Journal of microscopy最新文献

{"title":"Optimisation of EDS quantitative f-ratio method via ZAF correction","authors":"Jiayi Chen,&nbsp;Yue Sun,&nbsp;Liujia Tian,&nbsp;Yiling Huang,&nbsp;Xuemei Song,&nbsp;Fan Peng,&nbsp;Wei Zheng,&nbsp;Yi Zeng","doi":"10.1111/jmi.70095","DOIUrl":"10.1111/jmi.70095","url":null,"abstract":"<p>This study investigates the f-ratio quantitative method applicable to Cold Field Emission Scanning Electron Microscope/Energy Dispersive X-Ray Spectroscopy (CFE-SEM/EDS) systems. To address the issue of cumbersome calculations arising from the reliance on real-time simulation in traditional f-ratio methods, this study proposes an efficient strategy of ‘preset curve + ZAF correction’. This method employs pre-constructed ‘f-ratio vs. concentration’ models for individual elements derived from standard samples to substitute the traditional real-time simulation of the unknown sample, and introduces ZAF factors to effectively compensate for matrix effects, thereby achieving quantitative analysis of unknown samples with matrix differences. An iterative approach was implemented to resolve the challenge of determining the absorption parameter (<span></span><math>\u0000 <semantics>\u0000 <mi>χ</mi>\u0000 <annotation>$chi $</annotation>\u0000 </semantics></math>), which arises due to the unknown concentration of the unknown sample. F-ratio models were established based on the P–In, As–Ga, S–Zn, Se–Cd, and S–Fe systems. Quantitative f-ratio analysis was subsequently performed on the binary system As–In and Se–Zn, as well as the ternary system S–As–Fe. The results demonstrate that the ZAF correction f-ratio method achieves satisfactory precision.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"303 1","pages":"79-87"},"PeriodicalIF":1.9,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147698872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TOC - Issue Information","authors":"","doi":"10.1111/jmi.70131","DOIUrl":"https://doi.org/10.1111/jmi.70131","url":null,"abstract":"","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"303 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.70131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148274745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On optimisation of Paganin's method for propagation-based X-ray phase-contrast imaging and tomography","authors":"Timur E. Gureyev,&nbsp;David M. Paganin,&nbsp;Ashkan Pakzad,&nbsp;Harry M. Quiney","doi":"10.1111/jmi.70083","DOIUrl":"10.1111/jmi.70083","url":null,"abstract":"<p>Paganin's method for image reconstruction in propagation-based phase-contrast X-ray imaging and tomography has enjoyed broad acceptance in recent years, with over one thousand publications citing its use. The present paper discusses approaches to optimisation of the method with respect to simple image quality metrics, such as signal-to-noise ratio and spatial resolution, as well as a reference-based metric corresponding to the relative mean squared difference between the reconstructed image and the ‘ground truth’ image that would be obtained in a setup with perfect spatial resolution and no noise. The problem of optimisation of the intrinsic regularisation parameter of Paganin's method with respect to spatial resolution in the reconstructed image is studied in detail. It is also demonstrated that a combination of Paganin's method with a Tikhonov-regularised deconvolution of the point-spread function of the imaging system can provide significantly higher image quality compared to the standard version of the method. Analytical expressions for some relevant image quality metrics are obtained and compared with results of numerical simulations. Advantages and shortcomings of optimisation approaches using a number of different image quality metrics are discussed. The results of this study are expected to be useful in practical X-ray imaging and in training of machine learning models for image denoising and segmentation.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"303 1","pages":"37-52"},"PeriodicalIF":1.9,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.70083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate diagnosis of non-small cell and small cell lung cancer by using machine learning models trained with physical science features extracted from pathological images","authors":"Wencheng Shao,&nbsp;Yun Bao,&nbsp;Ke Jin,&nbsp;Xueqiao Yu,&nbsp;Yixuan Wang,&nbsp;Ning Zhan,&nbsp;Shilong Liu","doi":"10.1111/jmi.70090","DOIUrl":"10.1111/jmi.70090","url":null,"abstract":"<p><b>Background</b>: Accurate differentiation between non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) is crucial for optimising treatment strategies and improving patient outcomes in lung cancer management. Early and precise classification supports tailored therapeutic decisions and enhances prognosis prediction.</p><p><b>Purpose</b>: This study develops a novel method to use machine learning models trained with physical science features extracted from pathological images to classify lung cancer into NSCLC and SCLC with high accuracy and robustness.</p><p><b>Methods</b>: Physical science features were employed to acquire quantitative cellular microarchitecture of cancer cells from histopathological images. Random Forest algorithm was applied to identify the most informative 20 features. Next, the selected top features were used to train and evaluate four machine learning classifiers: Support Vector Machine (SVM), Gradient Boosting, Logistic Regression, and Decision Tree. The dataset included pathological images from 240 histologically confirmed lung cancer cases, divided randomly into training and validation sets (80% train, 20% test). Then, model performance was evaluated using accuracy, recall, F1 score, and area under the receiver operating characteristic curve (AUC), with robustness validation via fivefold cross-validation.</p><p><b>Results</b>: Logistic Regression achieved the highest overall performance, with a median accuracy near 90% and an AUC consistently above 0.90 across fivefold cross-validation. SVM and Gradient Boosting followed closely, each surpassing 0.90 in AUC, demonstrating reliable discrimination between NSCLC and SCLC. Decision Tree showed broader variability, though it maintained acceptable recall for SCLC. Random Forest feature selection revealed refractive index percentiles and polarisation histograms as top contributors to model performance.</p><p><b>Conclusions</b>: Machine learning models trained on physical science features have the potential to serve as a highly accurate and robust framework for differentiating NSCLC from SCLC.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"303 1","pages":"60-78"},"PeriodicalIF":1.9,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147654288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating integrative strategies for incorporating phenotypic features in spatial transcriptomics.","authors":"Levin M Moser, Ahmad Kamal Hamid, Esteban Miglietta, Nodar Gogoberidze, Beth A Cimini","doi":"10.1111/jmi.70129","DOIUrl":"10.1111/jmi.70129","url":null,"abstract":"<p><p>The key advantage of spatial transcriptomics (ST) technologies lies in the spatial domain: these techniques not only offer an unprecedented opportunity to interrogate intact biological samples in a spatially informed manner, but also set the stage for integration with other imaging-based modalities. However, how to most effectively exploit spatial context and integrate ST with imaging-based modalities that capture morphological insight remains an open and heavily investigated question. To address this, particularly under real-world experimental constraints such as limited dataset size, class imbalance, and bounding-box-based segmentation, we used a publicly available murine ileum Multiplexed Error-Robust Fluorescence In Situ Hybridisation (MERFISH) dataset to evaluate whether a minimally tuned variational autoencoder (VAE) could extract informative low-dimensional representations from cell crops of spot counts, nuclear stain, membrane stain, or a combination thereof. We assessed the resulting embeddings through PERMANOVA, cross-validated classification, and unsupervised clustering, and compared them to classical image-based feature vectors extracted via CellProfiler. While transcript counts (TC) generally outperformed other feature spaces, the VAE-derived latent spaces (LSs) captured meaningful biological variation and enabled improved label recovery for specific cell types. LS2, in particular, trained solely on morphological input, also exhibited moderate predictive power for a handful of genes in a ridge regression model. Notably, combining TC with LSs improved clustering outcomes in a strategy-dependent manner, yielding gains in either cluster homogeneity or label recovery under graph- or factor-based clustering approaches, respectively. In contrast, CellProfiler-derived features provided little benefit, highlighting the advantage of learned representations over handcrafted features. Collectively, these findings demonstrate that even under constrained conditions, VAEs can extract biologically meaningful signals from imaging data and constitute a promising strategy for multi-modal integration.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148258425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"20th Anniversary of human-induced pluripotent stem cells and the role of microscopy.","authors":"Philomena Hallford, Colette J Mustard, Nicole Brace, Antonia L Pritchard","doi":"10.1111/jmi.70136","DOIUrl":"https://doi.org/10.1111/jmi.70136","url":null,"abstract":"<p><p>It has been 20 years since the pioneering work of Shinya Yamanaka and Kazutoshi Takahashi at Kyoto University led to the first successful generation of induced pluripotent stem cells (iPSCs) from mouse embryonic and adult fibroblast cells. iPSCs have the capacity to differentiate into any type of cell in the human body, and as such, they have become ubiquitous in medical and biological research. Throughout the development and use of iPSCs and their derivatives, fluorescent microscopy has been particularly integral, playing a central role in the characterisation of cells and analysis of cellular function of both iPSCs and their derived cells. On the 20th anniversary of their discovery, this short review summarises the development of iPSCs, their application in research and clinical settings, and highlights advances in microscopy and imaging methodologies that have been crucial in developing and characterising iPSCs and their derived cell types.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148258387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"International microscopy facility benchmarking survey","authors":"Alfonso J. Schmidt,&nbsp;Ellie Cho,&nbsp;Nicholas D. Condon,&nbsp;Eleanor Kable,&nbsp;Paul J. McMillan","doi":"10.1111/jmi.70079","DOIUrl":"10.1111/jmi.70079","url":null,"abstract":"<p>Microscopy core facilities are critical to modern research, providing access to advanced microscopy applications and expert support. Despite this, global benchmarking data on facility operations, staffing and sustainability remain limited. We conducted the first truly global international survey of microscopy facilities, capturing 196 responses across 34 countries and six continents. The survey includes 74 questions covering facility leaders, facilities, optical microscopy, electron microscopy and image analysis. The results reveal distinct benchmarking data and operational trends on a global and continent basis. This will enable facility leaders, institutions and policymakers to strengthen their microscopy infrastructure, optimise facility staffing and operations, and ensure equitable access to advanced microscopy applications.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"302 3","pages":"419-437"},"PeriodicalIF":1.9,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.70079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147504048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing mature and immature collagen in tonsillar tissue using second harmonic generation microscopy.","authors":"Kay Polland, Megan Clapperton, Thushitha Kunanandam, Catalina D Florea, Catriona M Douglas, Gail McConnell","doi":"10.1111/jmi.70130","DOIUrl":"10.1111/jmi.70130","url":null,"abstract":"<p><p>Recurrent tonsillitis (RT) and obstructive sleep apnoea (OSA) are major indications for tonsillectomy, yet their tissue-level pathology remains unclear. Recurrent inflammation in RT may induce collagen remodelling through excess extracellular matrix deposition. Here, we used label-free second harmonic generation (SHG) microscopy to quantify the area fraction of the ratio of forward-to-backward SHG signals in palatine tonsils from RT and OSA patients. The ratio of forward-to-backward SHG signals showed a higher mean area fraction in OSA when compared to RT, agreeing with trends previously found using immunohistochemical staining methods; however, no statistical significance was found. Spatial analysis of epithelial dominant regions and interior regions of tissue show higher forward/backward SHG signal in OSA rather than RT; however, no statistical significance was found in either measurement. These results demonstrate a new method of using SHG microscopy to measure collagen distribution within palatine tonsils of OSA and RT.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148149975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global survey of microscopy core facilities","authors":"Kirk J. Czymmek,&nbsp;Alexander Scott,&nbsp;Jessica B. Back,&nbsp;Derek Davies,&nbsp;Zhenxi Guo,&nbsp;Masilamani Elangovan,&nbsp;Peter O'Toole","doi":"10.1111/jmi.70097","DOIUrl":"10.1111/jmi.70097","url":null,"abstract":"<p>As part of two open international core facility surveys focused on microscopy and flow cytometry, we obtained an in-depth snapshot of their state-of-play. Here we focus on microscopy cores across ‘the Americas’, ‘Asia Pacific’ and ‘Europe and Africa’ with responses from 241 facilities. We compared instrument types and numbers, staffing, user fees, funding, etc. This revealed both remarkable similarities and notable differences. For example, the staff/instrument ratio in Europe and Africa was smaller compared to other regions World View and Asia Pacific had a larger proportion of electron microscopes. Importantly, we want to emphasise that the anonymised raw data is made available for anyone to do their own further mining and analysis, including cross-correlations and constrained groupings. This survey will serve as an essential baseline for future longitudinal studies following trends across all metrics as our facilities evolve.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"302 3","pages":"419-433"},"PeriodicalIF":1.9,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Running an electron microscopy core facility","authors":"Ilkka Miinalainen,&nbsp;Eija Jokitalo","doi":"10.1111/jmi.70088","DOIUrl":"10.1111/jmi.70088","url":null,"abstract":"<p>As electron microscopes became more costly, technically complex, and integral to a wide range of scientific fields, centralised electron microscopy (EM) core facilities have become essential for maintaining accessibility, performance, and quality. Drawing on the experiences of running two EM core facilities in Finland, we outline key considerations for establishing and operating a biological and biomedical EM facility. We cover facility infrastructure and technical requirements, user training, sample preparation, safety and occupational considerations, personnel and cost management, and finish by outlining three areas, sustainability, research data management, and artificial intelligence, that we consider to be increasingly defining the strategic direction of EM facilities. By distinguishing the specific requirements of EM from those of light microscopy, this article provides practical guidance to support efficient, safe, and high-quality facility operations within institutional and national imaging networks.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"302 3","pages":"346-357"},"PeriodicalIF":1.9,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.70088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147674322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}