Jerzy Leszczynski to Metal Nanoparticles
This is a "connection" page, showing publications Jerzy Leszczynski has written about Metal Nanoparticles.
Connection Strength
6.248
-
Kar S, Pathakoti K, Leszczynska D, Tchounwou PB, Leszczynski J. In vitro and in silico study of mixtures cytotoxicity of metal oxide nanoparticles to Escherichia coli: a mechanistic approach. Nanotoxicology. 2022 06; 16(5):566-579.
Score: 0.698
-
Toropova AP, Toropov AA, Leszczynska D, Leszczynski J. Application of quasi-SMILES to the model of gold-nanoparticles uptake in A549?cells. Comput Biol Med. 2021 09; 136:104720.
Score: 0.645
-
Sizochenko N, Mikolajczyk A, Syzochenko M, Puzyn T, Leszczynski J. Zeta potentials (?) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling. NanoImpact. 2021 04; 22:100317.
Score: 0.632
-
Kar S, Pathakoti K, Tchounwou PB, Leszczynska D, Leszczynski J. Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In?Vitro and In Silico studies. Chemosphere. 2021 Feb; 264(Pt 1):128428.
Score: 0.608
-
Sizochenko N, Syzochenko M, Fjodorova N, Rasulev B, Leszczynski J. Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques. Ecotoxicol Environ Saf. 2019 Dec 15; 185:109733.
Score: 0.567
-
Toropova AP, Toropov AA, Leszczynska D, Leszczynski J. CORAL and Nano-QFAR: Quantitative feature - Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2). Ecotoxicol Environ Saf. 2017 May; 139:404-407.
Score: 0.474
-
Sizochenko N, Rasulev B, Gajewicz A, Kuz'min V, Puzyn T, Leszczynski J. From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles. Nanoscale. 2014 Nov 21; 6(22):13986-93.
Score: 0.405
-
Toropova AP, Toropov AA, Rallo R, Leszczynska D, Leszczynski J. Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicol Environ Saf. 2015 Feb; 112:39-45.
Score: 0.404
-
Gajewicz A, Schaeublin N, Rasulev B, Hussain S, Leszczynska D, Puzyn T, Leszczynski J. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studies. Nanotoxicology. 2015 May; 9(3):313-25.
Score: 0.394
-
Kar S, Gajewicz A, Puzyn T, Roy K, Leszczynski J. Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach. Ecotoxicol Environ Saf. 2014 Sep; 107:162-9.
Score: 0.393
-
Puzyn T, Rasulev B, Gajewicz A, Hu X, Dasari TP, Michalkova A, Hwang HM, Toropov A, Leszczynska D, Leszczynski J. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat Nanotechnol. 2011 Mar; 6(3):175-8.
Score: 0.312
-
Toropova AP, Toropov AA, Leszczynski J, Sizochenko N. Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions. Environ Toxicol Pharmacol. 2021 Aug; 86:103665.
Score: 0.158
-
Golbamaki A, Golbamaki N, Sizochenko N, Rasulev B, Leszczynski J, Benfenati E. Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties. Nanotoxicology. 2018 12; 12(10):1113-1129.
Score: 0.130
-
Kar S, Gajewicz A, Roy K, Leszczynski J, Puzyn T. Extrapolating between toxicity endpoints of metal oxide nanoparticles: Predicting toxicity to Escherichia coli and human keratinocyte cell line (HaCaT) with Nano-QTTR. Ecotoxicol Environ Saf. 2016 Apr; 126:238-244.
Score: 0.110
-
Golbamaki N, Rasulev B, Cassano A, Marchese Robinson RL, Benfenati E, Leszczynski J, Cronin MT. Genotoxicity of metal oxide nanomaterials: review of recent data and discussion of possible mechanisms. Nanoscale. 2015 Feb 14; 7(6):2154-98.
Score: 0.103
-
Gajewicz A, Cronin MT, Rasulev B, Leszczynski J, Puzyn T. Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across. Nanotechnology. 2015 Jan 09; 26(1):015701.
Score: 0.102
-
Gajewicz A, Rasulev B, Dinadayalane TC, Urbaszek P, Puzyn T, Leszczynska D, Leszczynski J. Advancing risk assessment of engineered nanomaterials: application of computational approaches. Adv Drug Deliv Rev. 2012 Dec; 64(15):1663-93.
Score: 0.085
-
Sizochenko N, Gajewicz A, Leszczynski J, Puzyn T. Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models. Nanoscale. 2016 Apr 07; 8(13):7203-8.
Score: 0.028