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Related Concept Videos

Autophagy01:27

Autophagy

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Autophagy is a self-digesting process by which a cell protects itself from threats both within and outside the cell, ranging from abnormal proteins to invading bacteria. In this process, obsolete components of the cell and invading microbes are degraded by hydrolytic enzymes active in an acidic environment of the lysosomal lumen.
An autophagic pathway consists of a series of signaling events activated in response to diverse stress and physiological conditions such as food deprivation,...
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Autophagic Cell Death01:18

Autophagic Cell Death

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Christian de Duve discovered “autophagy,” a process in which cellular components are engulfed by membrane-bound organelles called autophagosomes. The autophagosomes then fuse with lysosomes to digest the enclosed contents. Autophagy is generally activated in cells to prevent cell death. However, cell death is triggered when the damage is beyond repair.
Autophagy and Apoptosis
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Delivery Pathways to the Lysosome01:36

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Eukaryotic cells use different mechanisms to eliminate toxic waste obsolete and worn-out substances. Lysosomes play a pivotal role in this, and hence, these substances are carried to the lysosome from other parts of the cell and extracellular space through different pathways. The most elaborately studied pathways to the lysosome are the endocytic pathways.
Endocytosis
In endocytosis, the cell membrane takes up macromolecules and particles from the surrounding medium. Clathrin-mediated...
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Phagocytosis of Apoptotic Cells01:17

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Cells undergoing apoptosis form apoptotic bodies that must be removed immediately to prevent inflammation, autoimmune diseases, and necrosis. Phagocytosis is carried out by professional phagocytes such as macrophages or  immature dendritic cells. Non-professional phagocytes such as  epithelial cells and fibroblasts also take part in this process; however, they are not as effective as professional phagocytes. 
Normal cells contain receptors that prevent them from being recognized...
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Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
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Overview of Cell Death01:30

Overview of Cell Death

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Cell death is an essential process where the body gets rid of old or damaged cells. Cell proliferation and death need to be balanced, as an imbalance between the two may lead to cancer or autoimmune diseases.
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Related Experiment Video

Updated: Jan 18, 2026

Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond
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Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond

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Explainable AI to unveil cellular autophagy dynamics.

Oriana Presacan1, María Hernández Mesa2,3,4, Alexandru C Aldea5

  • 1AI Multimedia Lab, Campus Research Institute, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania.

Plos One
|September 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an automated deep learning pipeline to analyze cellular autophagy, significantly reducing manual effort in microscopy image analysis. The advanced framework accurately detects, segments, and classifies autophagic states, accelerating biomedical research.

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Related Experiment Videos

Last Updated: Jan 18, 2026

Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond
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Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy
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Area of Science:

  • Cell Biology
  • Computational Biology
  • Artificial Intelligence in Medicine

Background:

  • Autophagy is a crucial cellular process for homeostasis, but its complexity hinders manual analysis in research.
  • Dysfunctional autophagy is linked to diseases like cancer and neurodegenerative disorders.
  • Automating autophagy analysis is essential for advancing our understanding and therapeutic strategies.

Purpose of the Study:

  • To develop and validate a deep learning computational pipeline for automated analysis of autophagy.
  • To enhance the efficiency and accuracy of quantifying autophagic processes from fluorescence microscopy images.
  • To provide interpretable insights into autophagy dynamics using explainable AI.

Main Methods:

  • Utilized a dataset of 6,240 fluorescence microscopy images (CELLULAR dataset).
  • Integrated deep learning models: YOLOv8 for object detection, U-Net++ for cell segmentation, and a vision transformer for classification.
  • Developed a custom cell tracking algorithm and employed explainability methods (Class Activation Mapping, t-SNE).

Main Results:

  • Achieved high performance: YOLOv8 (mAP50=0.80), U-Net++ (IoU=0.82), Vision Transformer (Accuracy=0.86).
  • Custom tracking algorithm successfully handled cell division and morphological changes without annotated data.
  • Explainability methods validated model decisions and provided deeper data insights.

Conclusions:

  • The developed pipeline automates complex autophagy analysis, significantly reducing manual workload.
  • Deep learning and explainable AI offer powerful tools to streamline biomedical research and uncover autophagy dynamics.
  • Findings validated by experts, demonstrating potential to advance autophagy research and disease understanding.