Time-Dependent Subcellular Distribution and Effects of Carbon Nanotubes in Lungs of Mice
Bibliographic record
Abstract
BACKGROUND AND METHODS: Pulmonary deposited carbon nanotubes (CNTs) are cleared very slowly from the lung, but there is limited information on how CNTs interact with the lung tissue over time. To address this, three different multiwalled CNTs were intratracheally instilled into female C57BL/6 mice: one short (850 nm) and tangled, and two longer (4 μm and 5.7 μm) and thicker. We assessed the cellular interaction with these CNTs using transmission electron microscopy (TEM) 1, 3 and 28 days after instillation. RESULTS: TEM analysis revealed that the three CNTs followed the same overall progression pattern over time. Initially, CNTs were taken up either by a diffusion mechanism or via endocytosis. Then CNTs were agglomerated in vesicles in macrophages. Lastly, at 28 days post-exposure, evidence suggesting CNT escape from vesicle enclosures were found. The longer and thicker CNTs more often perturbed and escaped vesicular enclosures in macrophages compared to the smaller CNTs. Bronchoalveolar lavage (BAL) showed that the CNT exposure induced both an eosinophil influx and also eosinophilic crystalline pneumonia. CONCLUSION: Two very different types of multiwalled CNTs had very similar pattern of cellular interactions in lung tissue, with the longer and thicker CNTs resulting in more severe effects in terms of eosinophil influx and incidence of eosinophilic crystalline pneumonia (ECP).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".