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Quantifying drug-induced structural toxicity in hepatocytes and cardiomyocytes derived from hiPSCs using a deep learning method.
Author: DameKeri, GraftonFrancis, LoewkeKevin, MaddahMahnaz, MandegarMohammad A, RibeiroAlexandre J S
Original Abstract of the Article :
Cardiac and hepatic toxicity result from induced disruption of the functioning of cardiomyocytes and hepatocytes, respectively, which is tightly related to the organization of their subcellular structures. Cellular structure can be analyzed from microscopy imaging data. However, subtle or complex st...See full text at original site
Dr.Camel's Paper Summary Blogラクダ博士について
ラクダ博士は、Health Journal が論文の内容を分かりやすく解説するために作成した架空のキャラクターです。
難解な医学論文を、専門知識のない方にも理解しやすいように、噛み砕いて説明することを目指しています。
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* ラクダ博士は架空のキャラクターであり、実際の医学研究者や医療従事者とは一切関係がありません。
* 解説の内容は Health Journal が独自に解釈・作成したものであり、原論文の著者または出版社の見解を反映するものではありません。
引用元:
https://doi.org/10.1016/j.vascn.2020.106895
データ提供:米国国立医学図書館(NLM)
Quantifying Drug-Induced Structural Toxicity: A Deep Learning Approach
The world of drug development, like a vast desert filled with hidden treasures and potential dangers, is a quest for safe and effective therapies. This study, like a team of scientists using advanced tools to explore a complex ecosystem, focuses on the potential of deep learning to identify drug-induced structural toxicity in human cells. The authors utilize an innovative image-based approach to detect subtle changes in cellular structure caused by drug exposure.
This study, like a prospector discovering a vein of gold in a barren desert, reveals a promising new method for assessing drug safety. The authors developed a deep learning method, PhenoTox, that can analyze microscopic images of cells and identify even subtle structural changes caused by drug exposure. This approach, like a camel caravan equipped with advanced navigation tools, allows for more precise and sensitive assessment of drug toxicity, potentially leading to safer and more effective medications.
The Desert of Drug Development: Navigating Toward Safety
The study's findings, like a compass guiding a caravan through a treacherous desert, highlight the potential of deep learning to revolutionize drug safety testing. By leveraging the power of artificial intelligence, researchers can gain a deeper understanding of how drugs interact with cells and identify potential risks at an early stage. This approach, like a camel's ability to adapt to changing desert conditions, can help us navigate the complex terrain of drug development, ensuring the safety and efficacy of the medications we use.
Dr. Camel's Conclusion
This study, like a shining beacon in the vast desert of drug development, offers a new and powerful tool for assessing drug safety. By incorporating deep learning into our research, we can enhance our ability to identify potential risks and bring safer and more effective medications to those who need them. This advancement, like a wellspring of water in a dry desert, is a testament to the ongoing pursuit of knowledge and innovation in the field of medicine.
Date :
- Date Completed 2021-07-15
- Date Revised 2021-07-15
Further Info :
Related Literature
English
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