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Record W2952152397 · doi:10.1038/s41388-018-0153-z

How asbestos drives the tissue towards tumors: YAP activation, macrophage and mesothelial precursor recruitment, RNA editing, and somatic mutations

2018· article· en· W2952152397 on OpenAlexaff
Hubert Rehrauer, Licun Wu, Walter Blum, Lazslo Pecze, Thomas Henzi, Véronique Serre‐Beinier, Catherine Aquino, Bart Vrugt, Marc de Perrot, Beat Schwaller, Emanuela Felley‐Bosco

Bibliographic record

VenueOncogene · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersPrincess Margaret Hospital FoundationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsMesothelial CellMesotheliumBiologyMesotheliomaTranscriptomeCancer researchSomatic cellCarcinogenesisPeritoneumMacrophageTumor progressionDownregulation and upregulationPathologyGene expressionGeneMedicineIn vitroAnatomy

Abstract

fetched live from OpenAlex

Chronic exposure to intraperitoneal asbestos triggered a marked response in the mesothelium well before tumor development. Macrophages, mesothelial precursor cells, cytokines, and growth factors accumulated in the peritoneal lavage. Transcriptome profiling revealed YAP/TAZ activation in inflamed mesothelium with further activation in tumors, paralleled by increased levels of cells with nuclear YAP/TAZ. Arg1 was one of the highest upregulated genes in inflamed tissue and tumor. Inflamed tissue showed increased levels of single-nucleotide variations, with an RNA-editing signature, which were even higher in the tumor samples. Subcutaneous injection of asbestos-treated, but tumor-free mice with syngeneic mesothelioma tumor cells resulted in a significantly higher incidence of tumor growth when compared to naïve mice supporting the role of the environment in tumor progression.

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 categoriesnone
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 score0.054
Threshold uncertainty score0.324

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 head0.017
GPT teacher head0.279
Teacher spread0.262 · 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 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

Citations73
Published2018
Admission routes1
Has abstractyes

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