Potential Role of Soluble Metal Impurities in the Acute Lung Inflammogenicity of Multi-Walled Carbon Nanotubes
Bibliographic record
Abstract
Multi-walled carbon nanotubes (MWCNTs) have variable metal impurities, but little is known about the impact of soluble metal impurities on the toxicity of MWCNTs. Here, we evaluated the role of soluble metal impurities to the acute inflammogenic potential of MWCNTs, using five types of high purity MWCNTs (>95%). MWCNTs and their soluble fractions collected at 24 h after incubation in phosphate-buffered saline showed diverse metal impurities with variable concentrations. The fiber-free soluble fractions produced variable levels of reactive oxygen species (ROS), and the iron level was the key determinant for ROS production. The acute inflammation at 24 h after intratracheal instillation of MWCNTs to rats at 0.19, 0.63, and 1.91 mg MWCNT/kg body weight (bw) or fiber-free supernatants from MWCNT suspensions at 1.91 and 7.64 mg MWCNT/kg bw showed that the number of granulocytes, a marker for acute inflammation, was significantly increased with a good dose-dependency. The correlation study showed that neither the levels of iron nor the ROS generation potential of the soluble fractions showed any correlations with the inflammogenic potential. However, the total concentration of transition metals in the soluble fractions showed a good correlation with the acute lung inflammogenic potential. These results implied that metal impurities, especially transitional metals, can contribute to the acute inflammogenic potential of MWCNTs, although the major parameter for the toxicity of MWCNTs is size and shape.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".