Theoretical Evaluation of DNA Genotoxicity of Graphene Quantum Dots: A Combination of Density Functional Theory and Molecular Dynamics Simulations
Bibliographic record
Abstract
Owing to their unique morphology, ultrasmall lateral sizes, and exceptional properties, graphene quantum dots (GQDs) hold great potential in many applications, especially in the fields of electrochemical biosensors, bioimaging, drug delivery, gene delivery, etc. Their biosafety and potential genotoxicity to human and animal cells have been a growing concern in recent years. Especially, the potential DNA damage caused by GQDs is very crucial but still unclear. In this study, the effect of GQDs on DNA damage has been evaluated by a combination of molecular dynamics (MD) simulations and density functional theory. Our results demonstrate that the DNA damaging mechanism of GQDs depends on the size of GQDs. The small GQDs (seven benzene rings) tend to enter into the interior of DNA molecules and cause a DNA base mismatch. The relatively large GQDs (61 benzene rings) tend to adsorb onto the two ends of a DNA molecule and cause DNA unwinding. Due to the strong interaction between guanine (G) and GQDs, the effect of GQDs is much larger on G than on the other three bases (A, C, and T). In addition, the concentration of GQDs could also affect the results of DNA damaging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".