Multiplexed, <scp>UVC</scp>‐Induced, Sequence‐Dependent <scp>DNA</scp> Damage Detection
Bibliographic record
Abstract
The exposure of DNA to ultraviolet (UV) radiation causes sequence-dependent damage. Thus, there is a need for an analytical technique that can detect damage in large numbers of DNA sequences simultaneously. In this study, we have designed an assay for UVC-induced DNA damage in multiple oligonucleotides simultaneously by using a 96-well plate and a novel automated sample mover. The UVC-induced DNA damage is measured using smart probes, analogs of molecular beacons in which guanosine nucleotides act as the fluorescence quencher. Our results show that the oligonucleotide damage constants obtained with this method are reproducible and similar to those obtained in cuvettes. The calibration curve for poly-dT shows good linearity (R(2) = 0.96), with limits of detection (LOD) and quantification (LOQ) equal to 55 and 183 nm, respectively. The results show that the damage kinetics upon irradiation is sensitive to the different types of photoproducts formed in the different sequences used; i.e. poly-A oligonucleotides containing guanine are damaged at a faster rate than poly-A oligonucleotides containing either thymine or cytosine. Thus, detecting DNA damage in a 96-well plate and quantifying the damage with smart probes are a simple, fast and inexpensive mix-and-read technique for multiplexed, sequence-specific DNA damage detection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".