Tu1193 COQ10 PROTECTS GASTRIC ENDOTHELIAL CELLS AGAINST NSAIDS INJURY BY PRESERVING MITOCHONDRIAL STRUCTURE, MEMBRANE POTENTIAL, AND METABOLIC SENSOR. QUANTITATIVE ASSESSMENT USING ARTIFICIAL INTELLIGENCE ANALYSIS

2020 ◽  
Vol 158 (6) ◽  
pp. S-1014
Author(s):  
Andrzej S. Tarnawski ◽  
Neil Hoa ◽  
Amrita Ahluwalia
Keyword(s):  
Artificial Intelligence ◽  
Endothelial Cells ◽  
Membrane Potential ◽  
Quantitative Assessment ◽  
Intelligence Analysis ◽  
Mitochondrial Structure ◽  
Metabolic Sensor

An Integrated Digital Pathology Platform for Tumors Using Artificial Intelligence Analysis

2021 ◽  
Author(s):  
Changjiang Zhou ◽  
Xiaobing Feng ◽  
Hongbin Cai ◽  
Yi Jin ◽  
Harvest F. Gu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Digital Pathology ◽  
Intelligence Analysis

Capillaries demonstrate changes in membrane potential in response to pharmacological stimuli

1998 ◽  
Vol 274 (1) ◽  
pp. H60-H65 ◽  
Author(s):  
Eugene D. McGahren ◽  
James M. Beach ◽  
Brian R. Duling
Keyword(s):  
Endothelial Cells ◽  
Membrane Potential ◽  
Fluorescence Emission ◽  
Cheek Pouch ◽  
Surrounding Tissue ◽  
Hamster Cheek Pouch ◽  
Capillary Membrane ◽  
Resistance Vessels ◽  
Capillary Endothelial Cells

It has been proposed that capillaries can detect changes in tissue metabolites and generate signals that are communicated upstream to resistance vessels. The mechanism for this communication may involve changes in capillary endothelial cell membrane potentials which are then conducted to upstream arterioles. We have tested the capacity of capillary endothelial cells in vivo to respond to pharmacological stimuli. In a hamster cheek pouch preparation, capillary endothelial cells were labeled with the voltage-sensitive dye di-8-ANEPPS. Fluorescence from capillary segments (75–150 μm long) was excited at 475 nm and recorded at 560 and 620 nm with a dual-wavelength photomultiplier system. KCl was applied using pressure injection, and acetylcholine (ACh) and phenylephrine (PE) were applied iontophoretically to these capillaries. Changes in the ratio of the fluorescence emission at two emission wavelengths were used to estimate changes in the capillary endothelial membrane potential. Application of KCl resulted in depolarization, whereas application of the vehicle did not. Application of ACh and PE resulted in hyperpolarization and depolarization, respectively. The capillary responses could be blocked by including a receptor antagonist (atropine or prazosin, respectively) in the superfusate. We conclude that the capillary membrane potential is capable of responding to pharmacological stimuli. We hypothesize that capillaries can respond to changes in the milieu of surrounding tissue via changes in endothelial membrane potential.

scholarly journals Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 155
Author(s):  
Joaquim Carreras ◽  
Naoya Nakamura ◽  
Rifat Hamoudi
Keyword(s):  
Artificial Intelligence ◽  
Overall Survival ◽  
B Cell ◽  
Mantle Cell Lymphoma ◽  
Cell Lymphoma ◽  
High Accuracy ◽  
Gene Set Enrichment Analysis ◽  
Intelligence Analysis ◽  
Mantle Cell ◽  
Pan Cancer

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.

scholarly journals Glyoxal Damages Human Aortic Endothelial Cells by Perturbing the Glutathione, Mitochondrial Membrane Potential, and Mitogen-Activated Protein Kinase Pathways

2021 ◽  
Author(s):  
Ming-Zhang Xie ◽  
Chun Guo ◽  
Jia-Qi Dong. BA ◽  
Jie Zhang ◽  
Ke-Tao Sun ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Membrane Potential ◽  
Protein Kinase ◽  
Mitochondrial Membrane Potential ◽  
Mitochondrial Membrane ◽  
Mitogen Activated Protein Kinase ◽  
Aortic Endothelial Cells ◽  
Mitogen Activated Protein ◽  
Human Aortic Endothelial Cells ◽  
Quantitative Fluorescence

Abstract Background: Exposure to glyoxal, the smallest dialdehyde, is associated with several diseases; humans are routinely exposed to glyoxal because of its ubiquitous presence in foods and the environment. The aim of this study was to examine the damage caused by glyoxal in human aortic endothelial cells. Methods: Cell survival assays and quantitative fluorescence assays were performed to measure DNA damage; oxidative stress was detected by colorimetric assays and quantitative fluorescence, and the mitogen-activated protein kinase pathways were assessed using western blotting. Results: Exposure to glyoxal was found to be linked to abnormal glutathione activity, the collapse of mitochondrial membrane potential, and the activation of mitogen-activated protein kinase pathways. However, DNA damage and thioredoxin oxidation were not induced by dialdehydes. Conclusions: Intracellular glutathione, members of the mitogen-activated protein kinase pathways, and the mitochondrial membrane potential are all critical targets of glyoxal. These findings provide novel insights into the molecular mechanisms perturbed by glyoxal and may facilitate the development of new therapeutics and diagnostic markers for cardiovascular diseases.

scholarly journals Advanced Deep Learning Model

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Learning Model ◽  
Intelligence Analysis ◽  
Basic Parameters ◽  
Deep Learning Model ◽  
Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

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