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Record W4387516449 · doi:10.1093/rpd/ncad055

Single-cell DNA sequencing—a potential dosimetric tool

2023· article· en· W4387516449 on OpenAlexafffund
Felix Mathew, James Manalad, Jonathan Yeo, Luc Galarneau, Norma Ybarra, Yu Chang Wang, Patricia N. Tonin, Ioannis Ragoussis, J. Kildea

Bibliographic record

VenueRadiation Protection Dosimetry · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsMcGill University Health CentreMcGill Genome CentreMcGill University
FundersNational University of SingaporeNatural Sciences and Engineering Research Council of CanadaMcGill UniversityUniversité Laval
KeywordsIn silicoGenomeMonte Carlo methodComputational biologyDNAIrradiationRadiationDNA sequencingCellBiologyGeneticsPhysicsGeneOpticsMathematics

Abstract

fetched live from OpenAlex

We hypothesised that single-cell whole-genome sequencing has the potential to detect mutational differences in the genomes of the cells that are irradiated with different doses of radiation and we set out to test our hypothesis using in silico and in vitro experiments. In this manuscript, we present our findings from a Monte Carlo single-cell irradiation simulation performed in TOPAS-nBio using a custom-built geometric nuclear deoxyribonucleic acid (DNA) model, which predicts a significant dose dependence of the number of cluster damages per cell as a function of radiation dose. We also present preliminary experimental results, obtained from single-cell whole-genome DNA sequencing analysis performed on cells irradiated with different doses of radiation, showing promising agreement with the simulation results.

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 score0.036
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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