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A Meta-Analysis of Carbon Nanotube Pulmonary Toxicity Studies-How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes

Jeremy M. Gernand, Elizabeth A. Casman
2013 Risk Analysis  
ABSTRACT This paper presents a regression-tree-based meta-analysis of rodent pulmonary toxicity studies of uncoated, non-functionalized carbon nanotube (CNT) exposure.  ...  It was found that the CNT attributes that contribute the most to pulmonary toxicity were metallic impurities, CNT length and diameter, and aggregate size.  ...  ACKNOWLEDGEMENTS This work has not been subjected to EPA review and no official endorsement should be inferred.  ... 
doi:10.1111/risa.12109 pmid:24024907 fatcat:i7c7zx5pt5frbdieamw5in454e

Carbon Nanotubes' Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra

Michael González-Durruthy, Jose Monserrat, Bakhtiyor Rasulev, Gerardo Casañola-Martín, José Barreiro Sorrivas, Sergio Paraíso-Medina, Víctor Maojo, Humberto González-Díaz, Alejandro Pazos, Cristian Munteanu
2017 Nanomaterials  
This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (J m ) under three experimental conditions.  ...  The best model for the prediction of J m for other CNTs was provided by random forest using eight features, obtaining test R-squared (R 2 ) of 0.863 and test root-mean-square error (RMSE) of 0.0461.  ...  CNTtypes are multi-walled carbon nanotubes (MWCNT), mixed single walled/double-walled carbon nanotubes (SW+DWCNT), and single-walled carbon nanotube (SWCNT).  ... 
doi:10.3390/nano7110386 pmid:29137126 pmcid:PMC5707603 fatcat:sco224gkq5gdna4sblx6jkp4ca

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Georgios Konstantopoulos, Elias P. Koumoulos, Costas A. Charitidis
2022 Nanomaterials  
Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector.  ...  The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge  ...  resolution of the characterization technique and not by the machine learning model ability to identify and localize features.  ... 
doi:10.3390/nano12152646 pmid:35957077 pmcid:PMC9370746 fatcat:hbluqbnexffnjkqu6b4lzpqwiq

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

Buglak, Zherdev, Dzantiev
2019 Molecules  
We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles.  ...  The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests.  ...  Multi-Walled Carbon Nanotubes (MWCNTs) Certain MWCNTs display asbestos-like toxic effects.  ... 
doi:10.3390/molecules24244537 pmid:31835808 pmcid:PMC6943593 fatcat:zmgil6dbyne65gxxvqq4da2stq

Current situation on the availability of nanostructure–biological activity data

C. Oksel, C.Y. Ma, X.Z. Wang
2015 SAR and QSAR in environmental research (Print)  
Therefore, it is desirable to use time-effective computational methods, such as the quantitative structure-activity relationship (QSAR) models, in order to predict the toxicity of ENMs.  ...  As the production and use of ENMs are increasing, we are approaching the point at which it is impossible to individually assess the toxicity of a vast number of ENMs.  ...  [35] have been assessed below to find out their suitability for developing nano-(Q)SAR models:  Material group: The data are associated with multi-walled carbon nanotubes.  ... 
doi:10.1080/1062936x.2014.993702 pmid:25608859 fatcat:beuddwz525bi5oaph3gw6fyslm

Cancer Targeting and Diagnosis: Recent Trends with Carbon Nanotubes

Ragini Singh, Santosh Kumar
2022 Nanomaterials  
In this context, carbon nanotubes (CNTs) have recently garnered a great deal of interest in the field of cancer diagnosis and treatment due to various factors such as biocompatibility, thermodynamic properties  ...  In the next section, therapy techniques like photothermal therapy, photodynamic therapy, drug targeting, gene therapy, and immunotherapy are also explained in-depth.  ...  Machine learning also helps to explain the thermionic and vibrational properties of CNTs by correlating the number of iterations and the detection of defects in carbon nanotubes.  ... 
doi:10.3390/nano12132283 pmid:35808119 pmcid:PMC9268713 fatcat:damg57bvjjbn3i3uxs7w3lxzwa

Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes

Abhishek S. Dhoble, Pratik Lahiri, Kaustubh D. Bhalerao
2018 Journal of Biological Engineering  
A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers  ...  Conclusion: Machine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes  ...  Stablein and other team members for their valuable insights and contributions in carrying out the proposed work.  ... 
doi:10.1186/s13036-018-0112-9 pmid:30220912 pmcid:PMC6134764 fatcat:72jywqwbzrcmlkyqtos2cmqxcm

NanoEHS beyond toxicity – focusing on biocorona

Sijie Lin, Monika Mortimer, Ran Chen, Aleksandr Kakinen, Jim E. Riviere, Thomas P. Davis, Feng Ding, Pu Chun Ke
2017 Environmental Science: Nano  
We believe continued development of the field of environmental health and safety of nanomaterials (nanoEHS) hinges on a critical extension from reporting macroscopic and microscopic phenomena to understanding  ...  Ag, CuO and ZnO NPs, carbon nanotubes and graphene derivatives) bound to the bacterial cell wall or taken up by bacterial cells. 21, 22 Further development in this arena could exploit interactions of  ...  Compared to total NOM, EPS have been shown to improve the stability of NPs to a greater extent, including reduced aggregation of single-wall carbon nanotubes (SWCNTs) 79 and copper-based NPs. 80 Improved  ... 
doi:10.1039/c6en00579a pmid:29123668 pmcid:PMC5673284 fatcat:47pifnwhbfhalfjfqz23pdbkia

New Mechanistic Insights on Carbon Nanotubes' Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches

Michael González-Durruthy, Riccardo Concu, Juan M. Ruso, M. Natália D. S. Cordeiro
2021 Biology  
Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition.  ...  In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes  ...  Several approaches by many authors have been reported combining different molecular descriptors, methodologies, and algorithms, including machine learning and deep learning [34] [35] [36] [37] [38] [39  ... 
doi:10.3390/biology10030171 pmid:33668702 fatcat:dikb2np5wzcezfc7upykfqf3ia

Review of Underground Storage Tank Condition Monitoring Techniques

Ooi Ching Sheng, Wai Keng Ngui, Hui Kar Hoou, Lim Meng Hee, Mohd. Salman Leong, Lim Meng Hee
2019 MATEC Web of Conferences  
As an alternative means to deliver spatial information on structural integrity, the feasibility of integrating nondestructive evaluation (NDE) techniques with machine learning algorithms, on observing  ...  Generally, the UST has long been a favourite toxic substance reservation apparatus, thanks to its large capacity and minimum floor space requirement.  ...  The viability of a product life cycle prediction and fault diagnosis model based on NDE techniques and machine learning algorithms will be tabulated in the Recommendations section.  ... 
doi:10.1051/matecconf/201925502009 fatcat:ylv2tc24sfdktcj3zcge3t573m

Review article: Polymer-matrix Nanocomposites, Processing, Manufacturing, and Application: An Overview

Farzana Hussain, Mehdi Hojjati, Masami Okamoto, Russell E. Gorga
2006 Journal of composite materials  
Hence, this review offers a comprehensive discussion on technology, modeling, characterization, processing, manufacturing, applications, and health/safety concerns for polymer nanocomposites.  ...  This review is designed to be a comprehensive source for polymer nanocomposite research, including fundamental structure/property relationships, manufacturing techniques, and applications of polymer nanocomposite  ...  She would like to extend her gratitude to the publisher for their sincere cooperation and suggestions.  ... 
doi:10.1177/0021998306067321 fatcat:furybxgp7bh7xomhrwsg5zimti

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Iseult Lynch, Antreas Afantitis, Thomas Exner, Martin Himly, Vladimir Lobaskin, Philip Doganis, Dieter Maier, Natasha Sanabria, Anastasios G. Papadiamantis, Anna Rybinska-Fryca, Maciej Gromelski, Tomasz Puzyn (+18 others)
2020 Nanomaterials  
The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications.  ...  Chemical Identifier (InChI), which are machine-readable.  ...  Acknowledgments: The authors thank Cristiana Gheorghe for her support in facilitating the workshop in Iceland and the follow-up online drafting sessions to progress the manuscript.  ... 
doi:10.3390/nano10122493 pmid:33322568 pmcid:PMC7764592 fatcat:pxibwqk64ze25bqgpwc6zu6egi

Advancing risk assessment of engineered nanomaterials using deep learning approach

Timothy Oladele Odedele, Hussaini Doko Ibrahim
2022 World Journal of Advanced Engineering Technology and Sciences  
This paper, therefore, focuses on the capability of deep learning techniques to model physicochemical properties and toxic effects of nanomaterials.  ...  In view of these side effects, there is therefore the need to design and develop classification and nanomaterials toxicity predictive models using deep learning intelligent systems.  ...  The Council authorized us to reproduce and distribute reprints for reference purposes notwithstanding any copyright notation thereon.  ... 
doi:10.30574/wjaets.2022.6.1.0073 fatcat:qe6tlb4q7rhovlbru2nowmjxga

Quantitative Nanostructure−Activity Relationship Modeling

Denis Fourches, Dongqiuye Pu, Carlos Tassa, Ralph Weissleder, Stanley Y. Shaw, Russell J. Mumper, Alexander Tropsha
2010 ACS Nano  
We have generated QNAR models using machine learning approaches such as Support Vector Machine (SVM)-based classification and k Nearest Neighbors (kNN)-based regression; their external prediction power  ...  Our results suggest that QNAR models can be employed for: (i) predicting biological activity profiles of novel nanomaterials, and (ii) prioritizing the design and manufacturing of nanomaterials towards  ...  RW and SYS.  ... 
doi:10.1021/nn1013484 pmid:20857979 pmcid:PMC2997621 fatcat:df3knhixcbevjbh7ccifsqyxgi

Using natural language processing techniques to inform research on nanotechnology

Nastassja A Lewinski, Bridget T McInnes
2015 Beilstein Journal of Nanotechnology  
In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment.  ...  Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety.  ...  Supervised machine learning algorithms learn patterns and make predictions based on a set of training data.  ... 
doi:10.3762/bjnano.6.149 pmid:26199848 pmcid:PMC4505089 fatcat:p325xjjyzzaejnwxvys55p6sye
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