Microscopy Research and Technique最新文献_第5页

RETRACTION: UV-reflecting wing scales in the silk moth Antheraea assamensis: Its biophysical implications 缩回：柞蚕反射紫外线的翅鳞及其生物物理意义。

IF 2.1 3区工程技术

Microscopy Research and Technique Pub Date : 2026-07-02 Epub Date: 2026-03-26 DOI: 10.1002/jemt.70139

{"title":"RETRACTION: UV-reflecting wing scales in the silk moth Antheraea assamensis: Its biophysical implications","authors":"","doi":"10.1002/jemt.70139","DOIUrl":"10.1002/jemt.70139","url":null,"abstract":"\u0000 RETRACTION: S. Dey, S. Singh, S. Dey, S. Chaudhury, R. Chakraborty, R. N. K. Hooroo, and D. K. Sharma, “ UV-Reflecting Wing Scales in the Silk Moth Antheraea assamensis: Its Biophysical Implications,” Microscopy Research and Technique 74, no. 1 (2011): 28–35, https://doi.org/10.1002/jemt.20869.\u0000 The above article, published online on 22 December 2010 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editors-in-Chief, Julia Fernandez-Rodriguez and Paula Sampaio; and Wiley Periodicals LLC. The retraction has been agreed due to concerns raised by third parties. Specifically, it has been brought to the attention of the journal that although the title and textual description clearly state that the study pertains to Antheraea assamensis, the research appears to have been conducted on a different species, based on the morphological features discernible in the figures presented. The authors were unresponsive to our requests for clarification. As the misidentification leads to misleading interpretation of the findings, the article has been retracted. The authors have been informed of this decision but were not available for final confirmation.","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 8","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jemt.70139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147521380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fluctuation-Based Super-Resolution Microscopy Classification via Gradient Boosting Decision Trees 基于梯度增强决策树的波动超分辨率显微镜分类。

IF 2.1 3区工程技术

Microscopy Research and Technique Pub Date : 2026-07-02 Epub Date: 2026-03-20 DOI: 10.1002/jemt.70140

Zhiping Zeng, Xinyi Chen, Biqing Xu, Jin Qiu, Xiying Ren, Zhongwei Xu, Yantang Huang, Canhua Xu

{"title":"Fluctuation-Based Super-Resolution Microscopy Classification via Gradient Boosting Decision Trees","authors":"Zhiping Zeng, Xinyi Chen, Biqing Xu, Jin Qiu, Xiying Ren, Zhongwei Xu, Yantang Huang, Canhua Xu","doi":"10.1002/jemt.70140","DOIUrl":"10.1002/jemt.70140","url":null,"abstract":"<div>\u0000 \u0000 Fluorescence fluctuation-based super-resolution microscopy has broad applications in observing subcellular structures and monitoring their kinetic processes. Therefore, it is highly demanded to systematically study the reconstruction quality of multiple fluctuation-based super-resolution algorithms under different fluorescence temporal fluctuations. In this study, the performances in the image quality of multiple super-resolution techniques under different conditions are quantitatively analyzed and compared by employing comprehensive evaluation metrics. Furthermore, a gradient boosting decision tree (GBDT) model was constructed to accurately predict the most suitable super-resolution algorithm based on input features encompassing frame numbers, on–off brightness, on-state probability, and signal-to-noise ratios. The results show that high-quality super-resolution images can be obtained by increasing image frames together with enhancing the fluorescence fluctuation signals, and the GBDT model demonstrates robust predictive capability, achieving high classification accuracy after iterative training. This study could facilitate rapid selection and implementation of fluctuation-based super-resolution techniques for subcellular organelle research under diverse fluorescent labeling conditions.\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 8","pages":"1137-1153"},"PeriodicalIF":2.1,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147490904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Broadband Microwave Near-Field Imaging by Localized Field Perturbations 局域场扰动宽带微波近场成像。

IF 2.1 3区工程技术

Microscopy Research and Technique Pub Date : 2026-07-02 Epub Date: 2026-03-20 DOI: 10.1002/jemt.70141

Qi Wang, Qiang Zhu, Huan Fei Wen, Tao Pei, Jun Xing, Xin Li, Zhonghao Li, Hao Guo, Zongmin Ma, Jun Tang, Jun Liu

{"title":"Broadband Microwave Near-Field Imaging by Localized Field Perturbations","authors":"Qi Wang, Qiang Zhu, Huan Fei Wen, Tao Pei, Jun Xing, Xin Li, Zhonghao Li, Hao Guo, Zongmin Ma, Jun Tang, Jun Liu","doi":"10.1002/jemt.70141","DOIUrl":"10.1002/jemt.70141","url":null,"abstract":"<div>\u0000 \u0000 Inverted scanning microwave microscopy (iSMM), as a novel near-field characterization tool, demonstrates unique advantages in research fields such as biomedicine and the dielectric properties of two-dimensional materials. This paper proposes a dual-port inverted scanning imaging method based on the perturbation of the spatial microwave field in microstrip lines by an uncoated silicon tip. Combining microwave vector network analysis techniques, a non-invasive silicon tip scans the surface of samples above the microstrip line to obtain local S11 and S21 scattering parameters. Theoretical modeling and experiments validate the broadband resonant characteristics of the microstrip structure across the 1–10 GHz frequency range. Near-field images of dried mouse fibroblasts were successfully acquired at selected frequencies (3, 5, and 8 GHz), revealing their distinct electromagnetic responses at different frequencies. Comparison of the signal-to-noise ratios (SNR) for S11 and S21 parameters indicates that the S21 SNR exhibits more stable fluctuations (30–60 dB) across the 1–10 GHz range. This testing method possesses broadband microwave imaging capabilities, providing a reliable technical foundation for structural characterization studies in fields such as microelectronic devices, biological tissues, and functional materials.\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 8","pages":"1130-1136"},"PeriodicalIF":2.1,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147486760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Externally Controlled In Situ SEM: Multi-Rate Scanning With Signal Regulation and Spatiotemporal Fusion. 外部控制的原位扫描电镜：多速率扫描与信号调节和时空融合。

IF 2.1 3区工程技术

Microscopy Research and Technique Pub Date : 2026-06-11 DOI: 10.1002/jemt.70152

Jieping Ding, Ling'en Liu, Ni Wang, Yixu Zhang, Yuefei Zhang, Junxia Lu, Ze Zhang

{"title":"Externally Controlled In Situ SEM: Multi-Rate Scanning With Signal Regulation and Spatiotemporal Fusion.","authors":"Jieping Ding, Ling'en Liu, Ni Wang, Yixu Zhang, Yuefei Zhang, Junxia Lu, Ze Zhang","doi":"10.1002/jemt.70152","DOIUrl":"https://doi.org/10.1002/jemt.70152","url":null,"abstract":"In situ scanning electron microscopy is crucial for investigating materials' properties. High-temperature in situ experiments are important for revealing the phase transitions and thermal deformations of materials. However, thermal electrons and noise in the high-temperature environment can cause image distortion. Reducing the influence of thermal electrons and noise is a key means to improve the quality of high-temperature imaging. Rapid scanning emerges as a key strategy to address this challenge, as it minimizes the duration of thermal electron interference on images. This study presents an externally controlled in situ image acquisition system that mitigates thermal electron accumulation through multi-rate scanning, incorporates a dual-stage signal regulator to compensate for signal attenuation during high-speed scanning, and integrates an image fusion algorithm to effectively balance noise suppression and detail preservation. Together, these components constitute a coordinated hardware-algorithm solution for high-temperature imaging. The results show that the acquisition system can obtain high-quality images at 1050°C, with a total image acquisition time of only 1.5 s, which is significantly faster than the 3.5 s of traditional systems. Compared to the conventional acquisition speed of 3.2 μs/pixel, the synthesized images exhibit an increase in SSIM and a decrease in NIQE within the temperature range of 900°C~1050°C. This method provides reliable technical support for the dynamic microstructure characterization of high-temperature materials and promotes the application of in situ SEM in extreme environments.","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148226628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Based Framework for Detection and Classification of Leukemia Using Microscopic Images. 基于深度学习的白血病显微图像检测与分类框架。

IF 2.1 3区工程技术

Microscopy Research and Technique Pub Date : 2026-06-11 DOI: 10.1002/jemt.70156

Rida Arif, Shahzad Akbar, Usama Shahzore, Faten S Alamri, Salma Idris, Amjad R Khan

{"title":"Deep Learning Based Framework for Detection and Classification of Leukemia Using Microscopic Images.","authors":"Rida Arif, Shahzad Akbar, Usama Shahzore, Faten S Alamri, Salma Idris, Amjad R Khan","doi":"10.1002/jemt.70156","DOIUrl":"https://doi.org/10.1002/jemt.70156","url":null,"abstract":"Excessive proliferation of white blood cells (WBCs) in the bone marrow leads to a type of blood cancer known as leukemia. This blood cancer impairs the immune response, and timely detection and diagnosis are crucial for human health. Several manual and automated methods for leukemia diagnosis have emerged recently, with the latter still requiring medical practitioners' attention for leukemia treatment. Microscopy is an essential technique in the diagnosis of leukemia, as it allows examination of blood cells in detail and accurate identification of cancerous cells. But artificial intelligence (AI), especially deep learning, has recently been explored to enhance leukemia detection and classification. This study presents an approach to detect leukemia from microscopic images using a convolutional neural network (CNN). The approach starts with image pre-processing, then enhances the training dataset through data augmentation strategies, increasing the number of image samples from 270 to 1268. The U-Net model is used to segment leukemia and normal cells, allowing for efficient feature extraction from high resolution microscopic images. Then, the ASH dataset is classified using a CNN-based architecture, differentiating between the different subtypes of leukemia. Through 10-fold cross-validation, the proposed model achieves an accuracy rate of 99.06% for binary classification and 98.68% for multi-class classification, with a recall of 96.74%, a precision of 96.83%, and an F1-score of 96.77% ± 1.09%. These results suggest that the proposed model performs similarly to other methods. The framework of microscopic imaging and deep learning in our model shows promise as computer-aided diagnosis of leukemia. But more work is needed to train it on a larger and more diverse dataset. This process can be extended to detect other blood disorders through the inclusion of other deep learning models or potentially investigate robust data augmentation techniques in the future.","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148226589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight Truncated Fused-MirrorNet for Classification and Analysis of Histopathology Images 用于组织病理学图像分类和分析的轻量级截断融合镜网。

IF 2.1 3区工程技术

Microscopy Research and Technique Pub Date : 2026-06-01 Epub Date: 2026-02-04 DOI: 10.1002/jemt.70127

Amit Kumar Chanchal, Shyam Lal

{"title":"Lightweight Truncated Fused-MirrorNet for Classification and Analysis of Histopathology Images","authors":"Amit Kumar Chanchal, Shyam Lal","doi":"10.1002/jemt.70127","DOIUrl":"10.1002/jemt.70127","url":null,"abstract":"<div>\u0000 \u0000 Renal cell carcinoma is the primary cause of cancer-related mortality, highlighting the importance of early detection and accurate diagnosis. Manual histopathology image classification methods include limitations such as labor effort, time consumption, and interpathologist variations, which can lead to misdiagnosis, especially in the early stages. An autonomous solution based on deep learning is essential to overcome these constraints. However, vision-based models require significant processing resources and data sets, which pose difficulties for low-end infrastructures. In this study, we have described an approach for analyzing histopathology images using a lightweight truncated Fused-MirrorNet model. With its mirrored architecture, we use partial layer freezing and feature fusion approaches to improve performance. In kidney histopathology image analysis, our suggested strategy outperforms existing CNN and vision transformer models in histopathology image classification. We significantly reduced training time while preserving classification accuracy. The proposed model is deployable, scalable, and reproducible, allowing it to be used on low-end devices. Our strategy also makes it easier to create vision-based deep learning models by removing the requirement for sophisticated computational methodologies and procedures. The proposed model and the comparison models were trained and evaluated using histopathology images from two datasets. The experimental results reveal that the proposed model (Fused-MirrorNet) surpassed the performance of state-of-the-art models used for the classification of histopathology images. The proposed model achieves an accuracy of 92.60% and 90.00% in the TCGA kidney and BreakHis datasets, respectively. These findings indicate that the research conducted to develop the suggested model produced favorable outcomes.\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 7","pages":"974-993"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146119608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of Rare Earth and Alkali Promoters on Distribution Profile and Properties of Spherical Shaped γ-Al2O3 Supported Ni Catalysts 稀土和碱促进剂对球形γ-Al2O3负载Ni催化剂分布、分布和性能的影响

IF 2.1 3区工程技术

Microscopy Research and Technique Pub Date : 2026-06-01 Epub Date: 2026-02-22 DOI: 10.1002/jemt.70134

Emine Ersezer, Servet Turan, Umut Savaci

{"title":"Effects of Rare Earth and Alkali Promoters on Distribution Profile and Properties of Spherical Shaped γ-Al2O3 Supported Ni Catalysts","authors":"Emine Ersezer, Servet Turan, Umut Savaci","doi":"10.1002/jemt.70134","DOIUrl":"10.1002/jemt.70134","url":null,"abstract":"<div>\u0000 \u0000 This study investigates the effects of rare-earth (La and Ce) and alkali (Mg and K) promoters on the distribution profile and properties of spherical γ-Al2O3 supported Ni catalysts. The catalysts were synthesized via sequential wet impregnation, targeting 10 wt.% Ni, with individual and co-promotion of promoters. The distribution profiles of Ni within the spherical support revealed that promoter addition caused a NiO concentration gradient along the spherical catalysts. Characterization techniques such as X-ray diffraction (XRD), BET (Brunauer–Emmet–Teller), scanning electron microscope & energy dispersive X-ray spectroscopy (SEM–EDX), scanning/transmission electron microscope (S/TEM), and temperature programmed reduction (TPR) demonstrated that promoter addition significantly influenced the distribution profile, particle size, and reducibility of NiO. According to the obtained results, catalysts co-promoted with La and Mg achieved a homogeneous distribution profile and the lowest reduction temperature, indicating enhanced Ni reducibility and potentially improved catalytic performance. These findings highlight the critical role of promoters as well as co-promotion in modifying the active metal distribution and properties of Ni/γ-Al2O3 catalysts.\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 7","pages":"1028-1038"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147271393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Nanoparticles Counting for TEM Images by Combination of Distance Transform, Watershed Segmentation and U-Net Machine Learning 结合距离变换、分水岭分割和U-Net机器学习的TEM图像纳米粒子自动计数。

IF 2.1 3区工程技术

Microscopy Research and Technique Pub Date : 2026-06-01 Epub Date: 2026-02-23 DOI: 10.1002/jemt.70131

W. A. A. L. Wanniarachchi, W. A. M. Madhavi

{"title":"Automatic Nanoparticles Counting for TEM Images by Combination of Distance Transform, Watershed Segmentation and U-Net Machine Learning","authors":"W. A. A. L. Wanniarachchi, W. A. M. Madhavi","doi":"10.1002/jemt.70131","DOIUrl":"10.1002/jemt.70131","url":null,"abstract":"<div>\u0000 \u0000 Accurate counting of nanoparticles in microscopy images such as SEM and TEM is critical for advancing materials science and nanotechnology. Though many conventional as well as novel methods exist to count particles in microscopy images, the presence of overlapping particles poses significant challenges. This study introduces a novel computational approach to count nanoparticles in microscopy images, addressing limitations in handling particle overlap. The proposed method integrates distance transform, watershed segmentation, and U-Net machine learning. Distance transform enhances finding center points of the particles; watershed segmentation effectively separates overlapping particles; and U-Net enables robust particle segmentation from complex image backgrounds. The methodology was first developed using computer-generated grayscale images with varying particle sizes and overlap percentages, then validated on a dataset of 17 TEM images (1022 × 668 pixels) of Fe3O4 and silica-coated Fe3O4 nanoparticles at 20 nm and 50 nm scales. Conventional convolution-based methods could accurately count non-overlapping particles but failed to give the accurate count when particles were overlapped. The accuracy of convolution-based methods also depended on kernel radius selection. Replacing convolution method with the distance transform and watershed method significantly improved the accuracy of particle counting in images with overlapping particles. The U-Net model, combined with a smooth blending algorithm, achieved a mean percentage error of 6.5% in particle counting on real TEM images. This approach demonstrates significant promise for applications in materials science, nanotechnology, and biology, where accurate particle quantification is essential. By addressing the limitations of conventional techniques, it offers a practical and efficient solution for automated nanoparticle analysis in microscopy images.\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 7","pages":"1039-1048"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147276480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spectral Phasor Analysis for Hyperspectral Imaging to Elucidate Acridine Orange Photophysics in Cells 高光谱成像的光谱相量分析阐明吖啶橙在细胞中的光物理。

IF 2.1 3区工程技术

Microscopy Research and Technique Pub Date : 2026-06-01 Epub Date: 2026-02-12 DOI: 10.1002/jemt.70130

Marcela Díaz, Flavio R. Zolessi, Leonel Malacrida

{"title":"Spectral Phasor Analysis for Hyperspectral Imaging to Elucidate Acridine Orange Photophysics in Cells","authors":"Marcela Díaz, Flavio R. Zolessi, Leonel Malacrida","doi":"10.1002/jemt.70130","DOIUrl":"10.1002/jemt.70130","url":null,"abstract":"<div>\u0000 \u0000 Acridine orange (AO) exhibits complex photophysical properties that have been utilized to study its interactions with RNA and DNA. Traditionally, AO has been used to stain cells, distinguishing the nucleus and cytoplasm based on differences in DNA and RNA composition. Its labeling behavior varies depending on factors such as concentration and pH. AO fluorescence is rich and complex; therefore, acquiring the full spectrum or the fluorescence lifetime is beneficial for understanding its intricate photophysics. Nevertheless, handling 4D datasets can be challenging and often relies on sophisticated or highly parameterized models for their analysis. Model-free approaches, such as spectral phasor (SP) analysis for hyperspectral imaging (HSI), are well suited to navigating these analytical challenges. Using SP's principles of linear combination and reciprocity, we identified spectral fingerprints of RNA and DNA in in vitro experiments that remain consistent in both live and fixed cells. Interestingly, SP analysis of AO fluorescence in live cells revealed a third spectral component at 640 nm. The linear properties of SP analysis enabled the quantification of AO bound to RNA, DNA, and the third component. Co-labeling with AO and LysoTracker suggested that some of these subcellular structures are associated with acidic compartments. Moreover, SP analysis distinguished AO self-interactions from its RNA binding in cytoplasmic compartments. This study establishes SP analysis as a robust tool for hyperspectral imaging of AO, thereby enhancing our understanding of its intricate photophysics and unlocking new opportunities for advanced in vivo cell imaging applications.\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 7","pages":"1002-1015"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146181027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zinc Iodide-Osmium Tetroxide (ZIO) Staining Reveals Differences in the Components of the Endomembrane System of Plant Secretory Cells 碘化锌-四氧化锇（ZIO）染色揭示植物分泌细胞内膜系统成分的差异。

IF 2.1 3区工程技术

Microscopy Research and Technique Pub Date : 2026-06-01 Epub Date: 2026-02-10 DOI: 10.1002/jemt.70129

Silvia Rodrigues Machado, Shelly Favorito Carvalho, Yve Canaveze

{"title":"Zinc Iodide-Osmium Tetroxide (ZIO) Staining Reveals Differences in the Components of the Endomembrane System of Plant Secretory Cells","authors":"Silvia Rodrigues Machado, Shelly Favorito Carvalho, Yve Canaveze","doi":"10.1002/jemt.70129","DOIUrl":"10.1002/jemt.70129","url":null,"abstract":"<div>\u0000 \u0000 The Zinc Iodide-Osmium Tetroxide method (ZIO) is recognized for its effectiveness in impregnating the endomembrane system, particularly Golgi bodies. Nevertheless, its application in plant cell studies remains limited. This research focuses on Palicourea rigida Kunth, a Rubiaceae species known for its colleters producing a mixed secretion primarily composed of lipophilic substances. The aim of our study was to assess the effectiveness of the ZIO method in investigating ultrastructural variations of epithelial cells during the secretory phase. Samples were fixed for 12 h in a solution containing 2.5% glutaraldehyde and 4% paraformaldehyde in 0.1 mol/L phosphate buffer at pH 7.3. This was followed by a 22-h incubation at 4°C in a solution that included zinc (Zn), resublimed iodine (I), TRIS-aminomethane, and osmium tetroxide (OsO4). Subsequently, the samples underwent dehydration through a graded series of acetone and were embedded in Araldite resin. Ultrathin sections were contrasted with uranyl acetate and lead citrate and analyzed using a transmission electron microscope at 60 kV. While the epithelial cells exhibited morphological similarities, the ZIO method revealed diversity in their endomembrane systems. Some cells were rich in rough endoplasmic reticulum (RER), while others were abundant in smooth endoplasmic reticulum (SER), and some primarily contained Golgi bodies. These findings underscore the division of labor among neighboring secretory cells, which, despite their morphological similarities, show differences in their endomembrane systems during the same secretion stage. Therefore, the ZIO method emerges as a promising tool in plant secretion research, contributing to the understanding of the functional diversity of the endomembrane system in plant glands.\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 7","pages":"994-1001"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146149460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0