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Related Concept Videos

The Nucleus01:32

The Nucleus

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The nucleus is a membrane-bound organelle that acts as a control center in a eukaryotic cell. It contains chromosomal DNA, which controls gene expression and precisely regulates the production of proteins within the cell. In contrast, the DNA inside the mitochondria and chloroplast only carries out functions that are specific to those organelles.
Arrangement of DNA within Nucleus
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The Nucleolus02:55

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The nucleolus is the most prominent substructure of the nucleus. When it was first discovered, it was considered to be an isolated organelle that forms fibrils and granules. In 1931, the relationship between the nucleolus and chromosomes was first described by Heitz. He observed that the appearance and size of nucleolus varies depending on the stage of the cell cycle. He also noticed constricted regions on different chromosomes clustered together at definite cell cycle stages. These regions,...
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Additional Subnuclear Structures02:10

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The eukaryotic nucleus is a double membrane-bound organelle that contains nearly all of the cell’s genetic material in the form of chromosomes. It is rightly called the “brain” of the cell as it shoulders the responsibility of responding to various physiological processes, stress, altered metabolic conditions, and other cellular signals. 
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Neurons, the fundamental units of the nervous system, can be classified based on both their structural and functional characteristics.
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Related Experiment Video

Updated: Sep 22, 2025

Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion
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Nucleus classification in histology images using message passing network.

Taimur Hassan1, Sajid Javed2, Arif Mahmood3

  • 1Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, P.O. Box 127788, UAE.

Medical Image Analysis
|May 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel message passing network for nucleus classification in histology images. The method improves computational pathology by identifying nuclear communities, enhancing tumor micro-environment profiling.

Keywords:
Computational pathologyEnd-to-end graph learningNuclear communitiesWhole slide images

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Area of Science:

  • Computational pathology
  • Digital histology
  • Bioinformatics

Background:

  • Accurate nucleus identification is crucial for tumor micro-environment profiling.
  • Existing computational pathology tools face challenges with nuclear heterogeneity.
  • Graph-based methods offer potential for nucleus classification by integrating appearance and spatial data.

Purpose of the Study:

  • To develop a fully learnable framework for nucleus classification using a message passing network.
  • To improve the identification of distinct nuclear phenotypes in histology images.
  • To enhance computational pathology tools for tumor micro-environment analysis.

Main Methods:

  • Constructed a nearest neighbor graph with nuclei centroids as nodes.
  • Computed appearance and geometric features for graph nodes and edges.
  • Employed a message passing network based on classical network flow for diffusing contextual information.
  • Inferred global information to predict biologically meaningful nuclear communities.

Main Results:

  • The message passing network framework demonstrated improved nucleus classification performance.
  • Learning nuclear communities enhanced the accuracy of nucleus classification tasks.
  • The proposed algorithm showed improved performance across four diverse, publicly available datasets.
  • The method can be integrated as a component into existing state-of-the-art computational pathology tools.

Conclusions:

  • The proposed message passing network offers a powerful and learnable approach to nucleus classification.
  • Integrating contextual information through message passing effectively addresses nuclear heterogeneity.
  • This method advances computational pathology by improving tumor micro-environment profiling and nucleus classification accuracy.