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Record W2795512548 · doi:10.18632/genesandcancer.124

Switching off malignant mesothelioma: exploiting the hypoxic microenvironment

2017· review· en· W2795512548 on OpenAlexafffund
Noushin Nabavi, Kevin L. Bennewith, Andrew Churg, Yuzhuo Wang, Colin C. Collins, Luciano Mutti

Bibliographic record

VenueGenes & Cancer · 2017
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsGenome British ColumbiaUniversity of British ColumbiaBC Cancer Agency
FundersCanadian Institutes of Health ResearchBC Cancer FoundationMitacsMichael Smith Health Research BC
KeywordsMesotheliomaMedicineTumor microenvironmentContext (archaeology)Cancer researchHypoxia (environmental)DiseaseCancerPathologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Malignant mesotheliomas are aggressive, asbestos-related cancers with poor patient prognosis, typically arising in the mesothelial surfaces of tissues in pleural and peritoneal cavity. The relative unspecific symptoms of mesotheliomas, misdiagnoses, and lack of precise targeted therapies call for a more critical assessment of this disease. In the present review, we categorize commonly identified genomic aberrations of mesotheliomas into their canonical pathways and discuss targeting these pathways in the context of tumor hypoxia, a hallmark of cancer known to render solid tumors more resistant to radiation and most chemo-therapy. We then explore the concept that the intrinsic hypoxic microenvironment of mesotheliomas can be Achilles' heel for targeted, multimodal therapeutic intervention.

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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
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 head0.119
GPT teacher head0.386
Teacher spread0.267 · 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

Citations26
Published2017
Admission routes2
Has abstractyes

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