A transcriptomic overview of lung and liver changes one day after pulmonary exposure to graphene and graphene oxide
Bibliographic record
Abstract
Hazard evaluation of graphene-based materials (GBM) is still in its early stage and it is slowed by their large diversity in the physicochemical properties. This study explores transcriptomic differences in the lung and liver after pulmonary exposure to two GBM with similar physical properties, but different surface chemistry. Female C57BL/6 mice were exposed by a single intratracheal instillation of 0, 18, 54 or 162 μg/mouse of graphene oxide (GO) or reduced graphene oxide (rGO). Pulmonary and hepatic changes in the transcriptome were profiled to identify commonly and uniquely perturbed functions and pathways by GO and rGO. These changes were then related to previously analyzed toxicity endpoints. GO exposure induced more differentially expressed genes, affected more functions, and perturbed more pathways compared to rGO, both in lung and liver tissues. The largest differences were observed for the pulmonary innate immune response and acute phase response, and for hepatic lipid homeostasis, which were strongly induced after GO exposure. These changes collective indicate a potential for atherosclerotic changes after GO, but not rGO exposure. As GO and rGO are physically similar, the higher level of hydroxyl groups on the surface of GO is likely the main reason for the observed differences. GO exposure also uniquely induced changes in the transcriptome related to fibrosis, whereas both GBM induced similar changes related to Reactive Oxygen Species production and genotoxicity. The differences in transcriptomic responses between the two GBM types can be used to understand how physicochemical properties influence biological responses and enable hazard evaluation of GBM and hazard ranking of GO and rGO, both in relation to each other and to other nanomaterials.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".