Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks
Whole organ decellularization techniques have facilitated the fabrication of extracellular matrices (ECMs) for engineering new organs. Unfortunately, there is no objective gold standard evaluation of the scaffold without applying a destructive method such as histological analysis or DNA removal quan...
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MDPI AG
2022-01-01
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| Online Access: | https://www.mdpi.com/2072-666X/13/1/79 |
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| author | Greta Ionela Barbulescu Taddeus Paul Buica Iacob Daniel Goje Florina Maria Bojin Valentin Laurentiu Ordodi Gheorghe Emilian Olteanu Rodica Elena Heredea Virgil Paunescu |
| author_facet | Greta Ionela Barbulescu Taddeus Paul Buica Iacob Daniel Goje Florina Maria Bojin Valentin Laurentiu Ordodi Gheorghe Emilian Olteanu Rodica Elena Heredea Virgil Paunescu |
| author_sort | Greta Ionela Barbulescu |
| collection | DOAJ |
| description | Whole organ decellularization techniques have facilitated the fabrication of extracellular matrices (ECMs) for engineering new organs. Unfortunately, there is no objective gold standard evaluation of the scaffold without applying a destructive method such as histological analysis or DNA removal quantification of the dry tissue. Our proposal is a software application using deep convolutional neural networks (DCNN) to distinguish between different stages of decellularization, determining the exact moment of completion. Hearts from male Sprague Dawley rats (n = 10) were decellularized using 1% sodium dodecyl sulfate (SDS) in a modified Langendorff device in the presence of an alternating rectangular electric field. Spectrophotometric measurements of deoxyribonucleic acid (DNA) and total proteins concentration from the decellularization solution were taken every 30 min. A monitoring system supervised the sessions, collecting a large number of photos saved in corresponding folders. This system aimed to prove a strong correlation between the data gathered by spectrophotometry and the state of the heart that could be visualized with an OpenCV-based spectrometer. A decellularization completion metric was built using a DCNN based classifier model trained using an image set comprising thousands of photos. Optimizing the decellularization process using a machine learning approach launches exponential progress in tissue bioengineering research. |
| first_indexed | 2024-03-10T00:55:13Z |
| format | Article |
| id | doaj.art-e0e1162623db468494ddb42a33814038 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T00:55:13Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-e0e1162623db468494ddb42a338140382023-11-23T14:44:33ZengMDPI AGMicromachines2072-666X2022-01-011317910.3390/mi13010079Optimization of Complete Rat Heart Decellularization Using Artificial Neural NetworksGreta Ionela Barbulescu0Taddeus Paul Buica1Iacob Daniel Goje2Florina Maria Bojin3Valentin Laurentiu Ordodi4Gheorghe Emilian Olteanu5Rodica Elena Heredea6Virgil Paunescu7Immuno-Physiology and Biotechnologies Center (CIFBIOTEH), Department of Functional Sciences, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, RomaniaCenter for Gene and Cellular Therapies in the Treatment of Cancer Timisoara-OncoGen, Clinical Emergency County Hospital “Pius Brinzeu” Timisoara, No. 156 Liviu Rebreanu, 300723 Timisoara, RomaniaDepartment of Medical Semiology I, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, RomaniaImmuno-Physiology and Biotechnologies Center (CIFBIOTEH), Department of Functional Sciences, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, RomaniaCenter for Gene and Cellular Therapies in the Treatment of Cancer Timisoara-OncoGen, Clinical Emergency County Hospital “Pius Brinzeu” Timisoara, No. 156 Liviu Rebreanu, 300723 Timisoara, RomaniaDepartment of Pathology, “Dr Victor Babes” Clinical Hospital of Infectious Disease and Pneumophysiology, 300041 Timisoara, RomaniaDepartment of Clinical Practical Skills, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, RomaniaImmuno-Physiology and Biotechnologies Center (CIFBIOTEH), Department of Functional Sciences, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, RomaniaWhole organ decellularization techniques have facilitated the fabrication of extracellular matrices (ECMs) for engineering new organs. Unfortunately, there is no objective gold standard evaluation of the scaffold without applying a destructive method such as histological analysis or DNA removal quantification of the dry tissue. Our proposal is a software application using deep convolutional neural networks (DCNN) to distinguish between different stages of decellularization, determining the exact moment of completion. Hearts from male Sprague Dawley rats (n = 10) were decellularized using 1% sodium dodecyl sulfate (SDS) in a modified Langendorff device in the presence of an alternating rectangular electric field. Spectrophotometric measurements of deoxyribonucleic acid (DNA) and total proteins concentration from the decellularization solution were taken every 30 min. A monitoring system supervised the sessions, collecting a large number of photos saved in corresponding folders. This system aimed to prove a strong correlation between the data gathered by spectrophotometry and the state of the heart that could be visualized with an OpenCV-based spectrometer. A decellularization completion metric was built using a DCNN based classifier model trained using an image set comprising thousands of photos. Optimizing the decellularization process using a machine learning approach launches exponential progress in tissue bioengineering research.https://www.mdpi.com/2072-666X/13/1/79regenerative medicinetissue engineeringdecellularized extracellular matrixmachine learningdeep convolutional neural networks |
| spellingShingle | Greta Ionela Barbulescu Taddeus Paul Buica Iacob Daniel Goje Florina Maria Bojin Valentin Laurentiu Ordodi Gheorghe Emilian Olteanu Rodica Elena Heredea Virgil Paunescu Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks Micromachines regenerative medicine tissue engineering decellularized extracellular matrix machine learning deep convolutional neural networks |
| title | Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks |
| title_full | Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks |
| title_fullStr | Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks |
| title_full_unstemmed | Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks |
| title_short | Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks |
| title_sort | optimization of complete rat heart decellularization using artificial neural networks |
| topic | regenerative medicine tissue engineering decellularized extracellular matrix machine learning deep convolutional neural networks |
| url | https://www.mdpi.com/2072-666X/13/1/79 |
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