Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Polarity of the Cytoskeleton

Polarity of the Cytoskeleton

The intrinsic polarity of cells can be primarily attributed to two factors- i) the asymmetric accumulation of mobile components such are regulatory molecules and subcellular components across the cell and ii) the orientation of polar cytoskeletal filaments that make up the cytoskeletal networks, specifically microfilaments, and microtubules arranged along the axis of polarity. Interactions between the cytoskeletal filaments are crucial for the establishment and maintenance of the polar nature...

You might also read

Related Articles

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

Sort by

Same author

Deficiency of G9a boosts muscle regeneration through IL13/Musclin-mediated crosstalk between macrophage and myofiber.

Cell death & disease·2026

Same author

Enhancing Lesion Segmentation via Medical Image-Mask Pair Synthesis using Phenotype-Conditioned Diffusion Models.

IEEE journal of biomedical and health informatics·2026

Same author

The genomic landscape of recombination in rice revealed by a large nested association mapping population.

The New phytologist·2026

Same author

Targeted Biomimetic Stem Cell Membrane-Exosome Composite Delivery System for the Treatment of Bone Defects.

ACS applied materials & interfaces·2026

Same author

EXPLORING THE MECHANISM OF ACTION OF HEMP SEEDS (CANNABIS SATIVA L.) IN TREATING OSTEOPOROSIS USING NETWORK PHARMACOLOGY.

Georgian medical news·2026

Same author

Laboratory Test-Guided Medical Image Generation for Multi-Modal Disease Prediction.

IEEE transactions on medical imaging·2026

Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026

Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026

Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026

Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026

Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026

Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 6, 2026

Three-dimensional Confocal Analysis of Microglia/macrophage Markers of Polarization in Experimental Brain Injury

Three-dimensional Confocal Analysis of Microglia/macrophage Markers of Polarization in Experimental Brain Injury

Published on: September 4, 2013

Polarity Prompting Vision Foundation Models for Pathology Image Analysis.

Chong Yin, Siqi Liu, Kaiyang Zhou

IEEE Transactions on Medical Imaging

|June 10, 2025

Summary

This summary is machine-generated.

Quantitative Attribute-based Polarity Visual Prompting (Q-PoVP) improves non-alcoholic fatty liver disease diagnosis by analyzing pathology images. This method enhances accuracy and interpretability for better clinical decisions.

More Related Videos

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy

Published on: September 8, 2023

Related Experiment Videos

Last Updated: May 6, 2026

Three-dimensional Confocal Analysis of Microglia/macrophage Markers of Polarization in Experimental Brain Injury

Three-dimensional Confocal Analysis of Microglia/macrophage Markers of Polarization in Experimental Brain Injury

Published on: September 4, 2013

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy

Published on: September 8, 2023

Area of Science:

Medical Imaging
Computational Pathology
Artificial Intelligence in Medicine

Background:

Non-alcoholic fatty liver disease (NAFLD) is a growing health concern requiring accurate diagnostic tools.
Pathology image analysis for NAFLD is challenging due to small datasets and limitations of generic prompting techniques.
Prompt tuning offers potential for vision model adaptation but requires specialized methods for pathological data.

Purpose of the Study:

To introduce Quantitative Attribute-based Polarity Visual Prompting (Q-PoVP), a novel prompting method for pathology image analysis.
To address the inadequacy of generic visual cues in current prompting techniques for complex pathological tissue analysis.
To enhance the accuracy and interpretability of diagnostic models for non-alcoholic fatty liver disease.

Main Methods:

Developed Q-PoVP, incorporating K-function-based spatial and histogram-based morphological attributes for quantitative tissue assessment.
Created a prompt generator to convert quantitative attributes into positive and negative visual prompts for nuanced image interpretation.
Implemented an orthogonal-based polarity visual prompt tuning technique to amplify positive attributes and suppress negative ones, enhancing feature discrimination.

Main Results:

Q-PoVP demonstrated superior performance in diagnostic accuracy across three distinct tasks.
The method significantly improved the interpretability of pathology image analysis compared to existing techniques.
Task-specific prompting using Q-PoVP proved valuable for clinical settings requiring reliable and transparent diagnostic reasoning.

Conclusions:

Q-PoVP offers a significant advancement in pathology image analysis for non-alcoholic fatty liver disease diagnosis.
The method provides a dual advantage of enhanced accuracy and improved interpretability, crucial for clinical applications.
This approach facilitates more informed patient care decisions through transparent and reliable diagnostic insights.