. NanoBioAccumulate offers an approach for comprehensively analyzing the kinetics of different forms of NMs via a nonlinear fitting feature, integrating them with environmental fate models, and considering important physiological processes. NanoBioAccumulate overcomes the constraint of requiring prior knowledge of kinetic rate constants associated with the biokinetic models and eliminates the need for external statistical analysis software as it quantifies the kinetic rate constants and other constants through the application of nonlinear regression, using user-provided experimental data. Furthermore, NanoBioAccumulate incorporates statistical analysis measures like the adjusted R-squared and the bias-corrected Akaike information criterion, allowing for assessment of the goodness-of-fit of the two different biokinetic models, assisting in the identification of the best-performing model for a specific nanoform and its uptake kinetics by a specific invertebrate. The tool also includes model scenarios, retrieved from literature, which involve examining the exposure of soil and aquatic invertebrates to various types of NMs such as TiO2, SiO2, C60, graphene, graphene oxide (GO), Au, Ag and its ionic control AgNO3. These model scenarios aim to enhance understanding of the uptake and elimination rates exhibited by different NM-species. NanoBioAccumulate features advanced integration capabilities, enabled by an extensive Application Programming Interface (API). This functionality promotes efficient data exchange and interoperability with other software and web applications, significantly expanding its utility in research, regulatory risk assessment and environmental surveillance and monitoring contexts. The inclusion of a user-friendly Graphical User Interface (GUI) in NanoBioAccumulate greatly improves the overall user experience by simplifying complex tasks and eliminating the need for programming proficiency, thereby expanding the tool's applicability to a diverse range of users across various fields such as environmental research, monitoring, and regulation.
NanoBioAccumulate 是一种基于网络的免费工具,托管在 Enalos DIAGONAL 云平台 (https://www.enaloscloud.novamechanics.com/diagonal/pbpk/) 上,用户可以使用两种常见的一阶单室生物动力学模型来模拟和预测土壤和水生无脊椎动物对纳米材料 (NMs) 的吸收和生物累积。NanoBioAccumulate 提供了一种方法,可通过非线性拟合功能全面分析不同形式的纳米材料动力学,将其与环境归宿模型相结合,并考虑重要的生理过程。NanoBioAccumulate 克服了需要事先了解与生物动力学模型相关的动力学速率常数的限制,无需外部统计分析软件,因为它通过应用非线性回归,利用用户提供的实验数据,量化了动力学速率常数和其他常数。此外,NanoBioAccumulate 还采用了调整后的 R 平方和偏差校正后的 Akaike 信息标准等统计分析措施,可评估两种不同生物动力学模型的拟合优度,帮助确定特定纳米形式及其被特定无脊椎动物吸收动力学的最佳模型。该工具还包括从文献中检索到的模型情景,涉及研究土壤和水生无脊椎动物暴露于各种类型的纳米介质(如 TiO2、SiO2、C60、石墨烯、氧化石墨烯 (GO)、金、银及其离子控制 AgNO3)的情况。这些模型方案旨在加深对不同 NM 种类所表现出的吸收和消除速率的理解。NanoBioAccumulate 具有先进的集成功能,通过广泛的应用编程接口 (API) 实现。该功能促进了与其他软件和网络应用程序的高效数据交换和互操作性,极大地扩展了其在研究、监管风险评估以及环境监测和监控方面的用途。NanoBioAccumulate 中包含一个用户友好型图形用户界面 (GUI),通过简化复杂任务和消除对编程熟练度的需求,极大地改善了整体用户体验,从而将该工具的适用范围扩大到环境研究、监测和监管等各个领域的不同用户。
{"title":"NanoBioAccumulate: Modelling the uptake and bioaccumulation of nanomaterials in soil and aquatic invertebrates via the Enalos DIAGONAL Cloud Platform","authors":"","doi":"10.1016/j.csbj.2024.09.028","DOIUrl":"10.1016/j.csbj.2024.09.028","url":null,"abstract":"<div><div><em>NanoBioAccumulate</em> is a free-to-use web-based tool hosted on the Enalos DIAGONAL Cloud Platform (<span><span>https://www.enaloscloud.novamechanics.com/diagonal/pbpk/</span><svg><path></path></svg></span>) that provides users with the capability to model and predict the uptake and bioaccumulation of nanomaterials (NMs) by soil and aquatic invertebrates using two common first-order one-compartment biokinetic models. <em>NanoBioAccumulate</em> offers an approach for comprehensively analyzing the kinetics of different forms of NMs via a nonlinear fitting feature, integrating them with environmental fate models, and considering important physiological processes. <em>NanoBioAccumulate</em> overcomes the constraint of requiring prior knowledge of kinetic rate constants associated with the biokinetic models and eliminates the need for external statistical analysis software as it quantifies the kinetic rate constants and other constants through the application of nonlinear regression, using user-provided experimental data. Furthermore, <em>NanoBioAccumulate</em> incorporates statistical analysis measures like the adjusted R-squared and the bias-corrected Akaike information criterion, allowing for assessment of the goodness-of-fit of the two different biokinetic models, assisting in the identification of the best-performing model for a specific nanoform and its uptake kinetics by a specific invertebrate. The tool also includes model scenarios, retrieved from literature, which involve examining the exposure of soil and aquatic invertebrates to various types of NMs such as TiO<sub>2</sub>, SiO<sub>2</sub>, C<sub>60</sub>, graphene, graphene oxide (GO), Au, Ag and its ionic control AgNO<sub>3</sub>. These model scenarios aim to enhance understanding of the uptake and elimination rates exhibited by different NM-species. <em>NanoBioAccumulate</em> features advanced integration capabilities, enabled by an extensive Application Programming Interface (API). This functionality promotes efficient data exchange and interoperability with other software and web applications, significantly expanding its utility in research, regulatory risk assessment and environmental surveillance and monitoring contexts. The inclusion of a user-friendly Graphical User Interface (GUI) in <em>NanoBioAccumulate</em> greatly improves the overall user experience by simplifying complex tasks and eliminating the need for programming proficiency, thereby expanding the tool's applicability to a diverse range of users across various fields such as environmental research, monitoring, and regulation.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
BioKGrapher: Initial evaluation of automated knowledge graph construction from biomedical literature BioKGrapher:从生物医学文献中自动构建知识图谱的初步评估
IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-10-17 DOI: 10.1016/j.csbj.2024.10.017
Background The growth of biomedical literature presents challenges in extracting and structuring knowledge. Knowledge Graphs (KGs) offer a solution by representing relationships between biomedical entities. However, manual construction of KGs is labor-intensive and time-consuming, highlighting the need for automated methods. This work introduces BioKGrapher, a tool for automatic KG construction using large-scale publication data, with a focus on biomedical concepts related to specific medical conditions. BioKGrapher allows researchers to construct KGs from PubMed IDs.
Methods The BioKGrapher pipeline begins with Named Entity Recognition and Linking (NER+NEL) to extract and normalize biomedical concepts from PubMed, mapping them to the Unified Medical Language System (UMLS). Extracted concepts are weighted and re-ranked using Kullback-Leibler divergence and local frequency balancing. These concepts are then integrated into hierarchical KGs, with relationships formed using terminologies like SNOMED CT and NCIt. Downstream applications include multi-label document classification using Adapter-infused Transformer models.
Results BioKGrapher effectively aligns generated concepts with clinical practice guidelines from the German Guideline Program in Oncology (GGPO), achieving F1-Scores of up to 0.6. In multi-label classification, Adapter-infused models using a BioKGrapher cancer-specific KG improved micro F1-Scores by up to 0.89 percentage points over a non-specific KG and 2.16 points over base models across three BERT variants. The drug-disease extraction case study identified indications for Nivolumab and Rituximab.
Conclusion BioKGrapher is a tool for automatic KG construction, aligning with the GGPO and enhancing downstream task performance. It offers a scalable solution for managing biomedical knowledge, with potential applications in literature recommendation, decision support, and drug repurposing.
背景 生物医学文献的增长给知识的提取和结构化带来了挑战。知识图谱(KG)通过表示生物医学实体之间的关系提供了一种解决方案。然而,手工构建知识图谱耗费大量人力和时间,因此需要自动化方法。这项工作介绍了 BioKGrapher,这是一种利用大规模出版物数据自动构建知识图谱的工具,重点关注与特定医疗条件相关的生物医学概念。方法 BioKGrapher 管道从命名实体识别和链接(NER+NEL)开始,提取 PubMed 中的生物医学概念并将其规范化,将其映射到统一医学语言系统(UMLS)。利用库尔巴克-莱伯勒发散和局部频率平衡对提取的概念进行加权和重新排序。然后将这些概念整合到分层 KG 中,并使用 SNOMED CT 和 NCIt 等术语形成关系。结果 BioKGrapher 有效地将生成的概念与德国肿瘤学指南项目(GGPO)的临床实践指南相一致,F1 分数高达 0.6。在多标签分类中,使用 BioKGrapher 癌症特异性 KG 的适配器注入模型比非特异性 KG 的微观 F1 分数提高了 0.89 个百分点,比三种 BERT 变体的基础模型提高了 2.16 个百分点。药物-疾病提取案例研究确定了 Nivolumab 和 Rituximab 的适应症。它为管理生物医学知识提供了一个可扩展的解决方案,在文献推荐、决策支持和药物再利用方面具有潜在的应用价值。
{"title":"BioKGrapher: Initial evaluation of automated knowledge graph construction from biomedical literature","authors":"","doi":"10.1016/j.csbj.2024.10.017","DOIUrl":"10.1016/j.csbj.2024.10.017","url":null,"abstract":"<div><div><strong>Background</strong> The growth of biomedical literature presents challenges in extracting and structuring knowledge. Knowledge Graphs (KGs) offer a solution by representing relationships between biomedical entities. However, manual construction of KGs is labor-intensive and time-consuming, highlighting the need for automated methods. This work introduces BioKGrapher, a tool for automatic KG construction using large-scale publication data, with a focus on biomedical concepts related to specific medical conditions. BioKGrapher allows researchers to construct KGs from PubMed IDs.</div><div><strong>Methods</strong> The BioKGrapher pipeline begins with Named Entity Recognition and Linking (NER+NEL) to extract and normalize biomedical concepts from PubMed, mapping them to the Unified Medical Language System (UMLS). Extracted concepts are weighted and re-ranked using Kullback-Leibler divergence and local frequency balancing. These concepts are then integrated into hierarchical KGs, with relationships formed using terminologies like SNOMED CT and NCIt. Downstream applications include multi-label document classification using Adapter-infused Transformer models.</div><div><strong>Results</strong> BioKGrapher effectively aligns generated concepts with clinical practice guidelines from the German Guideline Program in Oncology (GGPO), achieving <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-Scores of up to 0.6. In multi-label classification, Adapter-infused models using a BioKGrapher cancer-specific KG improved micro <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-Scores by up to 0.89 percentage points over a non-specific KG and 2.16 points over base models across three BERT variants. The drug-disease extraction case study identified indications for Nivolumab and Rituximab.</div><div><strong>Conclusion</strong> BioKGrapher is a tool for automatic KG construction, aligning with the GGPO and enhancing downstream task performance. It offers a scalable solution for managing biomedical knowledge, with potential applications in literature recommendation, decision support, and drug repurposing.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Toxicogenomic assessment of in vitro macrophages exposed to profibrotic challenge reveals a sustained transcriptomic immune signature 对暴露于损伤性挑战的体外巨噬细胞进行的毒物基因组学评估揭示了持续的转录组免疫特征
IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-10-08 DOI: 10.1016/j.csbj.2024.10.010
Immune signalling is a crucial component in the progression of fibrosis. However, approaches for the safety assessment of potentially profibrotic substances, that provide information on mechanistic immune responses, are underdeveloped. This study aimed to develop a novel framework for assessing the immunotoxicity of fibrotic compounds. We exposed macrophages in vitro to multiple sublethal concentrations of the profibrotic agent bleomycin, over multiple timepoints, and generated RNA sequencing data. Using a toxicogenomic approach, we performed dose-dependent analysis to discover genes dysregulated by bleomycin exposure in a dose-responsive manner. A subset of immune genes displayed a sustained dose-dependent and differential expression response to profibrotic challenge. An immunoassay revealed cytokines and proteinases responding to bleomycin exposure that closely correlated to transcriptomic alterations, underscoring the integration between transcriptional immune response and external immune signalling activity. This study not only increases our understanding of the immunological mechanisms of fibrosis, but also offers an innovative framework for the toxicological evaluation of substances with potential fibrogenic effects on macrophage signalling. Our work brings a new immunotoxicogenomic direction for hazard assessment of fibrotic compounds, through the implementation of a time and resource efficient in vitro methodology.
免疫信号是纤维化进程中的一个重要组成部分。然而,对潜在的促纤维化物质进行安全评估、提供机理免疫反应信息的方法还不完善。本研究旨在开发一种评估纤维化化合物免疫毒性的新框架。我们在体外将巨噬细胞暴露于多种亚致死浓度的促纤维化物质博莱霉素中,并在多个时间点进行检测,同时生成 RNA 测序数据。利用毒性基因组学方法,我们进行了剂量依赖性分析,以发现博莱霉素暴露以剂量反应方式导致的基因失调。免疫基因子集对损伤性挑战表现出持续的剂量依赖性和差异表达反应。免疫测定显示,细胞因子和蛋白酶对暴露于博莱霉素的反应与转录组的改变密切相关,这突显了转录免疫反应与外部免疫信号活动之间的整合。这项研究不仅加深了我们对纤维化免疫学机制的理解,还为对巨噬细胞信号具有潜在纤维化效应的物质进行毒理学评估提供了一个创新框架。我们的工作通过实施一种省时、省资源的体外方法,为纤维化化合物的危害评估提供了一个新的免疫毒性基因组学方向。
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引用次数: 0
Transforming in-clinic post-operative and intermediate care with cosinuss° 利用 cosinuss° 改变诊所内的术后和中间护理
IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-10-05 DOI: 10.1016/j.csbj.2024.10.002
Continuous, mobile patient monitoring plays a critical role in healthcare, particularly for post-surgery, intermediate care in clinics. The implementation of vital signs monitoring technology enables healthcare professionals to triage patients effectively by maintaining real-time awareness of their health status and allowing for prompt intervention when necessary. This technology supports early mobilization and facilitates the detection of potential complications such as post-surgical sepsis. cosinuss° technology has been evaluated in various studies, in terms of its accuracy in capturing vital parameters and its usability, emphasizing its potential to enhance intermediate patient care and outcomes. This report outlines the design and implementation of cosinuss° Health patient monitoring solution for use in intermediate, postoperative clinic settings. It presents the results and insights from three recent, in-clinic applications, discussing both technical and practical aspects, clinical processes, and the reported satisfaction from both patients and medical caregivers. The findings highlight the promising potential of cosinuss° Health on improving patient monitoring and overall clinical outcomes.
持续的移动病人监测在医疗保健中发挥着至关重要的作用,尤其是在手术后和诊所的中间护理中。生命体征监测技术的实施使医护人员能够实时了解病人的健康状况,并在必要时及时进行干预,从而有效地对病人进行分流。cosinuss° 技术在捕捉生命体征参数的准确性和可用性方面接受了多项研究评估,强调了它在加强中间护理和提高疗效方面的潜力。本报告概述了 cosinuss° 健康患者监测解决方案在中级术后诊所环境中的设计和实施。报告介绍了最近三次诊所内应用的结果和见解,讨论了技术和实践方面、临床流程以及患者和医疗护理人员的满意度报告。研究结果凸显了 cosinuss° Health 在改善患者监护和整体临床效果方面的巨大潜力。
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引用次数: 0
Text mining approach for feature extraction and cartilage disease grade classification using knee MRI radiology reports 利用膝关节磁共振成像放射学报告进行特征提取和软骨病等级分类的文本挖掘方法
IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-10-05 DOI: 10.1016/j.csbj.2024.10.003
MRI radiology reporting processes can be improved by exploiting structured and semantically labelled data that can be fed to artificial intelligence (AI) tools. AI-based tools assisting radiology reporting can help to automatically individuate cartilage grading in textual magnetic resonance imaging (MRI) reports, thus supporting clinicians' decisions regarding medical imaging utilisation, diagnosis and treatment. In this study, we extracted information (clinical findings, observations, anatomical regions, etc.) and classified knee cartilage degradation from medical reports utilising transfer-learning techniques applied to the Bidirectional Encoder Representations from Transformers (BERT) model and its variants, pre-trained on an Italian-language corpus. To realise this objective, we used a dataset of 750 MRI knee reports written by three radiologists who contributed to a manual annotation process to perform text classification (TC) and named entity recognition (NER) tasks. The dataset was obtained from an internal database of the IRCCS SYNLAB SDN. Seventy percent of the dataset was used for training, 10% was used for validation and 20% was used for testing. The best-performing configurations for NER and TC tasks were based on the pre-trained BERT model. The macro F1-scores obtained with the NER and TC models are 0.89 and 0.81, respectively. The accuracies calculated on the test set for both tasks are 0.96 and 0.99, respectively.
核磁共振成像(MRI)放射学报告流程可通过利用结构化和语义标注数据加以改进,这些数据可输入人工智能(AI)工具。基于人工智能的放射学报告辅助工具可帮助自动对文本磁共振成像(MRI)报告中的软骨分级进行个性化处理,从而为临床医生在医学影像利用、诊断和治疗方面的决策提供支持。在这项研究中,我们从医学报告中提取信息(临床发现、观察结果、解剖区域等)并对膝关节软骨退化进行分类,将迁移学习技术应用于在意大利语语料库上预先训练的变压器双向编码器表示(BERT)模型及其变体。为实现这一目标,我们使用了由三位放射科医生撰写的 750 份核磁共振膝关节成像报告数据集,他们参与了手动注释过程,以执行文本分类(TC)和命名实体识别(NER)任务。该数据集来自 IRCCS SYNLAB SDN 的内部数据库。数据集的 70% 用于训练,10% 用于验证,20% 用于测试。NER 和 TC 任务的最佳配置基于预训练的 BERT 模型。NER 和 TC 模型获得的宏观 F1 分数分别为 0.89 和 0.81。在测试集上计算出的这两项任务的准确率分别为 0.96 和 0.99。
{"title":"Text mining approach for feature extraction and cartilage disease grade classification using knee MRI radiology reports","authors":"","doi":"10.1016/j.csbj.2024.10.003","DOIUrl":"10.1016/j.csbj.2024.10.003","url":null,"abstract":"<div><div>MRI radiology reporting processes can be improved by exploiting structured and semantically labelled data that can be fed to artificial intelligence (AI) tools. AI-based tools assisting radiology reporting can help to automatically individuate cartilage grading in textual magnetic resonance imaging (MRI) reports, thus supporting clinicians' decisions regarding medical imaging utilisation, diagnosis and treatment. In this study, we extracted information (clinical findings, observations, anatomical regions, etc.) and classified knee cartilage degradation from medical reports utilising transfer-learning techniques applied to the Bidirectional Encoder Representations from Transformers (BERT) model and its variants, pre-trained on an Italian-language corpus. To realise this objective, we used a dataset of 750 MRI knee reports written by three radiologists who contributed to a manual annotation process to perform text classification (TC) and named entity recognition (NER) tasks. The dataset was obtained from an internal database of the IRCCS SYNLAB SDN. Seventy percent of the dataset was used for training, 10% was used for validation and 20% was used for testing. The best-performing configurations for NER and TC tasks were based on the pre-trained BERT model. The macro F1-scores obtained with the NER and TC models are 0.89 and 0.81, respectively. The accuracies calculated on the test set for both tasks are 0.96 and 0.99, respectively.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
NanoTube Construct: A web tool for the digital construction of nanotubes of single-layer materials and the calculation of their atomistic descriptors powered by Enalos Cloud Platform 纳米管构建:由 Enalos 云平台驱动的单层材料纳米管数字构建及其原子描述符计算网络工具
IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-10-05 DOI: 10.1016/j.csbj.2024.09.023
NanoTube Construct is a web tool for the digital construction of nanotubes based on real and hypothetical single-layer materials including carbon-based materials such as graphene, graphane, graphyne polymorphs, graphidiyene and non-carbon materials such as silicene, germanene, boron nitride, hexagonal bilayer silica, haeckelite silica, molybdene disulfide and tungsten disulfide. Contrary to other available tools, NanoTube Construct has the following features: a) it is not limited to zero thickness materials with specific symmetry, b) it applies energy minimisation to the geometrically constructed Nanotubes to generate realistic ones, c) it derives atomistic descriptors (e.g., the average potential energy per atom, the average coordination number, etc.), d) it provides the primitive unit cell of the constructed Nanotube which corresponds to the selected rolling vector (i.e., the direction in which the starting nanosheet is rolled to form a tube), e) it calculates whether the Nanotube or its corresponding nanosheet is more energetically stable and f) it allows negative chirality indexes. Application of NanoTube Construct for the construction of energy minimised graphane and molybdenum disulfide nanotubes are presented, showcasing the tool's capability. NanoTube Construct is freely accessible through the Enalos Cloud Platform (https://enaloscloud.novamechanics.com/diagonal/nanotube/).
NanoTube Construct 是一种基于真实和假设单层材料的纳米管数字构建网络工具,这些材料包括碳基材料,如石墨烯、石墨烷、石墨炔多晶体、石墨二烯和非碳材料,如硅烯、锗烯、氮化硼、六角双层二氧化硅、白炭黑二氧化硅、二硫化钼和二硫化钨。与其他可用工具不同,NanoTube Construct 具有以下特点:a) 它不局限于具有特定对称性的零厚度材料;b) 它将能量最小化应用于几何构造的纳米管,以生成逼真的纳米管;c) 它可得出原子描述符(例如,每个原子的平均势能、每个原子的平均势能、每个原子的平均势能、每个原子的平均势能、每个原子的平均势能、每个原子的平均势能、每个原子的平均势能、每个原子的平均势能、每个原子的平均势能)、每个原子的平均势能、平均配位数等),d) 提供与所选滚动矢量(即起始纳米片滚动形成管子的方向)相对应的纳米管原始单元格,e) 计算纳米管或其相应纳米片的能量稳定性,f) 允许负手性指数。介绍了 NanoTube Construct 在构建能量最小化的石墨烷和二硫化钼纳米管中的应用,展示了该工具的能力。NanoTube Construct 可通过 Enalos 云平台 (https://enaloscloud.novamechanics.com/diagonal/nanotube/) 免费访问。
{"title":"NanoTube Construct: A web tool for the digital construction of nanotubes of single-layer materials and the calculation of their atomistic descriptors powered by Enalos Cloud Platform","authors":"","doi":"10.1016/j.csbj.2024.09.023","DOIUrl":"10.1016/j.csbj.2024.09.023","url":null,"abstract":"<div><div>NanoTube Construct is a web tool for the digital construction of nanotubes based on real and hypothetical single-layer materials including carbon-based materials such as graphene, graphane, graphyne polymorphs, graphidiyene and non-carbon materials such as silicene, germanene, boron nitride, hexagonal bilayer silica, haeckelite silica, molybdene disulfide and tungsten disulfide. Contrary to other available tools, NanoTube Construct has the following features: a) it is not limited to zero thickness materials with specific symmetry, b) it applies energy minimisation to the geometrically constructed Nanotubes to generate realistic ones, c) it derives atomistic descriptors (e.g., the average potential energy per atom, the average coordination number, etc.), d) it provides the primitive unit cell of the constructed Nanotube which corresponds to the selected rolling vector (i.e., the direction in which the starting nanosheet is rolled to form a tube), e) it calculates whether the Nanotube or its corresponding nanosheet is more energetically stable and f) it allows negative chirality indexes. Application of NanoTube Construct for the construction of energy minimised graphane and molybdenum disulfide nanotubes are presented, showcasing the tool's capability. NanoTube Construct is freely accessible through the Enalos Cloud Platform (<span><span>https://enaloscloud.novamechanics.com/diagonal/nanotube/</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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Computational and structural biotechnology journal
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