A Deep Learning Analysis Reveals Nitrogen-Doped Graphene Quantum Dots Damage Neurons of Nematode <i<Caenorhabditis elegans</i<

Along with the rapidly increasing applications of nitrogen-doped graphene quantum dots (N-GQDs) in the field of biomedicine, the exposure of N-GQDs undoubtedly pose a risk to the health of human beings, especially in the nervous system. In view of the lack of data from in vivo studies, this study us...
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Gespeichert in:

Autor*in:	Hongsheng Xu [verfasserIn] Xinyu Wang [verfasserIn] Xiaomeng Zhang [verfasserIn] Jin Cheng [verfasserIn] Jixiang Zhang [verfasserIn] Min Chen [verfasserIn] Tianshu Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	nanotoxicity neurotoxicity neurobehavior machine learning phenotyping

Übergeordnetes Werk:	In: Nanomaterials - MDPI AG, 2012, 11(2021), 12, p 3314
Übergeordnetes Werk:	volume:11 ; year:2021 ; number:12, p 3314

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/nano11123314

Katalog-ID:	DOAJ003366464

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callnumber-first	Q - Science
author	Hongsheng Xu
spellingShingle	Hongsheng Xu misc QD1-999 misc nanotoxicity misc neurotoxicity misc neurobehavior misc machine learning misc phenotyping misc Chemistry A Deep Learning Analysis Reveals Nitrogen-Doped Graphene Quantum Dots Damage Neurons of Nematode <i<Caenorhabditis elegans</i<
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topic_title	QD1-999 A Deep Learning Analysis Reveals Nitrogen-Doped Graphene Quantum Dots Damage Neurons of Nematode <i<Caenorhabditis elegans</i< nanotoxicity neurotoxicity neurobehavior machine learning phenotyping
topic	misc QD1-999 misc nanotoxicity misc neurotoxicity misc neurobehavior misc machine learning misc phenotyping misc Chemistry
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title	A Deep Learning Analysis Reveals Nitrogen-Doped Graphene Quantum Dots Damage Neurons of Nematode <i<Caenorhabditis elegans</i<
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title_full	A Deep Learning Analysis Reveals Nitrogen-Doped Graphene Quantum Dots Damage Neurons of Nematode <i<Caenorhabditis elegans</i<
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author_browse	Hongsheng Xu Xinyu Wang Xiaomeng Zhang Jin Cheng Jixiang Zhang Min Chen Tianshu Wu
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title_sort	deep learning analysis reveals nitrogen-doped graphene quantum dots damage neurons of nematode <i<caenorhabditis elegans</i<
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title_auth	A Deep Learning Analysis Reveals Nitrogen-Doped Graphene Quantum Dots Damage Neurons of Nematode <i<Caenorhabditis elegans</i<
abstract	Along with the rapidly increasing applications of nitrogen-doped graphene quantum dots (N-GQDs) in the field of biomedicine, the exposure of N-GQDs undoubtedly pose a risk to the health of human beings, especially in the nervous system. In view of the lack of data from in vivo studies, this study used the nematode <i<Caenorhabditis elegans</i< (<i<C. elegans</i<), which has become a valuable animal model in nanotoxicological studies due to its multiple advantages, to undertake a bio-safety assessment of N-GQDs in the nervous system with the assistance of a deep learning model. The findings suggested that accumulated N-GQDs in the nematodes’ bodies damaged their normal behavior in a dose- and time-dependent manner, and the impairments of the nervous system were obviously severe when the exposure dosages were above 100 μg/mL. When assessing the morphological changes of neurons caused by N-GQDs, a quantitative image-based analysis based on a deep neural network algorithm (YOLACT) was used because traditional image-based analysis is labor-intensive and limited to qualitative evaluation. The quantitative results indicated that N-GQDs damaged dopaminergic and glutamatergic neurons, which are involved in the neurotoxic effects of N-GQDs in the nematode <i<C. elegans</i<. This study not only suggests a fast and economic <i<C. elegans</i< model to undertake the risk assessment of nanomaterials in the nervous system, but also provides a valuable deep learning approach to quantitatively track subtle morphological changes of neurons at an unbiased level in a nanotoxicological study using <i<C. elegans</i<.
abstractGer	Along with the rapidly increasing applications of nitrogen-doped graphene quantum dots (N-GQDs) in the field of biomedicine, the exposure of N-GQDs undoubtedly pose a risk to the health of human beings, especially in the nervous system. In view of the lack of data from in vivo studies, this study used the nematode <i<Caenorhabditis elegans</i< (<i<C. elegans</i<), which has become a valuable animal model in nanotoxicological studies due to its multiple advantages, to undertake a bio-safety assessment of N-GQDs in the nervous system with the assistance of a deep learning model. The findings suggested that accumulated N-GQDs in the nematodes’ bodies damaged their normal behavior in a dose- and time-dependent manner, and the impairments of the nervous system were obviously severe when the exposure dosages were above 100 μg/mL. When assessing the morphological changes of neurons caused by N-GQDs, a quantitative image-based analysis based on a deep neural network algorithm (YOLACT) was used because traditional image-based analysis is labor-intensive and limited to qualitative evaluation. The quantitative results indicated that N-GQDs damaged dopaminergic and glutamatergic neurons, which are involved in the neurotoxic effects of N-GQDs in the nematode <i<C. elegans</i<. This study not only suggests a fast and economic <i<C. elegans</i< model to undertake the risk assessment of nanomaterials in the nervous system, but also provides a valuable deep learning approach to quantitatively track subtle morphological changes of neurons at an unbiased level in a nanotoxicological study using <i<C. elegans</i<.
abstract_unstemmed	Along with the rapidly increasing applications of nitrogen-doped graphene quantum dots (N-GQDs) in the field of biomedicine, the exposure of N-GQDs undoubtedly pose a risk to the health of human beings, especially in the nervous system. In view of the lack of data from in vivo studies, this study used the nematode <i<Caenorhabditis elegans</i< (<i<C. elegans</i<), which has become a valuable animal model in nanotoxicological studies due to its multiple advantages, to undertake a bio-safety assessment of N-GQDs in the nervous system with the assistance of a deep learning model. The findings suggested that accumulated N-GQDs in the nematodes’ bodies damaged their normal behavior in a dose- and time-dependent manner, and the impairments of the nervous system were obviously severe when the exposure dosages were above 100 μg/mL. When assessing the morphological changes of neurons caused by N-GQDs, a quantitative image-based analysis based on a deep neural network algorithm (YOLACT) was used because traditional image-based analysis is labor-intensive and limited to qualitative evaluation. The quantitative results indicated that N-GQDs damaged dopaminergic and glutamatergic neurons, which are involved in the neurotoxic effects of N-GQDs in the nematode <i<C. elegans</i<. This study not only suggests a fast and economic <i<C. elegans</i< model to undertake the risk assessment of nanomaterials in the nervous system, but also provides a valuable deep learning approach to quantitatively track subtle morphological changes of neurons at an unbiased level in a nanotoxicological study using <i<C. elegans</i<.
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title_short	A Deep Learning Analysis Reveals Nitrogen-Doped Graphene Quantum Dots Damage Neurons of Nematode <i<Caenorhabditis elegans</i<
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A Deep Learning Analysis Reveals Nitrogen-Doped Graphene Quantum Dots Damage Neurons of Nematode <i<Caenorhabditis elegans</i<

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