MRI Measurement of Regional Lung Deposition in Mice Exposed Nose-Only to Nebulized Superparamagnetic Iron Oxide Nanoparticles
Bibliographic record
Abstract
Superparamagnetic iron oxide nanoparticles show potential in magnetic targeting of inhaled aerosols to localized sites within the lung. These particles are also used as contrast agents in magnetic resonance imaging (MRI). In the present work, we examine the feasibility of measuring regional lung deposition of iron oxide nanoparticles using MRI. Mice were exposed nose-only to nebulized superparamagnetic iron oxide nanoparticles. The droplet size distribution in the inhalation chamber was measured using a time-of-flight device. Regional concentrations of iron in the left and right lung were assessed with MRI by measuring the longitudinal relaxation times (T(1)) of the lung tissue in exposed mice, compared to a baseline group. Regional concentrations of iron in the lungs of the mice ranged from 1.1 +/- 0.8 microg/cm(3) (mean +/- one standard deviation, n = 6) in peripheral lung regions to 2.7 +/- 1.4 microg/cm(3) in the central lung, with no significant difference between the left and right lung. The nebulized droplets in the inhalation chamber had mass median aerodynamic diameter (MMAD) of 5.6 +/- 0.8 microm, with a geometric standard deviation (GSD) of 1.30 +/- 0.03 (both values expressed as mean +/- one standard deviation, n = 6). MRI shows promise for in vivo measurement of regional lung concentrations of superparamagnetic iron oxide nanoparticles, and may be useful in studies of lung deposition and clearance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".