Distribution and immunotoxicity by intravenous injection of iron nanoparticles in a murine model
Bibliographic record
Abstract
With the increased application of iron oxide nanoparticles (FeNPs) for biomedical imaging purposes, concerns regarding the onset of the unexpected adverse health effects following exposure have been rapidly raised. In this study, we investigated the tissue distribution and immunotoxicity of FeNPs (2 and 4 mg kg(-1)) over time (2, 4 and 13 weeks) after single intravenous injection. At 13 weeks after a single injection, the iron levels increased in all measured tissues compared to the control, and iron accumulation was notable in the liver, spleen and thymus. These changes were accompanied by changes in levels of redox reaction-related elements, including copper, manganese, zinc and cobalt. In addition, as compared to the control, the number of white blood cells and percentage of neutrophils significantly increased in the treated groups, and the interleukin-8 secretion and lactate dehydrogenase release were clearly elevated in the treated groups along with enhanced expressions of chemotaxis-related proteins. However, expression of antigen presenting related proteins attenuated following accumulation of FeNPs. Taken together, we suggest that FeNPs may primarily induce toxicity in the liver and immune system, and immunotoxicological evaluation should be considered to predict adverse health effects following exposure to NPs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".