Pub Date : 2024-10-03DOI: 10.1038/s43586-024-00347-x
Benjamin Victor Ineichen, Ulrike Held, Georgia Salanti, Malcolm Robert Macleod, Kimberley Elaine Wever
The preclinical research community faces an ever-expanding corpus of biomedical literature, making it challenging to keep abreast with the latest findings. This hampers evidence-based research and informed decision-making. Thus, reliable tools are warranted to manage this evidence and maximize the global investment in research. Systematic reviews, syntheses of existing scientific evidence that address a focused question in an unbiased manner and using explicit methods, have gained momentum as an effective solution. Systematic reviews have an important role in uncovering problems in preclinical research, informing best practice guidelines, reducing research waste, promoting reproducibility and guiding translational research. Systematic reviews of preclinical studies also promote ethical animal use by maximizing the use of existing animal studies, thereby fostering animal welfare. However, poorly performed systematic reviews can produce unreliable results, leading to incorrect conclusions about the underlying literature. This Primer presents guidance for conducting a rigorous systematic review with or without meta-analysis of preclinical studies including animal and in vitro studies. It also discusses the limitations of systematic reviews and outlines current developments such as systematic review automation. By following this Primer, researchers can ensure the rigour and usefulness of their systematic reviews, ultimately benefiting decision-making and research outcomes in preclinical research. Preclinical systematic reviews look at scientific evidence addressing focused questions from animal research studies to inform future clinical research. In this Primer, Ineichen et al. discuss the best practices for conducting preclinical systematic reviews, promoting reproducibility and guiding translational research.
{"title":"Systematic review and meta-analysis of preclinical studies","authors":"Benjamin Victor Ineichen, Ulrike Held, Georgia Salanti, Malcolm Robert Macleod, Kimberley Elaine Wever","doi":"10.1038/s43586-024-00347-x","DOIUrl":"10.1038/s43586-024-00347-x","url":null,"abstract":"The preclinical research community faces an ever-expanding corpus of biomedical literature, making it challenging to keep abreast with the latest findings. This hampers evidence-based research and informed decision-making. Thus, reliable tools are warranted to manage this evidence and maximize the global investment in research. Systematic reviews, syntheses of existing scientific evidence that address a focused question in an unbiased manner and using explicit methods, have gained momentum as an effective solution. Systematic reviews have an important role in uncovering problems in preclinical research, informing best practice guidelines, reducing research waste, promoting reproducibility and guiding translational research. Systematic reviews of preclinical studies also promote ethical animal use by maximizing the use of existing animal studies, thereby fostering animal welfare. However, poorly performed systematic reviews can produce unreliable results, leading to incorrect conclusions about the underlying literature. This Primer presents guidance for conducting a rigorous systematic review with or without meta-analysis of preclinical studies including animal and in vitro studies. It also discusses the limitations of systematic reviews and outlines current developments such as systematic review automation. By following this Primer, researchers can ensure the rigour and usefulness of their systematic reviews, ultimately benefiting decision-making and research outcomes in preclinical research. Preclinical systematic reviews look at scientific evidence addressing focused questions from animal research studies to inform future clinical research. In this Primer, Ineichen et al. discuss the best practices for conducting preclinical systematic reviews, promoting reproducibility and guiding translational research.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-18"},"PeriodicalIF":50.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1038/s43586-024-00357-9
This PrimeView highlights how linear discriminant analysis projects data into lower-dimensional spaces.
本 PrimeView 着重介绍了线性判别分析如何将数据投射到低维空间。
{"title":"Linear discriminant analysis","authors":"","doi":"10.1038/s43586-024-00357-9","DOIUrl":"10.1038/s43586-024-00357-9","url":null,"abstract":"This PrimeView highlights how linear discriminant analysis projects data into lower-dimensional spaces.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-1"},"PeriodicalIF":50.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43586-024-00357-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1038/s43586-024-00346-y
Shuping Zhao, Bob Zhang, Jian Yang, Jianhang Zhou, Yong Xu
Linear discriminant analysis (LDA) is a versatile statistical method for reducing redundant and noisy information from an original sample to its essential features. Particularly, LDA is a supervised learning technique, in which the labelled data are necessary for its training process and have been widely used for data dimensionality reduction. Original data are transformed into a low-dimensional subspace by maximizing the trace of the between-class scatter matrix while minimizing the trace of the within-class scatter matrix, thereby enhancing the expressiveness of features. This Primer offers a thorough overview of LDA, including its definition and the interpretation of its numerical and graphical results. It details LDA variants, their implementation settings, experimental outcomes and widely used open-source databases. This Primer also explores applications of LDA-based methods, implementation details across various areas and connections with related methodologies. Reproducibility, limitation and optimization of LDA-based methods are discussed followed by future goals of LDA and its variants. Linear discriminant analysis (LDA) is a versatile statistical method for reducing redundant and noisy information from an original sample to its essential features. In this Primer, Zhao et al. discuss LDA variants and their implementation settings as well as best practices for applying LDA to analyse data.
{"title":"Linear discriminant analysis","authors":"Shuping Zhao, Bob Zhang, Jian Yang, Jianhang Zhou, Yong Xu","doi":"10.1038/s43586-024-00346-y","DOIUrl":"10.1038/s43586-024-00346-y","url":null,"abstract":"Linear discriminant analysis (LDA) is a versatile statistical method for reducing redundant and noisy information from an original sample to its essential features. Particularly, LDA is a supervised learning technique, in which the labelled data are necessary for its training process and have been widely used for data dimensionality reduction. Original data are transformed into a low-dimensional subspace by maximizing the trace of the between-class scatter matrix while minimizing the trace of the within-class scatter matrix, thereby enhancing the expressiveness of features. This Primer offers a thorough overview of LDA, including its definition and the interpretation of its numerical and graphical results. It details LDA variants, their implementation settings, experimental outcomes and widely used open-source databases. This Primer also explores applications of LDA-based methods, implementation details across various areas and connections with related methodologies. Reproducibility, limitation and optimization of LDA-based methods are discussed followed by future goals of LDA and its variants. Linear discriminant analysis (LDA) is a versatile statistical method for reducing redundant and noisy information from an original sample to its essential features. In this Primer, Zhao et al. discuss LDA variants and their implementation settings as well as best practices for applying LDA to analyse data.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-16"},"PeriodicalIF":50.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1038/s43586-024-00344-0
Theo Emmerich, Nathan Ronceray, Kumar Varoon Agrawal, Slaven Garaj, Manish Kumar, Aleksandr Noy, Aleksandra Radenovic
Fluid transport at the nanoscale is ubiquitous in nature. However, rigorous study of fluid flow and structure in artificial nanopores only emerged relatively recently. Termed nanofluidics, the field is driven by the rise of nanomaterials and nanofabrication techniques and supported by theoretical progress beyond continuum fluid dynamics. Nanofluidics has a wide range of applications, such as nanopore sensing and membrane technologies for sieving and energy harvesting, leading to growth of the field. In this Primer, an overview of nanofluidic methods is provided, from the fabrication of the first nanopores to advanced functionalities, such as brain-inspired ionic computing. Focus is given to experimental approaches, including device fabrication and scale-up strategies, in addition to a discussion of limitations, margin for improvements and future directions. Nanofluidics studies fluids in artificial nanopores, in which confinement and interfaces result in unique phenomena. This Primer looks at how to prepare nanostructures and probe fluid transport at the nanoscale, including scale-up strategies.
{"title":"Nanofluidics","authors":"Theo Emmerich, Nathan Ronceray, Kumar Varoon Agrawal, Slaven Garaj, Manish Kumar, Aleksandr Noy, Aleksandra Radenovic","doi":"10.1038/s43586-024-00344-0","DOIUrl":"10.1038/s43586-024-00344-0","url":null,"abstract":"Fluid transport at the nanoscale is ubiquitous in nature. However, rigorous study of fluid flow and structure in artificial nanopores only emerged relatively recently. Termed nanofluidics, the field is driven by the rise of nanomaterials and nanofabrication techniques and supported by theoretical progress beyond continuum fluid dynamics. Nanofluidics has a wide range of applications, such as nanopore sensing and membrane technologies for sieving and energy harvesting, leading to growth of the field. In this Primer, an overview of nanofluidic methods is provided, from the fabrication of the first nanopores to advanced functionalities, such as brain-inspired ionic computing. Focus is given to experimental approaches, including device fabrication and scale-up strategies, in addition to a discussion of limitations, margin for improvements and future directions. Nanofluidics studies fluids in artificial nanopores, in which confinement and interfaces result in unique phenomena. This Primer looks at how to prepare nanostructures and probe fluid transport at the nanoscale, including scale-up strategies.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-18"},"PeriodicalIF":50.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1038/s43586-024-00353-z
This PrimeView highlights the reconstruction of particle trajectories from raw microscopy or other imaging data to obtain insights into the dynamics of cellular processes.
{"title":"A guide to single-particle tracking","authors":"","doi":"10.1038/s43586-024-00353-z","DOIUrl":"10.1038/s43586-024-00353-z","url":null,"abstract":"This PrimeView highlights the reconstruction of particle trajectories from raw microscopy or other imaging data to obtain insights into the dynamics of cellular processes.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-1"},"PeriodicalIF":50.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43586-024-00353-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1038/s43586-024-00349-9
Marwa M. I. Rizk
Thriving in academia with a disability sparks creativity and innovation. My experience in chemistry and pharmacy laboratories highlights the need for universities to establish inclusive environments with clear, effective accommodations. I advocate for inclusivity, urging academia to support all researchers to excel and making science accessible to everyone.
{"title":"A personal journey of adapting laboratory techniques for disability inclusion in research","authors":"Marwa M. I. Rizk","doi":"10.1038/s43586-024-00349-9","DOIUrl":"10.1038/s43586-024-00349-9","url":null,"abstract":"Thriving in academia with a disability sparks creativity and innovation. My experience in chemistry and pharmacy laboratories highlights the need for universities to establish inclusive environments with clear, effective accommodations. I advocate for inclusivity, urging academia to support all researchers to excel and making science accessible to everyone.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-2"},"PeriodicalIF":50.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1038/s43586-024-00341-3
François Simon, Lucien E. Weiss, Sven van Teeffelen
Individual proteins and protein complexes undergo various motion types, including free diffusion, confinement, subdiffusion and directed motion. Different motion behaviours reflect different microenvironments, activity states, kinetics and interaction partners. Single-particle tracking (SPT) is a powerful method for analysing these behaviours directly and in live cells. However, SPT is confounded by multiple sources of experimental noise and biases. Interpreting tracks in terms of quantitative models thus remains a challenging task. We start this Primer by briefly presenting experimental setups and labelling techniques often employed for SPT, followed by a focus on the variety of tools available for analysing noisy tracks with multiple states. This includes tools designed to identify and characterize state fractions and diffusion coefficients, detect and quantify state transitions, predict the number of states and identify and parameterize various motion behaviours. We then highlight some of the many applications of SPT in cellular biology and discuss the limitations of current methods and what future developments are needed to address the current challenges of the SPT analysis. Single-particle tracking is a method for analysing different motion behaviours of individual proteins and protein complexes in live cells. This Primer outlines the experimental setup and labelling techniques commonly used for single-particle tracking, as well as its many applications in cellular biology.
{"title":"A guide to single-particle tracking","authors":"François Simon, Lucien E. Weiss, Sven van Teeffelen","doi":"10.1038/s43586-024-00341-3","DOIUrl":"10.1038/s43586-024-00341-3","url":null,"abstract":"Individual proteins and protein complexes undergo various motion types, including free diffusion, confinement, subdiffusion and directed motion. Different motion behaviours reflect different microenvironments, activity states, kinetics and interaction partners. Single-particle tracking (SPT) is a powerful method for analysing these behaviours directly and in live cells. However, SPT is confounded by multiple sources of experimental noise and biases. Interpreting tracks in terms of quantitative models thus remains a challenging task. We start this Primer by briefly presenting experimental setups and labelling techniques often employed for SPT, followed by a focus on the variety of tools available for analysing noisy tracks with multiple states. This includes tools designed to identify and characterize state fractions and diffusion coefficients, detect and quantify state transitions, predict the number of states and identify and parameterize various motion behaviours. We then highlight some of the many applications of SPT in cellular biology and discuss the limitations of current methods and what future developments are needed to address the current challenges of the SPT analysis. Single-particle tracking is a method for analysing different motion behaviours of individual proteins and protein complexes in live cells. This Primer outlines the experimental setup and labelling techniques commonly used for single-particle tracking, as well as its many applications in cellular biology.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-19"},"PeriodicalIF":50.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1038/s43586-024-00340-4
Diethelm Johannsmann, Ilya Reviakine
Quartz crystal microbalance with dissipation monitoring (QCM-D) probes interfaces by subjecting them to a periodic shear stress exerted by an acoustic resonator. The changes in the resonance frequency, $$Delta f$$ , and the half-width at half-maximum of the resonance, $$Delta Gamma $$ (closely related to the changes in the dissipation, $$Delta D$$ ), measured with the QCM-D are proportional to the in-phase and out-of-phase components of the area-averaged transverse stress at the resonator surface, respectively. Amounts, organization and properties of soft matter at an interface between the resonator and a liquid or a gas are derived from the measurements of $$Delta f$$ and $$Delta Gamma $$ on multiple overtones at megahertz frequencies. The properties include viscoelasticity and stress relaxation dynamics on the timescale of the oscillation period. This Primer offers guidelines on instrument design, experimental procedures and data analysis for interpreting frequency and bandwidth changes in terms of structure and dynamics of the sample. There is a focus on recent progress in the analysis of the acoustic ratio, $$Delta Gamma /(-,Delta f)$$ , and numerical methods of modelling. Limitations of the existing approaches for data analysis are discussed. Challenges and possible future developments are formulated in an outlook. Quartz crystal microbalance with dissipation monitoring probes interfaces by subjecting them to a periodic shear stress exerted by an acoustic resonator. In this Primer, Johannsmann and Reviakine offer guidelines on experimental aspects of quartz crystal microbalance with dissipation monitoring, including measurement cell design, resonator surface treatment, data collection and quantitative data analysis.
{"title":"Quartz crystal microbalance with dissipation monitoring for studying soft matter at interfaces","authors":"Diethelm Johannsmann, Ilya Reviakine","doi":"10.1038/s43586-024-00340-4","DOIUrl":"10.1038/s43586-024-00340-4","url":null,"abstract":"Quartz crystal microbalance with dissipation monitoring (QCM-D) probes interfaces by subjecting them to a periodic shear stress exerted by an acoustic resonator. The changes in the resonance frequency, $$Delta f$$ , and the half-width at half-maximum of the resonance, $$Delta Gamma $$ (closely related to the changes in the dissipation, $$Delta D$$ ), measured with the QCM-D are proportional to the in-phase and out-of-phase components of the area-averaged transverse stress at the resonator surface, respectively. Amounts, organization and properties of soft matter at an interface between the resonator and a liquid or a gas are derived from the measurements of $$Delta f$$ and $$Delta Gamma $$ on multiple overtones at megahertz frequencies. The properties include viscoelasticity and stress relaxation dynamics on the timescale of the oscillation period. This Primer offers guidelines on instrument design, experimental procedures and data analysis for interpreting frequency and bandwidth changes in terms of structure and dynamics of the sample. There is a focus on recent progress in the analysis of the acoustic ratio, $$Delta Gamma /(-,Delta f)$$ , and numerical methods of modelling. Limitations of the existing approaches for data analysis are discussed. Challenges and possible future developments are formulated in an outlook. Quartz crystal microbalance with dissipation monitoring probes interfaces by subjecting them to a periodic shear stress exerted by an acoustic resonator. In this Primer, Johannsmann and Reviakine offer guidelines on experimental aspects of quartz crystal microbalance with dissipation monitoring, including measurement cell design, resonator surface treatment, data collection and quantitative data analysis.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-21"},"PeriodicalIF":50.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1038/s43586-024-00351-1
This PrimeView highlights how soft matter can be studied using quartz crystal microbalance with dissipation monitoring and the role the quartz crystal resonator plays.
{"title":"Quartz crystal microbalance with dissipation monitoring for studying soft matter at interfaces","authors":"","doi":"10.1038/s43586-024-00351-1","DOIUrl":"10.1038/s43586-024-00351-1","url":null,"abstract":"This PrimeView highlights how soft matter can be studied using quartz crystal microbalance with dissipation monitoring and the role the quartz crystal resonator plays.","PeriodicalId":74250,"journal":{"name":"Nature reviews. Methods primers","volume":" ","pages":"1-1"},"PeriodicalIF":50.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43586-024-00351-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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