Microscopy Research and Technique最新文献

{"title":"Scanning Probe Microscopy of Graphene Sensors Before and After Nitrogen Exposure.","authors":"Dinara Sobola, Pavel Kaspar, Nikola Papež, Mohammed Ahmed Al-Anber, Marwan S Mousa, Robert Macků","doi":"10.1002/jemt.70190","DOIUrl":"https://doi.org/10.1002/jemt.70190","url":null,"abstract":"<p><p>Developing high-performance, miniaturized 2D material platforms requires a deep, multiscale understanding of gas-surface interactions and localized electronic behavior. In this study, we demonstrate an integrated experimental methodology that combines in situ environmental scanning electron microscopy (ESEM) with ex situ scanning probe microscopy (SPM) to analyze monolayer graphene field-effect transistor (GFET) sensors before and after controlled nitrogen (N₂) exposure. In situ electrical measurements performed inside the ESEM chamber show a continuous, monotonic increase in graphene channel resistance (up to approximately 5%, reaching 6.30 kΩ at 160 Pa) as N₂ vapor displaces residual atmospheric contaminants. On the micro and nanoscale, ex situ SPM characterization confirms that the gross topography and structural integrity of the diagonal contact-graphene boundary remain intact post-exposure. The initial highly heterogeneous, \"charge-puddle-like\" work-function landscape-characterized by a low-potential diagonal band-is replaced by a visually smoother, spatially uniform potential profile after N₂ treatment. This shift is primarily attributed to the displacement, desorption, and spatial reorganization of preadsorbed environmental p-dopants (such as water vapor and oxygen molecules) by N₂ gas.</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149182987","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}

{"title":"Low Expression of PINK1 Suppresses Mitophagy in Sonic Hedgehog-Driven Medulloblastoma in Association With Activation of the PI3K/AKT Signaling Pathway.","authors":"Qi Zhang, Hui Yang, Chao Hu, Cuiping Zhang, Longtao He","doi":"10.1002/jemt.70192","DOIUrl":"https://doi.org/10.1002/jemt.70192","url":null,"abstract":"<p><p>Sonic Hedgehog-activated medulloblastomas (SHH-MBs) are aggressive, heterogeneous embryonal neuroepithelial tumors predominantly occurring in the pediatric posterior fossa. Postoperative recurrence and metastasis closely associate with aberrant mitochondrial subcellular localization and dysfunctional mitochondrial quality control. Mitophagy, the selective autophagic degradation of damaged or superfluous mitochondria, is intricately regulated by the PINK1-Parkin signaling pathway in metazoan cells. Nevertheless, the biological significance and the regulatory mechanisms underlying Sonic Hedgehog-mediated medulloblastoma (SHH-MB) proliferation, invasion, metastasis, and recurrence are not yet fully elucidated. In this study, integrated molecular and microscopy-based analyses revealed that PINK1 mRNA and protein levels were significantly downregulated in primary human SHH-MB specimens versus adjacent non-tumor brain tissues, as well as in a panel of SHH-MB cell lines. Transmission electron microscopy (TEM) directly visualized impaired mitophagic flux, fragmented mitochondrial morphology, and accumulated damaged mitochondria in PINK1-deficient SHH-MB cells. In vitro functional assays showed that PINK1 overexpression minimally affected apoptosis but markedly suppressed cell proliferation, migration, and invasion, and potently restored mitophagic activity as validated by mitochondrial autophagy imaging. Combined RNA-sequencing, KEGG pathway enrichment, and Western blot verification further demonstrated that PINK1 depletion was associated with hyperactivation of the PI3K/AKT signaling cascade. Collectively, our microscopy-supported mechanistic data demonstrate that PINK1 downregulation inhibits mitophagy in SHH-MB, an effect associated with sustained PI3K/AKT pathway activation. These findings highlight PINK1 as a promising diagnostic biomarker and potential therapeutic target for SHH-driven medulloblastoma, although the precise mechanistic relationship between PINK1 and PI3K/AKT signaling requires further investigation.</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149182944","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}

{"title":"Advances in Low-Vacuum Scanning Electron Microscopy in Renal Pathological Diagnosis and Research.","authors":"Wanjun Shen, Fangxin Mu, Tiantian Wang","doi":"10.1002/jemt.70187","DOIUrl":"https://doi.org/10.1002/jemt.70187","url":null,"abstract":"<p><p>Owing to its operational simplicity, low cost, and strong adaptability to a wide range of samples, low-vacuum scanning electron microscopy (LVSEM) has gradually become an indispensable tool in renal ultrastructural pathology. This article reviews the diverse applications of LVSEM in renal pathological diagnosis and research, including broad-area assessment of the glomerular basement membrane (GBM) surface architecture, analysis of crystal composition, and localization of immune complexes. The findings indicate that LVSEM holds significant diagnostic value in glomerular diseases, hereditary nephropathies, tubulointerstitial lesions, and kidney transplant pathology. LVSEM not only is an effective complement to traditional transmission electron microscopy (TEM) but also, through its large depth of field and capacity for surface-relief imaging, supports broader-area, quantitative, and multimodal ultrastructural analysis, with broad prospects for clinical application and research.</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149137478","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}

{"title":"Spatial Attributes of Actin Filaments Tethered by Myosin Fragments In Vitro Observed Using a Super-Resolution Microscope","authors":"Daichi Kobayashi,&nbsp;Taiki Tsujimoto,&nbsp;Jun-ichi Hotta,&nbsp;Kuniyuki Hatori","doi":"10.1002/jemt.70163","DOIUrl":"10.1002/jemt.70163","url":null,"abstract":"<div>\u0000 \u0000 <p>To evaluate whether nanoscale configurational changes in cytoskeletal filaments can be detected, we imaged AZDye532-labeled actin filaments bound to various myosin fragments in vitro using a home-built, low-cost direct stochastic optical reconstruction microscopy (dSTORM) system. The microscope, constructed by modifying a conventional fluorescence setup with a multimode 532-nm laser, achieved a spatial resolution of 25 nm. In the absence of ATP, actin filaments tethered to myosin subfragment-1 (S1) immobilized on a collodion-coated glass surface exhibited an average full width at half maximum (FWHM) of 38 nm. Replacing S1 with full-length myosin increased the FWHM to approximately 68 nm, indicating that the super-resolution images capture size-dependent configurational differences arising from thermal fluctuations of the tethering molecules. Desmin intermediate filaments labeled at their single cysteine residue with Alexa Fluor 532 maleimide were also imaged at super-resolution. Their transverse FWHM averaged 65 nm, and partially repeating axial intensity peaks with a spacing of approximately 45 nm were detected. These results suggest that periodic structural elements within intermediate filaments can be visualized under appropriate conditions. Overall, these findings demonstrate that an accessible dSTORM setup provides a practical platform for detecting nanoscale configurational changes in cytoskeletal filaments interacting with related proteins.</p>\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 11","pages":"1270-1279"},"PeriodicalIF":2.1,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148549833","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}

{"title":"A Deep Learning Pipeline for Cell Segmentation and Viability Quantification in 3D Constructs From Fluorescence Microscopy Images","authors":"Federica Valtellina,&nbsp;Francesco Iannacci,&nbsp;Bianca Maria Colosimo,&nbsp;Mattia Sponchioni","doi":"10.1002/jemt.70170","DOIUrl":"10.1002/jemt.70170","url":null,"abstract":"<p>With the increasing adoption of 3D cell cultures and bioprinting in biomedical research, there is a growing demand for reliable and accurate monitoring methods. While fluorescence microscopy is widely accepted for 2D cell cultures, when introducing the third dimension it often suffers from signal attenuation and out-of-focus interference, making automated image analysis challenging. In this study, a hybrid pipeline that integrates deep learning and traditional image processing techniques is presented with the aim of establishing an automated tool for cell segmentation and viability assessment in 3D from fluorescence microscopy images. A <i>U</i>\u0000 ²-Net architecture was trained on 2D cell cultures for cell segmentation, while watershed-based separation and intensity-based classification were employed for viability detection. The model demonstrated accurate segmentation with minimal overfitting. Quantitative comparisons with manual counts and ImageJ-based results confirmed its accuracy. We then demonstrated the possibility of extending this model to 3D constructs by tracking cell density and viability during time in the case of human umbilical vein endothelial cells bioprinted in gelatin methacrylate at different seeding densities, taken as case study. Automated counts closely matched manual ones, highlighting the method reliability and minimal invasiveness avoiding the need for sacrificing the construct. Hence, this approach provides a scalable, reproducible, and efficient alternative to manual counting in 3D environments, enabling high-throughput analysis of complex fluorescence microscopy data.</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 11","pages":"1327-1338"},"PeriodicalIF":2.1,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jemt.70170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713362","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}

{"title":"Improved Post-Processing Framework for Whole Slide Imaging and 3D Reconstruction","authors":"Emily L. De Leon Alvarado,&nbsp;Daryan Chitsaz,&nbsp;Ilana R. Leppert,&nbsp;Jennifer S. W. Campbell,&nbsp;Yangyingqi Zhou,&nbsp;G. Bruce Pike,&nbsp;Timothy E. Kennedy,&nbsp;Christine L. Tardif,&nbsp;Christopher D. Rowley","doi":"10.1002/jemt.70164","DOIUrl":"10.1002/jemt.70164","url":null,"abstract":"<p>Whole slide imaging (WSI) enables high-resolution digitization of histological sections, but 3D reconstruction from serial slides remains hindered by illumination artifacts, tile inconsistencies, and section-specific geometric distortions. We present an enhanced reconstruction framework that addresses both tile-level intensity inhomogeneity and instability in deformable registration. First, we introduce an overlap-aware illumination correction that refines an initial flat-field estimate by adding an intensity equality constraint in overlapping tile regions. Second, intensity normalization of sections was performed before reconstruction to ensure consistent intensity profiles among serial sections. Lastly, we incorporate level-set method (LSM) constraints into an existing volumetric reconstruction framework to stabilize alignment. The framework was developed for marmoset brain histology, including within-specimen blockface photography and magnetic resonance imaging (MRI). The LSM reconstruction was validated using a synthetic banana image with a known initial shape and with mouse histology reconstructed using an external 3D MRI reference. Overlap-aware correction in the marmoset WSI significantly reduced residual gridding artifacts across 112 sections (Friedman <i>χ</i>\u0000 ²(2) = 890.04, <i>p</i> &lt; 2.2 × 10⁻¹⁶). Within the marmoset dataset, intensity normalization reduced inter-slice intensity variability (coefficient of variation: 0.392 to 0.246) and incorporation of LSM consistently improved registration accuracy by approximately 10% in Normalized Mutual Information scores and 3%–5% in Structural Similarity Index Measure. LSM also yielded improved structural correspondence in both gray matter and white matter structures.</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 11","pages":"1280-1295"},"PeriodicalIF":2.1,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jemt.70164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148549849","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}

{"title":"Automated Detection and Classification of Pollen Grains Using YOLO-Based Deep Learning Models","authors":"Zeynep Türker,&nbsp;Onur Mutlu,&nbsp;Uğur Şevik,&nbsp;Kamil Coşkunçelebi","doi":"10.1002/jemt.70165","DOIUrl":"10.1002/jemt.70165","url":null,"abstract":"<div>\u0000 \u0000 <p>Authentication of high-value honey, particularly from the biodiverse Anzer Valley, relies on melissopalynology to verify botanical origin. However, traditional manual analysis is time-consuming and subject to observer bias. This study developed an automated system for the simultaneous detection and classification of 53 pollen grains belonging to ecologically and apiculturally important taxa distributed in Anzer Valley using advanced deep learning architectures. A novel dataset was constructed from approximately 2095 light microscopy images of the pollen grains, enhanced to 19,860 images via geometric and photometric data augmentation to improve model robustness. Two state-of-the-art object detection models, YOLOv11 and YOLOv12, were trained and compared. The YOLOv11 architecture outperformed YOLOv12, demonstrating superior stability and precision with a mean Average Precision ([email protected]) of 93.2% and an F1-score of 0.896. The model showed exceptional generalization on unseen data (recall 90.6%), successfully handling the visual complexity of raw microscopic samples. Misclassifications were minimal, primarily occurring among pollen grains belonging to morphologically similar taxa of Geraniaceae and Fabaceae. These findings demonstrate that YOLOv11 provides a reliable, high-throughput framework for automated pollen analysis. By significantly reducing processing time while maintaining high accuracy, this system offers a scalable tool for the identification of pollen grains, overcoming the bottlenecks of manual microscopy. Beyond its technical contribution, this approach may support more objective, standardized, and scalable regulatory practices for honey authentication and geographical origin verification.</p>\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 11","pages":"1306-1326"},"PeriodicalIF":2.1,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148679318","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}

{"title":"A Fast f-Ratio Quantification Method for SEM-EDS Based on the XPP Analytical Model and Iterative Algorithms","authors":"Jiayi Chen,&nbsp;Haojie Zhu,&nbsp;Yiling Huang,&nbsp;Zeyu Chen,&nbsp;Xuemei Song,&nbsp;Fan Peng,&nbsp;Wei Zheng,&nbsp;Yi Zeng","doi":"10.1002/jemt.70168","DOIUrl":"10.1002/jemt.70168","url":null,"abstract":"<div>\u0000 \u0000 <p>Although the f-ratio method effectively eliminates the influence of beam current fluctuations in scanning electron microscope/energy dispersive spectroscopy (SEM-EDS) quantitative analysis, its practical application remains strictly limited by the reliance on time-consuming Monte Carlo (MC) simulations. To overcome this fundamental computational bottleneck, a novel fast f-ratio quantification method combining the XPP analytical model with a dynamic iterative numerical algorithm is proposed in this work. By substituting the MC simulation with the XPP model, the computation time for theoretical intensities is drastically reduced from hours to milliseconds. Experimental validations performed on binary systems (CdSe, InAs, and ZnSe) and a ternary mineral (FeAsS) demonstrate that the proposed method achieves a remarkably low quantification error ranging from 0.6% to 2.8% after merely a few iterations, completely circumventing the requirement of a preset standard database. In summary, this study provides a new framework for real-time, high-precision standardless or standard-limited EDS quantitative analysis.</p>\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 11","pages":"1296-1305"},"PeriodicalIF":2.1,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148592717","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}

{"title":"Transformer-Enhanced Deep CNN for Early Detection of Uterine Cancer in Histopathological Images.","authors":"P M Lavanya, Mahesh Muthulakshmi R, P Baraneedharan, D Rasi, K Amuthambigaiyin Sundari, D Sheema","doi":"10.1002/jemt.70176","DOIUrl":"https://doi.org/10.1002/jemt.70176","url":null,"abstract":"<p><p>Uterine cancer is one of the most common malignancies in women, and early detection is vital for improving clinical outcomes and enhancing women's well-being. However, subtle tissue variations make manual diagnosis subjective and time-consuming. This study presents a transformer-enhanced deep convolutional neural network (TE-DCNN) for automated classification and segmentation of uterine histopathological images. The architecture integrates convolutional layers for local feature extraction with transformer modules for global contextual learning, supported by dual channel-spatial attention (SA) to highlight diagnostically critical regions. Evaluated on four classes-Normal Endometrium, Endometrial Hyperplasia, Endometrial Polyp, and Endometrial Adenocarcinoma, the model achieved 99% accuracy, 0.97 sensitivity, and 0.98 specificity, outperforming CNN and vision transformer (ViT) baselines. Visual segmentation confirmed precise localization of malignant areas, enhancing interpretability. The proposed TE-DCNN offers a fast, explainable, and reliable tool for early uterine cancer diagnosis and can be extended to other gynecological imaging tasks.</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149111816","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}

{"title":"Virtual Staining for Nonlinear Imaging of Cancers.","authors":"Zelai Deng, Yuchen Wu, Zhiqing Zhang","doi":"10.1002/jemt.70188","DOIUrl":"https://doi.org/10.1002/jemt.70188","url":null,"abstract":"<p><p>Nonlinear imaging such as third-harmonic generation (THG) microscopy can produce label-free images of tissue with histopathological quality, yet the interpretation of label-free images is challenging. Virtual staining can convert label-free images into histology-like images, but its application to nonlinear imaging remains limited even though it has been well established for fluorescence and bright-field imaging. In this review, we summarize the optical principles of several nonlinear imaging modalities, including multiphoton excitation fluorescence, stimulated Raman scattering, and THG microscopy. We then review the recent progression in virtual staining, major network architectures and metrics, and the main challenges for translation into the clinic. In particular, we emphasize the challenges of virtual staining in THG imaging. Unlike molecularly specific contrast mechanisms, THG contrast arises from spatial variations in refractive index and/or third-order nonlinear susceptibility. Consequently, the collected THG signals may reflect multiple structural sources, including water-lipid interfaces and chromatin-associated optical heterogeneity within the nucleus, making it complex and difficult to establish a direct correspondence between THG features and histological labels. By linking imaging to computational methods, this review highlights key directions for advancing THG-oriented virtual staining towards clinical translation.</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149111991","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}