Lung inflammation caused by long‐term exposure to titanium dioxide in mice involving in NF‐κB signaling pathway
Bibliographic record
Abstract
Abstract Titanium dioxide nanoparticles (TiO2 NPs) are used in many fields, such as paints, medicine additives, food additives, sunscreens, and agriculture. The aim of this study was to investigate the mechanism behind the formation of inflammation induced by TiO2 NPs. ICR mice were exposed to TiO2 NPs through intragastric administration at 2.5, 5, and 10 mg/kg body weight every day for 90 consecutive days. The experiment suggested that long‐term exposure to TiO2 NPs resulted in an obvious inflammatory response in mice lung tissues, which led to a thickened alveoli septum, lung hyperemia, and titanium accumulation. Furthermore, our results show that TiO2 NPs exposure remarkably altered the expression of inflammation‐related cytokines, with increases in proinflammatory cytokines—such as nucleic factor‐κB, interferon‐α, interferon‐β, interleukin‐1β, interleukin‐6, cyclo‐oxygen‐ase, interleukin‐8, interferon‐inducible protein‐10, and platelet‐derived growth factor AB—and decreases in anti‐inflammatory cytokines—such as inhibitor of NF‐κB suppressor of cytokine signaling 1, endothelin 1, peroxisome proliferators‐activated receptors‐γ, and peroxisome proliferators‐activated receptors coactivator‐1α. This finding indicated that TiO2 NPs cause lung inflammation in mice after intragastric administration, primarily through the NF‐κB signaling pathways. Therefore, more attention should be placed on the application of TiO2 NPs and their potential long‐term effects, especially in human beings. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 720–727, 2017.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".