A Novel Open Tubular Capillary Electrochromatographic Method for Differentiating the DNA Interaction Affinity of Environmental Contaminants
Bibliographic record
Abstract
The interaction of chemicals with DNA may lead to genotoxicity, mutation or carcinogenicity. A simple open tubular capillary electrochromatographic method is proposed to rapidly assess the interaction affinity of three environmental contaminants (1,4-phenylenediamine, pyridine and 2,4-diaminotoluene) to DNA by measuring their retention in the capillaries coated with DNA probes. DNA oligonucleotide probes were immobilized on the inner wall of a fused silica capillary that was first derivatized with 3-(aminopropyl)-triethoxysilane (APTES). The difference in retention times and factors was considered as the difference in interaction affinity of the contaminants to the DNA probes. The interaction of the contaminants with both double-stranded (dsDNA) and single-stranded DNA (ssDNA) coatings was compared. Retention factors of 1,4-phenylenediamine, pyridine and 2,4-diaminotoluene in the capillary coated with ssDNA probe were 0.29, 0.42, and 0.44, respectively. A similar trend was observed in the capillary coated with dsDNA, indicating that 2,4-diaminotoluene has the highest affinity among the three contaminants. The relative standard deviation (RSD) for the retention factors was in the range of 0.05-0.69% (n = 3). The results demonstrated that the developed technique could be applied for preliminary screening purpose to provide DNA interaction affinity information of various environmental contaminants.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".