Interaction Potency of Single-Walled Carbon Nanotubes with DNAs: A Novel Assay for Assessment of Hazard Risk
Bibliographic record
Abstract
Increasing use of single-walled carbon nanotubes (SWCNTs) necessitates a novel method for hazard risk assessment. In this work, we investigated the interaction of several types of commercial SWCNTs with single-stranded (ss) and double-stranded (ds) DNA oligonucleotides (20-mer and 20 bp). Based on the results achieved, we proposed a novel assay that employed the DNA interaction potency to assess the hazard risk of SWCNTs. It was found that SWCNTs in different sizes or different batches of the same product number of SWCNTs showed dramatically different potency of interaction with DNAs. In addition, the same SWCNTs also exerted strikingly different interaction potency with ss- versus ds- DNAs. The interaction rates of SWCNTs with DNAs were investigated, which could be utilized as the indicator of potential hazard for acute exposure. Compared to solid SWCNTs, the SWCNTs dispersed in liquid medium (2% sodium cholate solution) exhibited dramatically different interaction potency with DNAs. This indicates that the exposure medium may greatly influence the subsequent toxicity and hazard risk produced by SWCNTs. Based on the findings of dose-dependences and time-dependences from the interactions between SWCNTs and DNAs, a new chemistry based assay for hazard risk assessment of nanomaterials including SWCNTs has been presented.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".