AIMC Topic: Mitochondria
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Deep-learning based flat-fielding quantitative phase contrast microscopy.
Optics express
Mar 25, 2024
Quantitative phase contrast microscopy (QPCM) can realize high-quality imaging of sub-organelles inside live cells without fluorescence labeling, yet it requires at least three phase-shifted intensity images. Herein, we combine a novel convolutional ...
Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset.
Cell systems
Jan 18, 2023
Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train mode...
Evaluation of Image Classification for Quantifying Mitochondrial Morphology Using Deep Learning.
Endocrine, metabolic & immune disorders drug targets
Jan 1, 2023
BACKGROUND: Mitochondrial morphology reversibly changes between fission and fusion. As these changes (mitochondrial dynamics) reflect the cellular condition, they are one of the simplest indicators of cell state and predictors of cell fate. However, ...
Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.
Cell reports
Aug 2, 2022
Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demandin...
High-Throughput Image Analysis of Lipid-Droplet-Bound Mitochondria.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2021
Changes to mitochondrial architecture are associated with various adaptive and pathogenic processes. However, quantification of changes to mitochondrial structures is limited by the yet unmet challenge of defining the borders of each individual mitoc...
mRNALoc: a novel machine-learning based in-silico tool to predict mRNA subcellular localization.
Nucleic acids research
Jul 2, 2020
Recent evidences suggest that the localization of mRNAs near the subcellular compartment of the translated proteins is a more robust cellular tool, which optimizes protein expression, post-transcriptionally. Retention of mRNA in the nucleus can regul...
The retina revolution: signaling pathway therapies, genetic therapies, mitochondrial therapies, artificial intelligence.
Current opinion in ophthalmology
May 1, 2020
PURPOSE OF REVIEW: The aim of this article is to review and discuss the history, current state, and future implications of promising biomedical offerings in the field of retina.
Neuroprotective effects of Suhexiang Wan on the in vitro and in vivo models of Parkinson's disease.
Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan
Dec 1, 2019
OBJECTIVE: To examine the role of KSOP1009 (a modified formulation of Suhexiang Wan essential oil) in an animal model of Parkinson's disease (PD) induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) injection.
Opportunities and challenges using artificial intelligence in ADME/Tox.
Nature materials
May 1, 2019
A recent conference organized a panel of scientists representing small and big pharma companies, who work at the interface of machine learning (ML) and absorption, distribution, metabolism, excretion, and toxicology (ADME/Tox). With the recent rebirt...
Machine Learning: Advanced Image Segmentation Using ilastik.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2019
Segmentation is one of the most ubiquitous problems in biological image analysis. Here we present a machine learning-based solution to it as implemented in the open source ilastik toolkit. We give a broad description of the underlying theory and demo...
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