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Record W2912028404 · doi:10.5772/intechopen.81064

Oncogenetics of Lung Cancer Induced by Environmental Carcinogens

2019· book-chapter· en· W2912028404 on OpenAlex
Victor D. Martínez, Adam P. Sage, Erin A. Marshall, Miwa Suzuki, Aaron A. Goodarzi, Graham Dellaire, Wan L. Lam

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIntechOpen eBooks · 2019
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsUniversity of CalgaryCanadian Cancer SocietyOccupational Cancer Research CentreDalhousie University
FundersCanadian Institutes of Health ResearchUniversity of British ColumbiaCanada Research Chairs
KeywordsCarcinogenLung cancerEpigeneticsCancer researchDNA damageBiologyLungTobacco smokeMedicineGenePathologyGeneticsDNAInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

The molecular landscape of non-tobacco-induced primary lung tumors displays specific oncogenetic features. The etiology of these tumors has been largely associated with exposure to well-established environmental lung carcinogens such as radon, arsenic, and asbestos. Environmental carcinogens can induce specific genetic and epigenetic alterations in lung tissue, leading to aberrant function of lung cancer oncogenes and tumor suppressor genes. These molecular events result in the disruption of key cellular mechanisms, such as protection against oxidative stress and DNA damage-repair, which promotes tumor development and progression. This chapter provides a comprehensive discussion of the specific carcinogenic mechanisms associated with exposure to radon, arsenic, and asbestos. It also summarizes the main protein-coding and non-coding genes affected by exposure to these environmental agents, and the underlying molecular mechanisms promoting their deregulation in lung cancer. Finally, the chapter examines the anticipated challenges in personalized intervention strategies in non-tobacco-induced lung cancer.

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.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.018
GPT teacher head0.258
Teacher spread0.241 · 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