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Record W2991238681 · doi:10.1002/em.22347

<i>In utero</i> Exposure to Genotoxicants Leading to Genetic Mosaicism: An Overlooked Window of Susceptibility in Genetic Toxicology Testing?

2019· article· en· W2991238681 on OpenAlexaff
Roger Godschalk, Carole L. Yauk, Jan van Benthem, George R. Douglas, Francesco Marchetti

Bibliographic record

VenueEnvironmental and Molecular Mutagenesis · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsHealth Canada
Fundersnot available
KeywordsBiologyGenetic testingGeneticsGenotoxicityComputational biologyMedicineToxicity

Abstract

fetched live from OpenAlex

In utero development represents a sensitive window for the induction of mutations. These mutations may subsequently expand clonally to populate entire organs or anatomical structures. Although not all adverse mutations will affect tissue structure or function, there is growing evidence that clonally expanded genetic mosaics contribute to various monogenic and complex diseases, including cancer. We posit that genetic mosaicism is an underestimated potential health problem that is not fully addressed in the current regulatory genotoxicity testing paradigm. Genotoxicity testing focuses exclusively on adult exposures and thus may not capture the complexity of genetic mosaicisms that contribute to human disease. Numerous studies have shown that conversion of genetic damage into mutations during early developmental exposures can result in much higher mutation burdens than equivalent exposures in adults in certain tissues. Therefore, we assert that analysis of genetic effects caused by in utero exposures should be considered in the current regulatory testing paradigm, which is possible by harmonization with current reproductive/developmental toxicology testing strategies. This is particularly important given the recent proposed paradigm change from simple hazard identification to quantitative mutagenicity assessment. Recent developments in sequencing technologies offer practical tools to detect mutations in any tissue or species. In addition to mutation frequency and spectrum, these technologies offer the opportunity to characterize the extent of genetic mosaicism following exposure to mutagens. Such integration of new methods with existing toxicology guideline studies offers the genetic toxicology community a way to modernize their testing paradigm and to improve risk assessment for vulnerable populations. Environ. Mol. Mutagen. 61:55-65, 2020. © 2019 The Authors. Environmental and Molecular Mutagenesis published by Wiley Periodicals, Inc. on behalf of Environmental Mutagen Society.

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 score0.277
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.229
Teacher spread0.222 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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