Evaluating chemical effects on human neural cells through calcium imaging and deep learning
Summary: New substances intended for human consumption must undergo extensive preclinical safety pharmacology testing prior to approval. These tests encompass the evaluation of effects on the central nervous system, which is highly sensitive to chemical substances. With the growing understanding of...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224025239 |
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| author | Ray Yueh Ku Ankush Bansal Dipankar J. Dutta Satoshi Yamashita John Peloquin Diana N. Vu Yubing Shen Tomoki Uchida Masaaki Torii Kazue Hashimoto-Torii |
| author_facet | Ray Yueh Ku Ankush Bansal Dipankar J. Dutta Satoshi Yamashita John Peloquin Diana N. Vu Yubing Shen Tomoki Uchida Masaaki Torii Kazue Hashimoto-Torii |
| author_sort | Ray Yueh Ku |
| collection | DOAJ |
| description | Summary: New substances intended for human consumption must undergo extensive preclinical safety pharmacology testing prior to approval. These tests encompass the evaluation of effects on the central nervous system, which is highly sensitive to chemical substances. With the growing understanding of the species-specific characteristics of human neural cells and advancements in machine learning technology, the development of effective and efficient methods for the initial screening of chemical effects on human neural function using machine learning platforms is anticipated. In this study, we employed a deep learning model to analyze calcium dynamics in human-induced pluripotent stem cell-derived neural progenitor cells, which were exposed to various concentrations of four representative chemicals. We report that this approach offers a reliable and concise method for quantitatively classifying the effects of chemical exposures and predicting potential harm to human neural cells. |
| format | Article |
| id | doaj-art-a2f901ad3ae549c4b11b5a1c3f7378b7 |
| institution | Kabale University |
| issn | 2589-0042 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-a2f901ad3ae549c4b11b5a1c3f7378b72024-12-22T05:28:50ZengElsevieriScience2589-00422024-12-012712111298Evaluating chemical effects on human neural cells through calcium imaging and deep learningRay Yueh Ku0Ankush Bansal1Dipankar J. Dutta2Satoshi Yamashita3John Peloquin4Diana N. Vu5Yubing Shen6Tomoki Uchida7Masaaki Torii8Kazue Hashimoto-Torii9Center for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USACenter for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA; Corresponding authorCenter for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USACenter for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USACenter for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USACenter for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USACenter for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USANovel Business Development Department, Suntory Global Innovation Center Limited, 8-1-1 Seikadai, Seika-cho, Soraku-gun, Kyoto 619-0284, JapanCenter for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA; Department of Pediatrics, Pharmacology and Physiology, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20052, USA; Corresponding authorCenter for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA; Department of Pediatrics, Pharmacology and Physiology, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20052, USA; Corresponding authorSummary: New substances intended for human consumption must undergo extensive preclinical safety pharmacology testing prior to approval. These tests encompass the evaluation of effects on the central nervous system, which is highly sensitive to chemical substances. With the growing understanding of the species-specific characteristics of human neural cells and advancements in machine learning technology, the development of effective and efficient methods for the initial screening of chemical effects on human neural function using machine learning platforms is anticipated. In this study, we employed a deep learning model to analyze calcium dynamics in human-induced pluripotent stem cell-derived neural progenitor cells, which were exposed to various concentrations of four representative chemicals. We report that this approach offers a reliable and concise method for quantitatively classifying the effects of chemical exposures and predicting potential harm to human neural cells.http://www.sciencedirect.com/science/article/pii/S2589004224025239Biological sciencesNeuroscienceMachine learning |
| spellingShingle | Ray Yueh Ku Ankush Bansal Dipankar J. Dutta Satoshi Yamashita John Peloquin Diana N. Vu Yubing Shen Tomoki Uchida Masaaki Torii Kazue Hashimoto-Torii Evaluating chemical effects on human neural cells through calcium imaging and deep learning iScience Biological sciences Neuroscience Machine learning |
| title | Evaluating chemical effects on human neural cells through calcium imaging and deep learning |
| title_full | Evaluating chemical effects on human neural cells through calcium imaging and deep learning |
| title_fullStr | Evaluating chemical effects on human neural cells through calcium imaging and deep learning |
| title_full_unstemmed | Evaluating chemical effects on human neural cells through calcium imaging and deep learning |
| title_short | Evaluating chemical effects on human neural cells through calcium imaging and deep learning |
| title_sort | evaluating chemical effects on human neural cells through calcium imaging and deep learning |
| topic | Biological sciences Neuroscience Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589004224025239 |
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