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Record W2132635267 · doi:10.1186/1476-069x-11-89

Arsenic, asbestos and radon: emerging players in lung tumorigenesis

2012· review· en· W2132635267 on OpenAlexafffund
Roland Hubaux, Daiana D. Becker‐Santos, Katey S.S. Enfield, Stephen Lam, Wan L. Lam, Victor D. Martínez

Bibliographic record

VenueEnvironmental Health · 2012
Typereview
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsBC Cancer Agency
FundersNational Institute for Occupational Safety and HealthNational Institutes of HealthNational Cancer InstituteCanadian Institutes of Health ResearchCongressionally Directed Medical Research ProgramsU.S. Geological SurveyWorld Health Organization
KeywordsLung cancerCarcinogenesisCarcinogenEpigeneticsAsbestosMedicineLungCancer researchArsenicCancerOxidative stressBioinformaticsEnvironmental healthBiologyPathologyGeneticsInternal medicineGeneChemistry

Abstract

fetched live from OpenAlex

The cause of lung cancer is generally attributed to tobacco smoking. However lung cancer in never smokers accounts for 10 to 25% of all lung cancer cases. Arsenic, asbestos and radon are three prominent non-tobacco carcinogens strongly associated with lung cancer. Exposure to these agents can lead to genetic and epigenetic alterations in tumor genomes, impacting genes and pathways involved in lung cancer development. Moreover, these agents not only exhibit unique mechanisms in causing genomic alterations, but also exert deleterious effects through common mechanisms, such as oxidative stress, commonly associated with carcinogenesis. This article provides a comprehensive review of arsenic, asbestos, and radon induced molecular mechanisms responsible for the generation of genetic and epigenetic alterations in lung cancer. A better understanding of the mode of action of these carcinogens will facilitate the prevention and management of lung cancer related to such environmental hazards.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.024
GPT teacher head0.304
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations94
Published2012
Admission routes2
Has abstractyes

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