ArticleParticle and fibre toxicology2023
An experimental study on lung deposition of inhaled 2 μm particles in relation to lung characteristics and deposition models.
Article in Particle and fibre toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Human in vitro airway models to assess point-of-contact respiratory toxicity of surfactants and derive human equivalent concentrations after single dose exposure.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- A Monte Carlo-Based 3D Whole Lung Model for Aerosol Deposition Studies: Implementation and Validation.Bioengineering (Basel, Switzerland) · 2025Article
- Physicochemical and Toxicological Properties of Particles Emitted from Scalmalloy During the LPBF Process.Toxics · 2025Article
- Size-dependent retention of elongate mineral particles in human lungs: modeling and implications for risk assessment.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe understanding of inhaled particle respiratory tract deposition is a key link to understand the health effects of particles or the efficiency for medical drug delivery via the lung. However, there are few experimental data on particle respiratory tract deposition, and the existing data deviates considerably when comparing results for particles > 1 μm.
methodsWe designed an experimental set-up to measure deposition in the respiratory tract for particles > 1 μm, more specifically 2.3 μm, with careful consideration to minimise foreseen errors. We measured the deposition in seventeen healthy adults (21-68 years). The measurements were performed at tidal breathing, during three consecutive 5-minute periods while logging breathing patterns. Pulmonary function tests were performed, including the new airspace dimension assessment (AiDA) method measuring distal lung airspace radius (r
resultsThe measured fractional deposition for 2.3 μm particles was 0.60 ± 0.14, which is significantly higher than predicted by any of the models tested, ranging from 0.37 ± 0.08 to 0.53 ± 0.09. The multiple-path particle dosimetry (MPPD) model most closely predicted the measured deposition when using the new PNNL lung model. The individual variability in measured particle deposition was best explained by breathing pattern and distal airspace radius (r
conclusionsWhole lung models need to be tuned and improved to predict the respiratory tract particle deposition of micron-sized particles, and to capture individual variations - a variation that is known to be higher for aged and diseased lungs. Further, the results support the hypothesis that the AiDA method measures dimensions in the peripheral lung and that r
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.