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Record W2160700027 · doi:10.1183/09031936.00168509

Lung exposure to nanoparticles modulates an asthmatic response in a mouse model

2010· article· en· W2160700027 on OpenAlexaff
Salik Hussain, Jeroen Vanoirbeek, Katrien Luyts, Vanessa De Vooght, Eric Verbeken, Leen C.J. Thomassen, Johan A. Martens, David Dinsdale, Sonja Boland, Francelyne Marano, Benoît Nemery, Peter Hoet

Bibliographic record

VenueEuropean Respiratory Journal · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsResponse Biomedical (Canada)
Fundersnot available
KeywordsMedicineLungAsthmaImmunologyNanoparticleNanotechnologyInternal medicine

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the modulation of an asthmatic response by titanium dioxide (TiO₂) or gold (Au) nanoparticles (NPs) in a murine model of diisocyanate-induced asthma. On days 1 and 8, BALB/c mice received 0.3% toluene diisocyanate (TDI) or the vehicle acetone-olive oil (AOO) on the dorsum of both ears (20 μL). On day 14, the mice were oropharyngeally dosed with 40 μL of a NP suspension (0.4 mg·mL⁻¹ (∼0.8 mg·kg⁻¹) TiO₂ or Au). 1 day later (day 15), the mice received an oropharyngeal challenge with 0.01% TDI (20 μL). On day 16, airway hyperreactivity (AHR), bronchoalveolar lavage (BAL) cell and cytokine analysis, lung histology, and total serum immunoglobulin E were assessed. NP exposure in sensitised mice led to a two- (TiO₂) or three-fold (Au) increase in AHR, and a three- (TiO₂) or five-fold (Au) increase in BAL total cell counts, mainly comprising neutrophils and macrophages. The NPs taken up by BAL macrophages were identified by energy dispersive X-ray spectroscopy. Histological analysis revealed increased oedema, epithelial damage and inflammation. In conclusion, these results show that a low, intrapulmonary doses of TiO₂ or Au NPs can aggravate pulmonary inflammation and AHR in a mouse model of diisocyanate-induced asthma.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.305
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations161
Published2010
Admission routes1
Has abstractyes

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