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

Fixation and Sectioning01:03

Fixation and Sectioning

4.3K
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...
4.3K

You might also read

Related Articles

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

Sort by
Same author

<b>A new species of <i>Casinaria</i> (Hymenoptera: Ichneumonidae) parasitizing on <i>Aporia hippia</i> (Lepidoptera: Pieridae) from China, and its complete mitochondrial genome</b>.

Zootaxa·2026
Same author

Author Correction: Natural selection and genetic diversity maintenance in a parasitic wasp during continuous biological control application.

Nature communications·2026
Same author

DGCD-3D: Difference-guided conditional diffusion model for low-field 3D MRI enhancement to assist stroke assessment.

Medical image analysis·2026
Same author

Assessing the Therapeutic Efficacy of Xihuang Pill in Lung Adenocarcinoma by Single-Cell Raman Spectroscopy.

Analytical chemistry·2026
Same author

Clinical features, outcome and HLA subtypes in Eastern patients with anti-IgLON5 disease: a multicenter study.

Orphanet journal of rare diseases·2026
Same author

Class-Sensitive TPB-Guided Memory Refinement for Online Zero-Shot Anomaly Detection.

Sensors (Basel, Switzerland)·2026
Same journal

UniOCTSeg++: Refined Hierarchical Prompt Strategy and Bi-directional Progressive Consistency Learning for Universal Retinal Layer Segmentation in OCT.

IEEE transactions on medical imaging·2026
Same journal

Volumetric Functional Ultrasound Imaging in Macaques.

IEEE transactions on medical imaging·2026
Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Jun 20, 2025

Variations on Negative Stain Electron Microscopy Methods: Tools for Tackling Challenging Systems
06:06

Variations on Negative Stain Electron Microscopy Methods: Tools for Tackling Challenging Systems

Published on: February 6, 2018

32.7K

PST-Diff: Achieving High-Consistency Stain Transfer by Diffusion Models With Pathological and Structural Constraints.

Yufang He, Zeyu Liu, Mingxin Qi

    IEEE Transactions on Medical Imaging
    |July 18, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces PST-Diff, a novel method using diffusion models to create virtual immunohistochemistry (IHC) images from hematoxylin and eosin (HE) stained slides. This innovation aims to reduce costs and improve diagnostic accuracy in histopathology.

    More Related Videos

    Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
    08:17

    Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

    Published on: August 16, 2021

    1.9K
    Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes
    22:49

    Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

    Published on: December 13, 2012

    35.6K

    Related Experiment Videos

    Last Updated: Jun 20, 2025

    Variations on Negative Stain Electron Microscopy Methods: Tools for Tackling Challenging Systems
    06:06

    Variations on Negative Stain Electron Microscopy Methods: Tools for Tackling Challenging Systems

    Published on: February 6, 2018

    32.7K
    Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
    08:17

    Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

    Published on: August 16, 2021

    1.9K
    Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes
    22:49

    Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

    Published on: December 13, 2012

    35.6K

    Area of Science:

    • Digital Pathology
    • Computational Imaging
    • Artificial Intelligence in Medicine

    Background:

    • Histopathological diagnosis relies on Hematoxylin and Eosin (HE) and Immunohistochemistry (IHC) staining.
    • IHC provides detailed diagnostic information but incurs high costs and time.
    • Staining adjacent slides or re-staining HE slides for IHC can lead to information loss and reduced diagnostic accuracy.

    Purpose of the Study:

    • To develop PST-Diff, a method for generating virtual IHC images from HE images using diffusion models.
    • To enable simultaneous viewing of multiple staining results from a single tissue slide.
    • To address the limitations of traditional staining methods in terms of cost, time, and accuracy.

    Main Methods:

    • Development of PST-Diff, a diffusion model-based method for virtual IHC image generation.
    • Incorporation of an asymmetric attention mechanism (AAM) to preserve local pathological information and ensure target domain adherence.
    • Integration of a latent transfer (LT) module to transfer implicit representations and reduce domain bias.
    • Implementation of a conditional frequency guidance (CFG) module to maintain structural consistency and control image generation.

    Main Results:

    • PST-Diff effectively generates virtual IHC images from HE images.
    • The method maintains pathological consistency through AAM and LT modules.
    • Structural consistency is preserved using the CFG module.
    • PST-Diff demonstrates superior generalization and stable, functionally pathological image generation with top evaluation scores.

    Conclusions:

    • PST-Diff offers a cost-effective and efficient solution for virtual staining in histopathology.
    • The method enhances diagnostic accuracy by allowing multiple virtual stains from a single slide.
    • PST-Diff shows significant potential for clinical virtual staining and pathological image analysis.