Detection of Deoxyribonucleic Acid Damage Induced by Metal Ion Interactions and Repair of Metal Ion Deoxyribonucleic Acid Cross-links
Bibliographic record
Abstract
There are many forms of DNA damage. One of these, DNA-metal cross-links are \nparticularly problematic as they not only affect the tautomeric structure of DNA, but can \nalso inhibit enzymatic activity and block amplification with DNA polymerases. Since the \ninability to amplify DNA can affect many fields of genetic research, it is necessary to \nfind ways to identify this damage and repair the DNA. Copper is one metal that can form \nmetal-DNA cross-links and can seriously affect the recovery and analysis of degraded \nDNA from forensic or archaeological material. In this research DNA-copper cross-links \nwere generated for the development of a repair method and applied to an archaeological \nsample. The DNA-copper adducts were identified through the use of gas \nchromatography-mass spectrometry on several templates of different complexity: single \ndeoxyribonucleotides, a synthetic 22 base pair double stranded DNA fragment, modern \namplified DNA, and an ancient extract with naturally occurring copper cross-links. A \nnumber of chemicals were considered for direct reversal repair of copper-DNA cross- \nlinks of which ethylenediamine was successful. Treatment of all the templates with \nethylenediamine resulted in the repair of the nucleobase, specifically guanine which is the \nmost susceptible to copper cross-link formation. The success of the direct reversal repair \nwas verified using GC-MS based on expected retention time and the identification of ion \nfingerprints. The amount of copper-DNA adducts measured in each template varied \ngreatly as did the success of the direct reversal repair although repair was evident in all \nsamples.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".