Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Simple Staining Technique01:24

Simple Staining Technique

1.8K
OverviewStaining techniques in microscopy enhance the visualization of microorganisms by increasing contrast and allowing the differentiation of cellular structures. Simple staining is one of the fundamental methods used to observe the basic morphological characteristics of microorganisms, including their size, shape, and arrangement. This method relies on the application of a single dye to stain the entire cell, producing a clear contrast between the cell and the background.FixationFixation is...
1.8K
Fixation and Sectioning01:03

Fixation and Sectioning

6.9K
Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...
6.9K
Differential Staining Technique01:26

Differential Staining Technique

1.2K
Differential staining is an essential microbiological technique that exploits variations in cell wall structures to classify and identify microorganisms. It facilitates the distinction of bacteria, aiding in diagnostic and research applications. Two of the most widely used differential staining methods are Gram staining and acid-fast staining, both of which rely on the chemical and structural differences in bacterial cell walls.Gram Staining TechniqueGram staining differentiates bacteria by...
1.2K
Special Staining Techniques01:13

Special Staining Techniques

773
Specialized staining techniques play a vital role in microbiology by enabling the visualization of specific bacterial structures that remain undetectable with standard microscopy methods. These techniques not only enhance the structural visualization of bacterial cells but also provide critical insights into their pathogenicity and classification. Additionally, they support diagnostic and research endeavors in microbiology by identifying key bacterial features.Capsule Staining for Virulence...
773

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Development and Validation of a Pathomics Model for Prognosis Prediction in Neoadjuvant Therapy-Treated Breast Cancer: A Retrospective, Multicenter Study.

MedComm·2026
Same author

YIF1B Mutational Dysregulation Drives Cutaneous Melanoma Progression by Remodeling the TME.

Human mutation·2026
Same author

LncRNA MIR4435-2HG-mediated succinylation of USF1 promotes its protein stability and induces epithelial-mesenchymal transition in HNSCC.

Epigenetics·2026
Same author

Beyond Foundation Models: Distilling Geometric Priors for Lightweight Monocular Depth Estimation in Endoscopy.

IEEE transactions on medical imaging·2026
Same author

ETV7 promotes 5-FU resistance and malignant progression through CXCL1-induced NETs formation in colorectal cancer.

Communications biology·2026
Same author

Functional Characterization of OasiCSP12: A Chemosensory Protein Regulating Olfaction and Phase Change in <i>Oedaleus decorus asiaticus</i>.

Insects·2026

Related Experiment Video

Updated: Nov 11, 2025

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
05:22

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

604

Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks.

Shuting Liu, Baochang Zhang, Yiqing Liu

    IEEE Transactions on Medical Imaging
    |March 30, 2021
    PubMed
    Summary

    This study introduces a new AI method to create virtual immunohistochemistry (IHC) images from standard H&E stains, improving cancer diagnosis accessibility. The approach enhances structural details and ensures consistent pathological properties, outperforming existing techniques.

    More Related Videos

    Histological-Based Stainings Using Free-Floating Tissue Sections
    06:45

    Histological-Based Stainings Using Free-Floating Tissue Sections

    Published on: August 25, 2020

    20.1K
    Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay
    08:24

    Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay

    Published on: September 27, 2021

    3.4K

    Related Experiment Videos

    Last Updated: Nov 11, 2025

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
    05:22

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

    Published on: June 21, 2024

    604
    Histological-Based Stainings Using Free-Floating Tissue Sections
    06:45

    Histological-Based Stainings Using Free-Floating Tissue Sections

    Published on: August 25, 2020

    20.1K
    Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay
    08:24

    Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay

    Published on: September 27, 2021

    3.4K

    Area of Science:

    • Computational pathology
    • Artificial intelligence in histopathology
    • Digital pathology image analysis

    Background:

    • Pathological examination, including hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC), is crucial for cancer diagnosis.
    • H&E staining alone can be insufficient for accurate diagnosis, while IHC provides vital supplementary evidence.
    • Generating virtual IHC images from H&E images offers a solution to IHC's accessibility challenges, particularly in resource-limited settings.

    Purpose of the Study:

    • To develop a novel adversarial learning method for generating realistic Ki-67-stained images from H&E-stained images.
    • To address limitations of existing methods in preserving microscopic structures and pathological consistency.
    • To overcome the difficulty of obtaining pixel-level paired histopathological data.

    Main Methods:

    • Proposed a novel adversarial learning framework incorporating structural similarity and skip connection constraints for enhanced structural detail preservation.
    • Introduced pathology consistency constraint and a pathological representation network to ensure consistent pathological properties between H&E and generated IHC images.
    • Utilized two different unpaired histopathological datasets for empirical validation.

    Main Results:

    • The proposed method demonstrated superior performance in generating Ki-67-stained images compared to state-of-the-art approaches.
    • Achieved significant improvements in microscopic structural preservation and pathological property consistency.
    • Showcased robust and stable performance on unbalanced datasets, indicating strong generalizability.

    Conclusions:

    • The developed method effectively generates virtual IHC images from H&E stains, addressing key limitations of current techniques.
    • This approach holds significant potential for clinical virtual staining and advancing computer-aided multi-staining histology image analysis.
    • The method offers a promising solution for improving cancer diagnosis accessibility, especially in underserved regions.