Evidence map›Paper›PMID 31933000›Full record

ArticleAnnals of biomedical engineering2020

An Integrated In Vitro-In Silico Approach for Silver Nanoparticle Dosimetry in Cell Cultures.

Daniele Poli, Giorgio Mattei, Nadia Ucciferri, Arti Ahluwalia

Erratum issuedOpen access · hybridAbstract read
In one paragraph

Article in Annals of biomedical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.9field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 13 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Daniele PoliResearch Center E. Piaggio, University of Pisa, Pisa, Italy.
Giorgio MatteiDepartment of Information Engineering, University of Pisa, Pisa, Italy.
Nadia UcciferriResearch Center E. Piaggio, University of Pisa, Pisa, Italy.
Arti AhluwaliaResearch Center E. Piaggio, University of Pisa, Pisa, Italy. arti.ahluwalia@unipi.it.ORCID http://orcid.org/0000-0001-5370-6750
University of Pisa · IT

Funding

Horizon 2020 760813
6 · The paper itself

Abstract

Potential human and environmental hazards resulting from the exposure of living organisms to silver nanoparticles (Ag NPs) have been the subject of intensive discussion in the last decade. Despite the growing use of Ag NPs in biomedical applications, a quantification of the toxic effects as a function of the total silver mass reaching cells (namely, target cell dose) is still needed. To provide a more accurate dose-response analysis, we propose a novel integrated approach combining well-established computational and experimental methodologies. We first used a particokinetic model (ISD3) for providing experimental validation of computed Ag NP sedimentation in static-cuvette experiments. After validation, ISD3 was employed to predict the total mass of silver reaching human endothelial cells and hepatocytes cultured in 96 well plates. Cell viability measured after 24 h of culture was then related to this target cell dose. Our results show that the dose perceived by the cell monolayer after 24 h of exposure is around 85% lower than the administered nominal media concentration. Therefore, accurate dosimetry considering particle characteristics and experimental conditions (e.g., time, size and shape of wells) should be employed for better interpreting effects induced by the amount of silver reaching cells.

Indexed as

Models, BiologicalCells, CulturedCell SurvivalComputer SimulationDose-Response Relationship, DrugHepatocytesHumansHuman Umbilical Vein Endothelial CellsMetal NanoparticlesSilverSilverAg nanoparticlesDiffusionDissolutionParticokinetic modelSedimentation

Identifiers

PMID31933000
PMCPMC7089903
OpenAlexW3000089188

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.