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Gamma and Ultraviolet Radiation Cause DNA Crosslinking in the Presence of Metal Ions at High pH¶

2001· article· en· W1926902672 on OpenAlexafffund
Shaunivan Labiuk, Louis T. J. Delbaere, Jeremy S. Lee

Bibliographic record

VenuePhotochemistry and Photobiology · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Nucleic Acid Chemistry
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsUltravioletIonMetal ions in aqueous solutionRadiationChemistryMetalPhotochemistryUltraviolet radiationDNARadiochemistryMaterials scienceOptoelectronicsBiochemistryOpticsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

M-DNA is a novel duplex conformation in which metal ions such as Co2+, Ni2+ or Zn2+ replace the imino protons of every base pair. An ethidium fluorescence assay was used to estimate lesions in M-DNA induced by gamma- and UV radiation. General damage to DNA was assessed from the loss of ethidium fluorescence after irradiation of calf thymus DNA. Crosslinks were measured from the return of ethidium fluorescence after a heating and cooling step. Strand breaks were estimated from the loss of fluorescence in covalently closed circular plasmid DNA after a heating and cooling step. For the Co2+ form of M-DNA, gamma-radiation caused the very efficient formation of crosslinks which was not observed with B-DNA nor with the Ni2+ or Zn2+ forms of M-DNA. The crosslinks occurred in both A-T and G-C base pairs but did not form in the presence of a free radical scavenger. Crosslinks induced by UV radiation also formed at a faster rate in the Co2+, Ni2+ and Zn2+ forms of M-DNA compared to B-DNA; crosslinking occurred in all DNA but was more prominent in AT-rich sequences and was not inhibited by a free radical scavenger. Therefore, the presence of certain metal ions may lead to large increases in the formation of radiation-induced crosslinks in DNA.

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 categoriesnone
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 score0.011
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2001
Admission routes2
Has abstractyes

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