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APDB: a database on air pollutant characterization and similarity prediction
The World Health Organization estimates that 9 out of 10 people worldwide breathe air containing high levels of pollutants. Long-term and chronic exposure to high concentrations of air pollutants is associated with deleterious effects on vital organs, including increased inflammation in the lungs, o...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348400/ https://www.ncbi.nlm.nih.gov/pubmed/37450416 http://dx.doi.org/10.1093/database/baad046 |
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author | Viesi, Eva Sardina, Davide Stefano Perricone, Ugo Giugno, Rosalba |
author_facet | Viesi, Eva Sardina, Davide Stefano Perricone, Ugo Giugno, Rosalba |
author_sort | Viesi, Eva |
collection | PubMed |
description | The World Health Organization estimates that 9 out of 10 people worldwide breathe air containing high levels of pollutants. Long-term and chronic exposure to high concentrations of air pollutants is associated with deleterious effects on vital organs, including increased inflammation in the lungs, oxidative stress in the heart and disruption of the blood–brain barrier. For this reason, in an effort to find an association between exposure to pollutants and the toxicological effects observable on human health, an online resource collecting and characterizing in detail pollutant molecules could be helpful to investigate their properties and mechanisms of action. We developed a database, APDB, collecting air-pollutant-related data from different online resources, in particular, molecules from the US Environmental Protection Agency, their associated targets and bioassays found in the PubChem chemical repository and their computed molecular descriptors and quantum mechanics properties. A web interface allows (i) to browse data by category, (ii) to navigate the database by querying molecules and targets and (iii) to visualize and download molecule and target structures as well as computed descriptors and similarities. The desired data can be freely exported in textual/tabular format and the whole database in SQL format. Database URL http://apdb.di.univr.it |
format | Online Article Text |
id | pubmed-10348400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103484002023-07-15 APDB: a database on air pollutant characterization and similarity prediction Viesi, Eva Sardina, Davide Stefano Perricone, Ugo Giugno, Rosalba Database (Oxford) Original Article The World Health Organization estimates that 9 out of 10 people worldwide breathe air containing high levels of pollutants. Long-term and chronic exposure to high concentrations of air pollutants is associated with deleterious effects on vital organs, including increased inflammation in the lungs, oxidative stress in the heart and disruption of the blood–brain barrier. For this reason, in an effort to find an association between exposure to pollutants and the toxicological effects observable on human health, an online resource collecting and characterizing in detail pollutant molecules could be helpful to investigate their properties and mechanisms of action. We developed a database, APDB, collecting air-pollutant-related data from different online resources, in particular, molecules from the US Environmental Protection Agency, their associated targets and bioassays found in the PubChem chemical repository and their computed molecular descriptors and quantum mechanics properties. A web interface allows (i) to browse data by category, (ii) to navigate the database by querying molecules and targets and (iii) to visualize and download molecule and target structures as well as computed descriptors and similarities. The desired data can be freely exported in textual/tabular format and the whole database in SQL format. Database URL http://apdb.di.univr.it Oxford University Press 2023-07-14 /pmc/articles/PMC10348400/ /pubmed/37450416 http://dx.doi.org/10.1093/database/baad046 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Viesi, Eva Sardina, Davide Stefano Perricone, Ugo Giugno, Rosalba APDB: a database on air pollutant characterization and similarity prediction |
title | APDB: a database on air pollutant characterization and similarity prediction |
title_full | APDB: a database on air pollutant characterization and similarity prediction |
title_fullStr | APDB: a database on air pollutant characterization and similarity prediction |
title_full_unstemmed | APDB: a database on air pollutant characterization and similarity prediction |
title_short | APDB: a database on air pollutant characterization and similarity prediction |
title_sort | apdb: a database on air pollutant characterization and similarity prediction |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348400/ https://www.ncbi.nlm.nih.gov/pubmed/37450416 http://dx.doi.org/10.1093/database/baad046 |
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