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Record W2913560801 · doi:10.1021/acsomega.8b03017

Comparison of K-<i>Ras</i> and N-<i>Ras</i> Mutagenic Hot Spots for UVC Damage

2019· article· he· W2913560801 on OpenAlexafffund
Sindhu Nair, Glen R. Loppnow

Bibliographic record

VenueACS Omega · 2019
Typearticle
Languagehe
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDNA damageGeneGenomeDNAGeneticsBiologyThymineMolecular biologyChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.288
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes2
Has abstractyes

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