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Record W1968110523 · doi:10.1073/pnas.1106896108

Sidestream tobacco smoke is a male germ cell mutagen

2011· article· en· W1968110523 on OpenAlexaff
Francesco Marchetti, Andrea Rowan‐Carroll, Andrew Williams, Aris Polyzos, M. Lynn Berndt-Weis, Carole L. Yauk

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2011
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsHealth Canada
FundersLawrence Berkeley National LaboratoryTobacco-Related Disease Research ProgramU.S. Department of Energy
KeywordsSomatic cellTobacco smokeDNA damageGermlineBiologyGeneticsMutagenSidestream smokeSpermGermline mutationMutationAndrologyCarcinogenDNACigarette smokeToxicologyGeneChemistryMedicine

Abstract

fetched live from OpenAlex

Active cigarette smoking increases oxidative damage, DNA adducts, DNA strand breaks, chromosomal aberrations, and heritable mutations in sperm. However, little is known regarding the effects of second-hand smoke on the male germ line. We show here that short-term exposure to mainstream tobacco smoke or sidestream tobacco smoke (STS), the main component of second-hand smoke, induces mutations at an expanded simple tandem repeat locus (Ms6-hm) in mouse sperm. We further show that the response to STS is not linear and that, for both mainstream tobacco smoke and STS, doses that induced significant increases in expanded simple tandem repeat mutations in sperm did not increase the frequencies of micronucleated reticulocytes and erythrocytes in the bone marrow and blood of exposed mice. These data show that passive exposure to cigarette smoke can cause tandem repeat mutations in sperm under conditions that may not induce genetic damage in somatic cells. Although the relationship between noncoding tandem repeat instability and mutations in functional regions of the genome is unclear, our data suggest that paternal exposure to second-hand smoke may have reproductive consequences that go beyond the passive smoker.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.097
GPT teacher head0.321
Teacher spread0.224 · 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 designObservational
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

Citations84
Published2011
Admission routes1
Has abstractyes

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