Cytotoxicity of Zirconium Oxide Nanoparticles on Rabbit Tooth Gum Cells
Bibliographic record
Abstract
The cytotoxicity mechanism of zirconium oxide nanoparticles (ZrO2-NPs) has not been clarified to-date. Nevertheless, the generation of reactive oxygen species (ROS) by ZrO2-NPs has been perceived as an important mechanism in some in vivo studies. Accordingly, this study was conducted to identify the potential effects of ZrO2-NPs on rabbit tooth gum cells as well as the mechanism/s of their cytotoxicity. Synthesized ZrO2-NPs were characterized using scanning electron microscopy (SEM), dynamic light scattering (DLS), Cell viability, ROS level, lipid peroxidation (LPO), mitochondrial membrane potential (MMP), glutathione count (GSH/GSSG), lysosome damage and apoptosis/necrosis were evaluated. Biodistribution studies were conducted on tooth gum, liver, kidney, heart and brain tissues in the rabbit using inductively coupled plasma optical emission spectrometry (ICP/OES). ZrO2-NPs increased ROS, MMP collapse and malondialdehyde (MDA). In contrast, GSH/GSSG and apoptosis/necrosis were found to be changed. The ZrO2-NPs accumulated significantly more on rabbit tooth gum tissues compared with other organs ([Formula: see text]). The study provided evidence that ZrO2-NPs cannot be considered completely biocompatible in the gum cell tissues of the rabbit. Before these nanoparticles can be used for human dental applications, further investigations on a wide range of cell death signaling should be performed.
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) | 0.001 | 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".