Metabolomics approach to study in vivo toxicity of graphene oxide nanosheets
Bibliographic record
Abstract
Abstract Although graphene oxide (GO) nanosheets are widely used in different fields, the mechanism of their toxicity remains relatively unknown. NMR‐based metabolomics was used to study in vivo time and dose‐dependent toxicity of GO nanosheets in mice. Sixty serum samples from mice in four different time intervals including 24 and 72 h and 7 and 21 days after injection of 0‐, 1‐, and 10‐mg/kg b.w. were analyzed based on 1HNMR spectra of each sample and multivariate methods. In comparison with the control group, 12 changed metabolites were identified in GO nanosheet‐treated mice groups. These metabolites are involved in steroid hormone biosynthesis and steroid biosynthesis pathways. It was seen that the time factor is more important than the dose factor and the groups were separated in a time direction, completely. We found that GO nanosheets has toxicity and can affect steroidal hormones. However, this study shows that after 21 days, the treated groups regardless of their GO nanosheet dose are very close to the control group. This means that in one step exposure to GO nanosheets, their toxicity diminished after 21 days.
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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 | 0.000 | 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".