A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia.
Neuroinflammation is a key feature of human neurodisease including neuropathy and neurodegenerative disease and is driven by the activation microglia, immune cells of the nervous system. During activation microglia release pro-inflammatory cytokines as well as reactive oxygen species (ROS) that can...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0320219 |
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| author | Patricia Sinclair William Jeffries Nadege Lebert Maheen Saeed Aman Ullah Nadine Kabbani |
| author_facet | Patricia Sinclair William Jeffries Nadege Lebert Maheen Saeed Aman Ullah Nadine Kabbani |
| author_sort | Patricia Sinclair |
| collection | DOAJ |
| description | Neuroinflammation is a key feature of human neurodisease including neuropathy and neurodegenerative disease and is driven by the activation microglia, immune cells of the nervous system. During activation microglia release pro-inflammatory cytokines as well as reactive oxygen species (ROS) that can drive local neuronal and glial damage. Phytocannabinoids are an important class of naturally occurring compounds found in the cannabis plant (Cannabis sativa) that interact with the body's endocannabinoid receptor system. Cannabidiol (CBD) is a prototype phytocannabinoid with anti-inflammatory properties observed in cells and animal models. We measured ROS in human microglia (HMC3) cells using CellROX, a fluorescent dynamic ROS indicator. We tested the effect of CBD on ROS level in the presence of three known immune activators: lipopolysaccharide (LPS), amyloid beta (Aβ42), and human immunodeficiency virus (HIV) glycoprotein (GP120). Confocal microscopy images within microglia were coupled to a deep learning model using a convolutional neural network (CNN) to predict ROS responses. Our study demonstrates a deep learning platform that can be used in the assessment of CBD effect in immune cells using ROS image measure. |
| format | Article |
| id | doaj-art-ebed6b7032264f24bfe7d202f406b58d |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ebed6b7032264f24bfe7d202f406b58d2025-08-20T01:55:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e032021910.1371/journal.pone.0320219A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia.Patricia SinclairWilliam JeffriesNadege LebertMaheen SaeedAman UllahNadine KabbaniNeuroinflammation is a key feature of human neurodisease including neuropathy and neurodegenerative disease and is driven by the activation microglia, immune cells of the nervous system. During activation microglia release pro-inflammatory cytokines as well as reactive oxygen species (ROS) that can drive local neuronal and glial damage. Phytocannabinoids are an important class of naturally occurring compounds found in the cannabis plant (Cannabis sativa) that interact with the body's endocannabinoid receptor system. Cannabidiol (CBD) is a prototype phytocannabinoid with anti-inflammatory properties observed in cells and animal models. We measured ROS in human microglia (HMC3) cells using CellROX, a fluorescent dynamic ROS indicator. We tested the effect of CBD on ROS level in the presence of three known immune activators: lipopolysaccharide (LPS), amyloid beta (Aβ42), and human immunodeficiency virus (HIV) glycoprotein (GP120). Confocal microscopy images within microglia were coupled to a deep learning model using a convolutional neural network (CNN) to predict ROS responses. Our study demonstrates a deep learning platform that can be used in the assessment of CBD effect in immune cells using ROS image measure.https://doi.org/10.1371/journal.pone.0320219 |
| spellingShingle | Patricia Sinclair William Jeffries Nadege Lebert Maheen Saeed Aman Ullah Nadine Kabbani A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia. PLoS ONE |
| title | A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia. |
| title_full | A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia. |
| title_fullStr | A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia. |
| title_full_unstemmed | A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia. |
| title_short | A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia. |
| title_sort | predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia |
| url | https://doi.org/10.1371/journal.pone.0320219 |
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