You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)
<script type="application/ld+json">
{
"@context": {
"bs": "https://bioschemas.org/",
"schema": "https://schema.org/",
"citation": "schema:citation",
"name": "schema:name",
"url": "schema:url",
"variableMeasured": "schema:variableMeasured"
},
"@type": "schema:Dataset",
"variableMeasured": [
{
"@type": "schema:PropertyValue",
"name": "composition"
},
{
"@type": "schema:PropertyValue",
"name": "size"
},
{
"@type": "schema:PropertyValue",
"name": "surface charge"
},
{
"@type": "schema:PropertyValue",
"name": "surface area"
},
{
"@type": "schema:PropertyValue",
"name": "assay"
},
{
"@type": "schema:PropertyValue",
"name": "cell type"
},
{
"@type": "schema:PropertyValue",
"name": "expose time"
},
{
"@type": "schema:PropertyValue",
"name": "dose"
},
{
"@type": "schema:PropertyValue",
"name": "toxicological endpoint"
},
{
"@type": "schema:PropertyValue",
"name": "size distribution"
}
],
"name": "RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)",
"schema:description": "This is an RDFied version of the dataset published in Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. Nanomaterials 2020, 10, 2017. The original dataset publication DOI: https://doi.org/10.3390/nano10102017. The Original publication authors: Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A.",
"@id": "https://zenodo.org/record/5743788",
"url": "https://zenodo.org/record/5743788",
"citation": "https://zenodo.org/record/5743788",
"http://purl.org/dc/terms/conformsTo": { "@type": "schema:CreativeWork", "@id": "https://bioschemas.org/profiles/Dataset/0.4-DRAFT" },
"schema:identifier": "10.5281/zenodo.5743788",
"schema:license": "https://creativecommons.org/licenses/by/4.0/legalcode",
"schema:creator": [
{
"@type": "schema:Organization",
"name": "NanoSolveIT"
}
],
"schema:datePublished": "2021-11-30"
}
</script>
A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⊥ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project’s Integrated Approach to Testing and Assessment (IATA). [Source: https://doi.org/10.3390/nano10102017]
Data Sample
Material_type
Core_size
Method_core_size
Hydro_size
Method_hydro_size
Surface_charge
Method_surface_charge
Surface_area
Method_surface_area
Hsf
Ec
Ev
MeO
Assay
Cell_name
Cell_species
Cell_origin
Cell_type
Exposure_time
Exposure_dose
log_n_atoms_all
log_n_atoms_core
log_n_atoms_shell
log_n_Al _atoms_all
log_n_Al _atoms_core
log_n_Al _atoms_shell
log_n_O_atoms_all
log_n_O_atoms_core
log_n_O_atoms_shell
peng_avg_all
peng_avg_core
peng_avg_shell
Al _peng_avg_all
Al _peng_avg_core
Al _peng_avg_shell
O_peng_avg_all
O_peng_avg_core
O_peng_avg_shell
coordN_avg_all
coordN_avg_core
coordN_avg_shell
Al _coordN_avg_all
Al _coordN_avg_core
Al _coordN_avg_shell
O_coordN_avg_all
O_coordN_avg_core
O_coordN_avg_shell
NP_diameter
NP_surface_area
NP_volume
Lattice_energy_of_NP
E_L_bulk-E_L_NP
Lattice_energy_of_NP_d_NP
Lattice_energy_of_NP_S_NP
Lattice_energy_of_NP_V_NP
force_vector_length_avg_all
force_vector_length_avg_core
force_vector_length_avg_shell
Al _force_vector_length_coordN_avg_all
Al _force_vector_length_coordN_avg_core
Al _force_vector_length_coordN_avg_shell
O_force_vector_length_avg_all
O_force_vector_length_avg_core
O_force_vector_length_avg_shell
force_vector_surface_normal_component_avg_all
force_vector_surface_normal_component_avg_core
force_vector_surface_normal_component_avg_shell
Al _force_vector_surface_normal_component_coordN_avg_all
Al _force_vector_surface_normal_component_coordN_avg_core
Al _force_vector_surface_normal_component_coordN_avg_shell
O_force_vector_surface_normal_component_avg_all
O_force_vector_surface_normal_component_avg_core
O_force_vector_surface_normal_component_avg_shell
force_vector_surface_tangent_component_avg_all
force_vector_surface_tangent_component_avg_core
force_vector_surface_tangent_component_avg_shell
Al _force_vector_surface_tangent_component_coordN_avg_all
Al _force_vector_surface_tangent_component_coordN_avg_core
Al _force_vector_surface_tangent_component_coordN_avg_shell