AIMC Topic: Cytoplasm

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Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis.

Nature communications Mar 14, 2025
Genetic variants can affect protein function by driving aberrant subcellular localization. However, comprehensive analysis of how mutations promote tumor progression by influencing nuclear localization is currently lacking. Here, we systematically ch...
Carcinogenesis Mutation Neoplasms Deep Learning Nuclear Localization Signals PTEN Phosphohydrolase Active Transport, Cell Nucleus Cell Nucleus Cytoplasm Humans
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A deep learning approach for automatic recognition of abnormalities in the cytoplasm of neutrophils.

Computers in biology and medicine Jun 4, 2024
BACKGROUND AND OBJECTIVES: This study aims to develop and evaluate NeuNN, a system based on convolutional neural networks (CNN) and generative adversarial networks (GAN) for the automatic identification of normal neutrophils and those containing seve...
Neutrophils Cytoplasm Humans Deep Learning Neural Networks, Computer Image Processing, Computer-Assisted
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Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images.

Computer methods and programs in biomedicine May 11, 2024
BACKGROUND AND OBJECTIVE: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinic...
Neural Networks, Computer Algorithms Humans Image Processing, Computer-Assisted Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Carcinoma, Squamous Cell Reproducibility of Results Cell Nucleus Cytoplasm
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Comprehensive data analysis of white blood cells with classification and segmentation by using deep learning approaches.

Cytometry. Part A : the journal of the International Society for Analytical Cytology Apr 2, 2024
Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previous studies was that they used more than one dataset, but used them separately. No study has been fo...
Cytoplasm Data Analysis Deep Learning Leukocytes Cell Nucleus Image Processing, Computer-Assisted Humans Neural Networks, Computer
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A Laplacian Pyramid Based Generative H&E Stain Augmentation Network.

IEEE transactions on medical imaging Feb 2, 2024
Hematoxylin and Eosin (H&E) staining is a widely used sample preparation procedure for enhancing the saturation of tissue sections and the contrast between nuclei and cytoplasm in histology images for medical diagnostics. However, various factors, su...
Cytoplasm Eosine Yellowish-(YS) Image Processing, Computer-Assisted Machine Learning Staining and Labeling
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Machine learning interpretable models of cell mechanics from protein images.

Cell Jan 8, 2024
Cellular form and function emerge from complex mechanochemical systems within the cytoplasm. Currently, no systematic strategy exists to infer large-scale physical properties of a cell from its molecular components. This is an obstacle to understandi...
Cell Adhesion Focal Adhesions Cytoskeletal Proteins Cytoplasm Machine Learning Models, Biological
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Label-Free Intracellular Multi-Specificity in Yeast Cells by Phase-Contrast Tomographic Flow Cytometry.

Small methods Sep 5, 2023
In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitative phase imaging approach, is the lack of specificity compared to fluorescence microscopy, thus res...
Artificial Intelligence Flow Cytometry Saccharomyces cerevisiae Cytoplasm Microscopy, Fluorescence
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Memristive Neural Networks for Predicting Seizure Activity.

Sovremennye tekhnologii v meditsine Jul 28, 2023
UNLABELLED: is to assess the possibilities of predicting epileptiform activity using the neuronal activity data recorded from the hippocampus and medial entorhinal cortex of mice with chronic epileptiform activity. To reach this goal, a deep artific...
Mice Silicon Dioxide Algorithms Animals Seizures Cytoplasm Neural Networks, Computer
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Deep learning techniques and mathematical modeling allow 3D analysis of mitotic spindle dynamics.

The Journal of cell biology Mar 2, 2023
Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal...
Deep Learning Cytoplasm Imaging, Three-Dimensional Spindle Apparatus Models, Theoretical Cell Line Fluorescent Dyes
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Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy.

Communications biology Dec 3, 2022
Bioluminescence microscopy is an appealing alternative to fluorescence microscopy, because it does not depend on external illumination, and consequently does neither produce spurious background autofluorescence, nor perturb intrinsically photosensiti...
Zebrafish Animals Caenorhabditis elegans Cell Nucleus Mice Deep Learning Microscopy, Fluorescence Cytoplasm
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