Evidence map›Paper›PMID 29029643›Full record

ArticleParticle and fibre toxicology2017

An integrated methodology for assessing the impact of food matrix and gastrointestinal effects on the biokinetics and cellular toxicity of ingested engineered nanomaterials.

Glen M DeLoid, Yanli Wang, Klara Kapronezai, Laura Rubio Lorente, Roujie Zhang, Georgios Pyrgiotakis, Nagarjun V Konduru, Maria Ericsson, Jason C White, Roberto De La Torre-Roche and 3 more

Open access · goldAbstract read
In one paragraph

Article in Particle and fibre toxicology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed
6.4field-weighted citation impact, top 2% 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

71 citing papers in PubMed, 156 citations in OpenAlex.

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  13. The Application of Engineered Nanomaterials in Perinatal Therapeutics.Small (Weinheim an der Bergstrasse, Germany) · 2024
    Review
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11 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 1 country.

Glen M DeLoidCenter for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Research Center, Department of Environmental Health, Harvard School of Public Health, Boston, MA, 02115, USA. gdeloid@hsph.harvard.edu.
Yanli WangCenter for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Research Center, Department of Environmental Health, Harvard School of Public Health, Boston, MA, 02115, USA.
Klara KapronezaiCenter for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Research Center, Department of Environmental Health, Harvard School of Public Health, Boston, MA, 02115, USA.
Laura Rubio LorenteCenter for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Research Center, Department of Environmental Health, Harvard School of Public Health, Boston, MA, 02115, USA.
Roujie ZhangDepartment of Food Science, University of Massachusetts Amherst, Amherst, MA, 01003, USA.
Georgios PyrgiotakisCenter for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Research Center, Department of Environmental Health, Harvard School of Public Health, Boston, MA, 02115, USA.
Nagarjun V KonduruCenter for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Research Center, Department of Environmental Health, Harvard School of Public Health, Boston, MA, 02115, USA.
Maria EricssonDepartment of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA.
Jason C WhiteDepartment of Analytical Chemistry, Connecticut Agricultural Experiment Station, New Haven, CT, 06504, USA.
Roberto De La Torre-RocheDepartment of Analytical Chemistry, Connecticut Agricultural Experiment Station, New Haven, CT, 06504, USA.
Hang XiaoDepartment of Food Science, University of Massachusetts Amherst, Amherst, MA, 01003, USA.
David Julian McClementsCenter for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Research Center, Department of Environmental Health, Harvard School of Public Health, Boston, MA, 02115, USA.
Philip DemokritouCenter for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Research Center, Department of Environmental Health, Harvard School of Public Health, Boston, MA, 02115, USA. pdemokri@hsph.harvard.edu.
National Institute of Environmental Health Sciences · USConnecticut Agricultural Experiment Station · USUniversity of Massachusetts Amherst · USHarvard University · US

Funding

Engineered Nanomaterial Synthesis, Characterization and Method Development Center for Nano-safety ResearchU24ES026946 · NIEHS · HARVARD SCHOOL OF PUBLIC HEALTH · PI DEMOKRITOU, PHILIP · 2016 to 2020
$3.8M
NIEHS NIH HHS U24 ES026946
6 · The paper itself

Abstract

backgroundEngineered nanomaterials (ENMs) are increasingly added to foods to improve their quality, sensory appeal, safety and shelf-life. Human exposure to these ingested ENMs (iENMS) is inevitable, yet little is known of their hazards. To assess potential hazards, efficient in vitro methodologies are needed to evaluate particle biokinetics and toxicity. These methodologies must account for interactions and transformations of iENMs in foods (food matrix effect) and in the gastrointestinal tract (GIT) that are likely to determine nano-biointeractions. Here we report the development and application of an integrated methodology consisting of three interconnected stages: 1) assessment of iENM-food interactions (food matrix effect) using model foods; 2) assessment of gastrointestinal transformations of the nano-enabled model foods using a three-stage GIT simulator; 3) assessment of iENMs biokinetics and cellular toxicity after exposure to simulated GIT conditions using a triculture cell model. As a case study, a model food (corn oil-in-water emulsion) was infused with Fe

methodsA corn oil in phosphate buffer emulsion was prepared using a high speed blender and high pressure homogenizer. Iron oxide ENM was dispersed in water by sonication and combined with the food model. The resulting nano-enabled food was passed through a three stage (mouth, stomach and small intestine) GIT simulator. Size distributions of nano-enabled food model and digestae at each stage were analyzed by DLS and laser diffraction. TEM and confocal imaging were used to assess morphology of digestae at each phase. Dissolution of Fe2O3 ENM along the GIT was assessed by ICP-MS analysis of supernatants and pellets following centrifugation of digestae. An in vitro transwell triculture epithelial model was used to assess biokinetics and toxicity of ingested Fe

resultsIt was demonstrated that the interactions of iENMs with food and GIT components influenced nanoparticle fate and transport, biokinetics and toxicological profile. Large differences in particle size, charge, and morphology were observed in the model food with and without Fe

conclusionsWe present a simple integrated methodology for studying the biokinetics and toxicology of iENMs, which takes into consideration nanoparticle-food-GIT interactions. The importance of food matrix and GIT effects on biointeractions was demonstrated, as well as the incorporation of these critical factors into a cellular toxicity screening model. Standardized food models still need to be developed and used to assess the effect of the food matrix effects on the fate and bioactivity of iENMs since commercial foods vary considerably in their compositions and structures.

Indexed as

EatingNanotechnologyAdministration, OralCaco-2 CellsCell SurvivalDigestionFerric CompoundsGastrointestinal TractHumansModels, AnatomicNanostructuresReproducibility of ResultsRisk AssessmentSolubilitySurface PropertiesTime FactorsFerric Compoundsferric oxideiENM nanotoxicologyIngested engineered nanomaterialNanosafety

Identifiers

PMID29029643
PMCPMC5640936
OpenAlexW2762858448

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.