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Record W4410020556 · doi:10.1016/j.jgeb.2025.100501

Impact of ionizing radiation and low-energy electrons on DNA functionality: radioprotection and radiosensitization potential of natural products

2025· review· en· W4410020556 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Genetic Engineering and Biotechnology · 2025
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Genetic and Mutation Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsIonizing radiationRadiationElectronNatural (archaeology)PhysicsRadiochemistryChemistryIrradiationBiologyNuclear physics

Abstract

fetched live from OpenAlex

Ionizing radiation (IR) is a key cancer treatment, but its DNA-damaging effects, particularly double-strand breaks (DSBs) and clustered lesions, pose challenges for therapy. Clustered DNA lesions, often induced by low-energy electrons (LEEs), contribute significantly to genomic instability and repair resistance. Chemotherapeutic agents like cisplatin can enhance IR-induced damage, making tumor cells more susceptible. Emerging strategies in radiation oncology target DNA repair pathways, using inhibitors like poly(ADP-ribose) polymerase (PARP) to sensitize tumors to IR. Natural products, including polyphenols, flavonoids, and alkaloids, offer promising radioprotective effects by scavenging reactive oxygen species and enhancing DNA repair. These agents not only protect normal tissues but also increase tumor sensitivity to IR, improving therapeutic outcomes. Future research should focus on optimizing these natural agents for clinical use, integrating them into radiotherapy protocols for enhanced efficacy and reduced toxicity.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.911
Threshold uncertainty score0.276

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 integrity0.0000.000
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 head0.007
GPT teacher head0.215
Teacher spread0.209 · 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