Predicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs.

scientific article published on 16 June 2016

Predicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs. is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1080/17435390.2016.1202352
P698PubMed publication ID27309010

P50authorRong JiQ42717128
Chengfang PangQ57069752
Danail HristozovQ57231876
John P. GiesyQ63214976
Baoshan XingQ69025069
Qing ZhaoQ83746668
P2093author name stringYue Zhou
Yan Hu
Fengchang Wu
Yunsong Mu
Yu Qie
P2860cites workMethods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARsQ24815757
Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammationQ28392757
Copper Oxide Nanoparticles Are Highly Toxic: A Comparison between Metal Oxide Nanoparticles and Carbon NanotubesQ28651579
Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticlesQ30722652
Rational selection of training and test sets for the development of validated QSAR modelsQ31165909
Physiological effects of nanoparticles on fish: a comparison of nanometals versus metal ionsQ33865461
Toxicity of ZnO nanoparticles to macrophages due to cell uptake and intracellular release of zinc ionsQ34474786
What can be inferred from bacterium-nanoparticle interactions about the potential consequences of environmental exposure to nanoparticles?Q37153629
How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR).Q37526685
Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential.Q38060983
Nanotoxicity: oxidative stress mediated toxicity of metal and metal oxide nanoparticlesQ38204438
Trojan-horse mechanism in the cellular uptake of silver nanoparticles verified by direct intra- and extracellular silver speciation analysis.Q38908383
Size-dependent cytotoxicity of silver nanoparticles in human lung cells: the role of cellular uptake, agglomeration and Ag releaseQ39023812
Modeling biological activities of nanoparticlesQ39265233
Predicting metal toxicity revisited: general properties vs. specific effectsQ40675648
Interactions between ultrafine particles and transition metals in vivo and in vitroQ40686152
Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticlesQ43415971
Size-dependent proinflammatory effects of ultrafine polystyrene particles: a role for surface area and oxidative stress in the enhanced activity of ultrafinesQ43738911
Silver nanoparticle exposure attenuates the viability of rat cerebellum granule cells through apoptosis coupled to oxidative stressQ44442569
Predicting water quality criteria for protecting aquatic life from physicochemical properties of metals or metalloidsQ44590622
In vitro assessment of Ag2O nanoparticles toxicity against Gram-positive and Gram-negative bacteria.Q45926532
A predictive toxicological paradigm for the safety assessment of nanomaterials.Q46026731
Nanosilver toxicity: ions, nanoparticles--or both?Q46133852
In vitro evaluation of cytotoxicity of engineered metal oxide nanoparticlesQ46235956
Derivation of marine water quality criteria for metals based on a novel QICAR-SSD modelQ46828475
Predicting criteria continuous concentrations of 34 metals or metalloids by use of quantitative ion character-activity relationships-species sensitivity distributions (QICAR-SSD) modelQ46929791
Determination of the mechanism of photoinduced toxicity of selected metal oxide nanoparticles (ZnO, CuO, Co3O4 and TiO2) to E. coli bacteriaQ46972778
Where does the toxicity of metal oxide nanoparticles come from: The nanoparticles, the ions, or a combination of both?Q51533298
The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR ModelsQ56432345
Focusing the research effortsQ56834568
Statistical variation in progressive scramblingQ57907612
Where Are We Heading in Nanotechnology Environmental Health and Safety and Materials Characterization?Q58467116
P433issue9
P921main subjectnanoparticleQ61231
metal poisoningQ4215775
P304page(s)1207-1214
P577publication date2016-07-11
P1433published inNanotoxicologyQ1964708
P1476titlePredicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs
P478volume10

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cites work (P2860)
Q38600120A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials.
Q90995714An assessment of applicability of existing approaches to predicting the bioaccumulation of conventional substances in nanomaterials
Q38658065Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials
Q47282095Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme
Q38644401ENVIRONMENTAL BEHAVIOR, POTENTIAL PHYTOTOXICITY, AND ACCUMULATION OF COPPER OXIDE NANOPARTICLES AND ARSENIC TO RICE PLANTS.
Q91966152Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials
Q52327090Nanomaterials in the Environment: Behavior, Fate, Bioavailability, and Effects-An Updated Review.
Q92632878Practices and Trends of Machine Learning Application in Nanotoxicology
Q38668201Regulation of engineered nanomaterials: current challenges, insights and future directions
Q49306400Toxicological Implications of Released Particulate Matter during Thermal Decomposition of Nano-Enabled Thermoplastics.
Q36399257What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps

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