MétaCan
Menu
Back to cohort

Transcriptional Modulation of the ERK1/2 MAPK and NF-kB Pathways in Human Urothelial Cells After Trivalent Arsenical Exposure: Implications for Urinary Bladder Cancer

2012· article· en· W3140863219 on OpenAlexvenueno aff
Kathryn A. Bailey, Kathleen Wallace, Lisa Smeester, Sheau‐Fung Thai, Douglas C. Wolf, Stephen W. Edwards, Rebecca C. Fry

Bibliographic record

VenueJournal of cancer research updates · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesU.S. Environmental Protection Agency
KeywordsMAPK/ERK pathwayCarcinogenesisKinaseArseniteCarcinogenCancer researchExtracellularSignal transductionMetaboliteBiologyCancerNF-κBChemistryCell biologyBiochemistryArsenicGenetics

Abstract

fetched live from OpenAlex

Chronic exposure to drinking water contaminated with inorganic arsenic (iAs) is associated with an increased risk of urinary bladder (UB) cancers in humans. The exact role of specific iAs metabolite(s) in As-mediated carcinogenesis remains largely unknown. Experimental evidence suggests that trivalent arsenicals, namely arsenite (iAsIII) and two of its metabolites, monomethylarsonous acid (MMAIII) and dimethylarsinous acid (DMAIII), are possible proximate UB carcinogens. Here, we used a transcriptomics approach to examine perturbed molecular pathways in a human urothelial cell line (UROtsa) after short-term exposure to iAsIII, MMAIII and DMAIII. Molecular pathways containing genes that encode proteins implicated in UB cancer development were perturbed by both MMAIII and DMAIII. These pathways included those of the extracellular signal-regulated kinase 1/2 mitogen-activated protein kinase (ERK 1/2 MAPK) and nuclear factor kappa beta (NF-kB). Together, these results may inform the current understanding of effects in the UB induced by acute As exposure and the relationship of these effects with As-mediated carcinogenesis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.673

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.365
Teacher spread0.304 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of cancer research updatesSame topicArsenic contamination and mitigationFrench-language works237,207