Comparison of K-<i>Ras</i> and N-<i>Ras</i> Mutagenic Hot Spots for UVC Damage
Bibliographic record
Abstract
Abstract It has been well established that mutations in K-Ras and N-Ras proto-oncogenes can convert them into active oncogenes. Current molecular cancer research has been focused on determining the key steps by which cellular genes become oncogenes and not on the underlying and fundamental chemical damage mechanism and susceptibility to damage. In this study, we investigate the damage hot spots present in the N-Ras and K-Ras genes upon exposure to UVC radiation. Detection of damage is accomplished by a simple, sensitive, mix-and-read assay using an EvaGreen probe in a 96-well microtiter plate. Our results show that, although there is high degree of sequential similarities among K-Ras and N-Ras genes, they show different degrees of UV damage in different portions of their genomes. Our experiments demonstrate that overall, the K-Ras genome is more prone to UVC damage than the N-Ras genome. We observe that the extent of damage increases with increasing number of TTs in a sequence, consistent with previous results that show that thymine cyclobutyl photodimers are the primary DNA damage photoproducts upon UVC irradiation. This understanding of the effect of UVC radiation on various codons of K-Ras and N-Ras genes will help to increase our understanding about hot spots of DNA damage and the chemical damage mechanism.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".