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Record W2332175967 · doi:10.1093/rpd/ncq599

Use of a dual-labelled oligonucleotide as a DNA dosemeter for radiological exposure detection

2011· article· en· W2332175967 on OpenAlexaff
T. Wood, B. J. Lewis, K. Mcdermott, L. G. I. Bennett, Kirsten A. Avarmaa, E. C. Corcoran, D. Wilkinson, A. R. Jones, T. Jones, Erika Kennedy, Louise Prud'homme-Lalonde, Donald A. Boudreau, Jean-François Gravel, C. Drolet, A Kerr, L J Schreiner, J. R. M. Pierre, R. Blagoeva, Teodor Veres

Bibliographic record

VenueRadiation Protection Dosimetry · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsInstitut National de la Recherche ScientifiqueDepartment of National DefenceCancer Care South EastUniversité LavalDefence Research and Development CanadaRoyal Military College of Canada
Fundersnot available
KeywordsLibrary scienceHistoryMedicineComputer science

Abstract

fetched live from OpenAlex

A reporter molecule consisting of a synthetic oligonucleotide is being characterised for a novel damage detection scenario for its potential use as a field-deployable, personal deoxyribonucleic acid (DNA) dosemeter for radiation detection. This dosemeter is devoid of any biological properties other than being naked DNA and therefore has no DNA repair capabilities. It supports biodosimetry techniques, which require lengthy analysis of cells from irradiated individuals, and improves upon inorganic dosimetry, thereby providing for a more relevant means of measuring the accumulated dose from a potentially mixed-radiation field. Radiation-induced single strand breaks (SSBs) within the DNA result in a quantifiable fluorescent signal. Proof of concept has been achieved over 250 mGy-10 Gy dose range in radiation fields from ⁶⁰Co, with similar results seen using a linear accelerator X-ray source. Further refinements to both the molecule and the exposure/detection platform are expected to lead to enhanced levels of detection for mixed-field radiological events.

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 score0.073
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 head0.047
GPT teacher head0.255
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

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

Citations3
Published2011
Admission routes1
Has abstractyes

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