Microscopy Research and Technique最新文献

{"title":"RETRACTION: Effect of Encapsulation or Grafting on Release Kinetics of Recombinant Human Bone Morphogenetic Protein-2 from Self-Assembled Poly(Lactide-co-Glycolide Ethylene Oxide Fumarate) Nanoparticles","authors":"","doi":"10.1002/jemt.70150","DOIUrl":"10.1002/jemt.70150","url":null,"abstract":"<p>\u0000 \u0000 <b>RETRACTION</b>: <span>A. E. Mercado</span> and <span>E. Jabbari</span>, “ <span>Effect of Encapsulation or Grafting on Release Kinetics of Recombinant Human Bone Morphogenetic Protein-2 from Self-Assembled Poly(Lactide-co-Glycolide Ethylene Oxide Fumarate) Nanoparticles</span>,” <i>Microscopy Research and Technique</i> <span>73</span>, no. <span>9</span> (<span>2010</span>): <span>824</span>-<span>833</span>, https://doi.org/10.1002/jemt.20846.\u0000 </p><p>The above article, published online on 23 August 2010 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editors-in-Chief, Julia Fernandez-Rodriguez and Paula Sampaio; and Wiley Periodicals LLC.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 upon following concerns raised by a third party. An investigation identified duplication of the inset image included in Figure 4 with another previously published article, as well as duplication of Figures 6A and 6B in three other earlier articles by overlapping authors, without disclosure of reuse. Furthermore, the images in Figures 6A and 6B are described differently in some of the earlier articles, raising concerns affecting the interpretation of the results. The editors consider the results and conclusions to be unreliable. The authors disagree with the retraction.</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jemt.70150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147776124","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":"Dose-Dependent Protective Effects of Agmatine on Sublingual Gland Dysfunction and Pancreatic Damage in Experimental Diabetes: Evidence From Histological, Biochemical, and Immunohistochemical Analyses","authors":"Heba Fikry,&nbsp;Lobna A. Saleh,&nbsp;Doaa Ramadan Sadek","doi":"10.1002/jemt.70144","DOIUrl":"10.1002/jemt.70144","url":null,"abstract":"<div>\u0000 \u0000 <p>Diabetes mellitus (DM) induces endocrine and exocrine dysfunction, including salivary gland impairment. While the sublingual gland (SLG) contributes to saliva production, its involvement in diabetes remains understudied. Agmatine (AGM), a naturally occurring polyamine, exhibits antioxidant and anti-inflammatory properties; however, its protective effects on the SLG and pancreas in diabetes have remained largely uninvestigated. This study introduces new insights into SLG injury in diabetes and the dose-dependent protective benefits of AGM through multi-level analysis. Male Wistar rats were rendered diabetic by a single intraperitoneal STZ injection (65 mg/kg) and treated with either low-dose (40 mg/kg) or high-dose (100 mg/kg) AGM for two weeks. SLGs and the pancreas were analyzed by histological, immunohistochemical, and biochemical methods. Mucin secretion (MUC-5B) was measured, oxidative stress markers and pro-inflammatory cytokines were quantified, and structural changes were evaluated using H&amp;E, Alcian Blue/PAS, and Mallory's trichrome staining. Immunohistochemistry included α-SMA and CK17 for SLG and insulin receptor for the pancreas. STZ-induced diabetes resulted in a substantial reduction in MUC-5B, increased oxidative stress and pro-inflammatory markers, damage to acinar and myoepithelial cells, and injury to pancreatic β-cells. Agmatine administration enhanced these parameters in a dose-dependent manner, with high-dose treatment reinstating mucin secretion, regulating oxidative and inflammatory indicators, and maintaining histological and immunohistochemical integrity in both the sublingual gland and pancreas. AGM confers dose-dependent protection against SLG dysfunction and pancreatic damage in diabetic rats via antioxidant, anti-inflammatory, and cytoprotective mechanisms. These findings highlight the therapeutic potential of AGM in preventing salivary gland dysfunction and maintaining pancreatic function in diabetes.</p>\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 9","pages":"1158-1184"},"PeriodicalIF":2.1,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147521334","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":"Mechanical Behavior of A375 Cell Membrane Puncture Based on AFM Experiments and Finite Element Analysis.","authors":"Wei Zheng, Yan Mi, Chi Ma, Jiayu Chen","doi":"10.1002/jemt.70166","DOIUrl":"https://doi.org/10.1002/jemt.70166","url":null,"abstract":"<p><p>Cell membrane rupture plays a pivotal role in drug delivery, physical tumor therapies, and cellular mechanobiology. Despite its importance, the quantitative mechanical mechanisms governing membrane failure remain poorly understood. A375 human melanoma cells were employed as a representative model to investigate membrane rupture behavior through the integration of atomic force microscopy (AFM) puncture experiments and finite element simulations. The AFM measurements yielded a membrane rupture force of 27.09 ± 0.446 nN, an indentation depth of 3.46 ± 0.429 μm, and a rupture energy of 16.77 ± 0.902 fJ. Based on these experimentally obtained parameters, a finite element model was developed to reproduce the stress evolution and failure process associated with membrane puncture. The predicted equivalent stress, equivalent strain, and strain energy density differed from the experimentally derived values by only 4.4%, 4.9%, and 8.1%, respectively, demonstrating good agreement between simulation and experiment. Comparative analyses of linear elastic, elastoplastic, and hyperelastic constitutive formulations further revealed that the hyperelastic model provided the most accurate representation of membrane puncture behavior. This finding highlights the dominant role of large deformation nonlinear mechanics in governing membrane failure during AFM puncture. Overall, the combined experimental and computational framework established in this work offers quantitative insights into the mechanics of cell membrane rupture and provides a useful platform for future investigations of membrane damage mechanisms and biomechanical modeling of living cells.</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148679408","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 Semi-Automated Microfluidic Platform Employing Machine Learning Analysis to Study Adhesion Kinetics in Acute Myeloid Leukemia.","authors":"Driti Ashok, Dennis Raith, Gabriel Kalweit, Maria Kalweit, Anusha Klett, Justus Duyster, Jonas Bermeitinger, Tanja Nicole Hartmann","doi":"10.1002/jemt.70161","DOIUrl":"https://doi.org/10.1002/jemt.70161","url":null,"abstract":"<p><p>Current platforms for drug screening typically do not account for the tumor microenvironment. Microfluidic shear flow assays provide a highly sensitive tool for studying tumor cell communication at the single cell level with the microenvironment. Adhesion thereby serves as a functional readout that reflects cellular state, including loss of viability. However, previous platforms required extensive manual handling and time-consuming post-assay analysis. We developed a bright-field microscopy-enabled, semi-automated shear flow platform that combines hardware operation with a machine-learning-based analysis pipeline. The algorithm delivers consistent, high-quality results within minutes with a precision of 98.3% and a recall of 99.1%, indicative of high tracking specificity and object discrimination.</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562444","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":"Development of a Pixelated STEM-In-SEM Detector for Microstructural Feature Characterization.","authors":"Julien Aubourg, Emmanuel Bouzy, Jean-Jacques Fundenberger, Julien Guyon, Yudong Zhang, Luc Morhain, Jean-Claude Ménard, Guillaume Brunetti, Antoine Guitton","doi":"10.1002/jemt.70159","DOIUrl":"https://doi.org/10.1002/jemt.70159","url":null,"abstract":"<p><p>The Scanning Transmission Electron Microscopy in a Scanning Electron Microscope (STEM-in-SEM) approach proposed in this work addresses the possibilities and limitations of a prototype developed for microstructural characterization in thin foils. The developed detector comprises a digital micromirror device (DMD) that can generate user-defined apertures for electron microscopy. As such, TEM-like characterizations are possible while minimizing the presence of artifacts that sometimes hinder analyses. In addition, the developed detector benefits from an On-Axis Transmission Kikuchi Diffraction (On-Axis TKD) geometry, allowing relatively fast diffraction pattern acquisition with a high signal-to-noise ratio. The experimental results demonstrate both the ability to obtain diffraction patterns and to image microstructural defects-including diffraction contrast to characterize dislocations-under adjustable bright-field and dark-field conditions. The proposed Pixelated STEM-in-SEM technique is part of a broader framework aimed at transferring TEM techniques to the Scanning Electron Microscope (SEM).</p>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562446","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":"Microscopy Research and Technique—Building on a Legacy, Shaping the Future","authors":"Paula Sampaio,&nbsp;Julia Fernandez-Rodriguez","doi":"10.1002/jemt.70135","DOIUrl":"10.1002/jemt.70135","url":null,"abstract":"","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 8","pages":"1062-1063"},"PeriodicalIF":2.1,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147377303","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":"Scanning Electron Microscopic Evaluation of Indocyanine Green-Loaded Cerium Oxide Nanoparticle-Mediated Photodynamic Therapy and Potassium Titanyl Phosphate Laser as Post-Space Disinfectant: Effects on Smear Layer Removal and Bond Strength","authors":"Fahad Alkhudhairy","doi":"10.1002/jemt.70132","DOIUrl":"10.1002/jemt.70132","url":null,"abstract":"<div>\u0000 \u0000 <p>This study evaluated the efficacy of novel post space disinfection protocols—indocyanine green-loaded cerium oxide nanoparticle-mediated photodynamic therapy (ICG-CeO₂NPs-PDT), sodium hypochlorite (NaOCl), and potassium titanyl phosphate (KTP) laser irradiation—combined with ethylenediaminetetraacetic acid (EDTA) chelation on smear layer (SL) removal and push-out bond strength (PBS) of glass fiber posts (GFP) to root canal dentin. Forty-eight extracted human premolars underwent standardized endodontic treatment and post-space preparation. Specimens were randomly allocated to four groups (<i>n</i> = 12): Group 1 (saline + EDTA control), Group 2 (2.5% NaOCl + EDTA), Group 3 (KTP laser + EDTA), and Group 4 (ICG-CeO₂NPs-PDT + EDTA). SL removal was quantified using scanning electron microscopy with a four-point scoring system. Following GFP cementation and thermocycling (10,000 cycles), PBS was measured at cervical, middle, and apical regions. Failure modes were classified as adhesive, cohesive, or mixed. Data were analyzed using one-way ANOVA and Tukey's post hoc test (<i>α</i> = 0.05). Groups 2 and 4 demonstrated significantly superior SL removal (scores 1.12–2.45 and 1.17–2.20, respectively) compared to Groups 1 and 3 (3.31–3.99 and 3.40–3.99, <i>p</i> &lt; 0.05). Correspondingly, Groups 2 and 4 achieved 40%–60% higher push-out bond strength (6.71–7.88 MPa and 7.02–8.45 MPa) versus Groups 1 and 3 (4.51–5.27 MPa and 4.73–5.48 MPa, <i>p</i> &lt; 0.05). Groups 2 and 4 exhibited predominantly cohesive failures (50%–80%), while Groups 1 and 3 showed adhesive failures (60%–90%). ICG-CeO₂NPs-PDT demonstrates efficacy equivalent to conventional NaOCl-EDTA irrigation for optimizing glass fiber post adhesion, outperforming KTP laser and saline protocols, warranting clinical evaluation.</p>\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 8","pages":"1064-1075"},"PeriodicalIF":2.1,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147365798","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":"Detection of Lung and Colon Cancer Using XRx-Net With Histopathological Images","authors":"Zulaikha Beevi Sulaiman,&nbsp;Sarojini Balakrishnan,&nbsp;Rama Gaikwad,&nbsp;Sreenu Ponnada","doi":"10.1002/jemt.70122","DOIUrl":"10.1002/jemt.70122","url":null,"abstract":"<div>\u0000 \u0000 <p>Lung and Colon Cancer (LCC) is a prevalent type of cancer influenced by factors such as genetics, smoking, second-hand smoke exposure, and alcohol consumption. Traditionally, cancer diagnosis, including grading and staging, relies on histopathology, where pathologists examine samples under a microscope. This process is time-consuming and complex. To address these challenges, the Xception ResNeXT Network (XRx-Net) framework is introduced for more efficient LCC detection. In this approach, histopathological images of LCC are processed through an Adaptive Bilateral Filter (ABF) for initial filtering. Next, hookNet is used to segment the cancerous cells. Features are then extracted using Wavelet texture analysis and Convolutional Neural Networks (CNNs). Later, these features are input into the LCC detection phase, where the XRx-Net framework is a combination of the Xception model and ResNeXt Networks employed for classification. The framework successfully identifies various outputs, including lung benign tissue, lung adenocarcinoma, lung squamous cell carcinoma, colon benign tissue, and colon adenocarcinoma. The XRx-Net framework achieves a testing accuracy of up to 91.34%, with a True Positive Rate (TPR) of 92.36% and a True Negative Rate (TNR) of 90.14%.</p>\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 8","pages":"1111-1129"},"PeriodicalIF":2.1,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147444014","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":"Accuracy of Automatic Quantitative Analysis of Pathological Tissue Images Using ChatGPT Data Analyst: Comparison With Semi-Automated Analysis in an MC38 Mouse Liver Metastasis Model","authors":"Yuta Sannomiya,&nbsp;Takuya Sakamoto,&nbsp;Yuka Hiramatsu,&nbsp;Moemi Ueshima,&nbsp;Yao Liu,&nbsp;Hisashi Nishiki,&nbsp;Akifumi Hashimoto,&nbsp;Yasuto Tomita,&nbsp;Takashi Miyata,&nbsp;Koichi Okamoto,&nbsp;Takeo Shimasaki,&nbsp;Hideto Fujita,&nbsp;Sohsuke Yamada,&nbsp;Hiroyuki Takamura","doi":"10.1002/jemt.70128","DOIUrl":"10.1002/jemt.70128","url":null,"abstract":"<div>\u0000 \u0000 <p>Colorectal cancer has a high incidence and mortality rate worldwide. In advanced cases, liver metastasis frequently occurs and determines the prognosis. Image analysis of pathological tissues from organs with metastasis is essential for tumor research and diagnosis, but relies on manual or semi-automated software processing, challenging user expertise, and efficiency. We evaluated the image analysis capabilities of ChatGPT (Data Analyst) to automatically quantify tumor regions in H&amp;E-stained sections from a mouse metastasis model established using MC38 cells. Using ChatGPT Data Analyst, we analyzed the tumor area, total liver area, tumor proportion, and tumor count and compared the results with those obtained using conventional ImageJ analysis. Area measurements showed a high correlation (<i>r</i> &gt; 0.99) and agreement (ICC &gt; 0.9) between the ChatGPT Data Analyst and ImageJ, with high reproducibility upon reanalysis. Given that the clarity of the boundary between the tumor and liver parenchyma is influenced by staining conditions, we investigated optimal protocols and identified specific conditions (hematoxylin for 2 min, eosin for 30 s) that are crucial for enhancing extraction accuracy. Furthermore, comprehensive benchmarking revealed that advanced general-purpose LLMs (GPT-5.1 and Gemini-2.5 Pro) achieved accuracy comparable to that of a specialized AI framework (SAM + PLIP) and commercial software (NIS-Elements AR). ChatGPT Data Analyst offers a novel method for pathological image analysis based on prompt instructions, with an accuracy comparable to that of conventional methods. Future improvements in tumor count detection through ChatGPT Data Analyst updates and standardized staining conditions will enhance its application in pathological research and diagnosis of tumors.</p>\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 8","pages":"1095-1110"},"PeriodicalIF":2.1,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147390363","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":"Frequency-Aware Feature Fusion Driven Multimodal Cell Microscopic Image Segmentation Framework","authors":"Shuhan Chen,&nbsp;Zihan Li,&nbsp;Xinyuan Zhang,&nbsp;Tianyue Xiong,&nbsp;Yujiao Song,&nbsp;Zhe Mao,&nbsp;Zijian Gao,&nbsp;Yang Zhang,&nbsp;Zhiyong Liu,&nbsp;Guanglan Liao,&nbsp;Lei Nie","doi":"10.1002/jemt.70137","DOIUrl":"10.1002/jemt.70137","url":null,"abstract":"<div>\u0000 \u0000 <p>Multimodal cell microscopic image segmentation is a core component of high-content imaging and analysis (HCIA) technology, and its segmentation accuracy directly impacts the precision of HCIA analysis results. Deep learning-based cell segmentation methods have been widely applied due to their end-to-end nature. However, when processing multimodal cell microscopy images, challenges such as missed cell detection, image degradation, and insufficient feature utilization persist, leading to limited segmentation accuracy. To address these issues, this paper proposes a novel deep learning framework designed to achieve accurate and efficient segmentation of multimodal cell microscopy images without manual parameter tuning or algorithm switching. This framework enhances segmentation accuracy through three core modules: a weighted bidirectional feature pyramid network (BiFPN), which reduces the problem of missed cell detection in low-contrast regions by constructing a weighted bidirectional cross-scale connection mechanism; frequency-aware feature fusion (FreqFusion), which precisely identifies cell boundaries under complex degradation conditions through a frequency-domain adaptive mechanism; and a mixed local channel attention (MLCA) mechanism, which guides the model to focus on critical channels and regions that are difficult to segment. On a custom dataset, the proposed framework achieved an average precision of 95.07%, a cell detection rate of 96.72%, and a segmentation speed of 59.23 FPS. Its robust generalization ability and computational efficiency were further validated on public datasets. These advancements lay a solid foundation for the quantitative analysis of microscopic images in precision medicine.</p>\u0000 </div>","PeriodicalId":18684,"journal":{"name":"Microscopy Research and Technique","volume":"89 8","pages":"1076-1094"},"PeriodicalIF":2.1,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147377299","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}