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Record W2066423573 · doi:10.1002/cam4.72

Characterization of <scp>hERG1</scp> channel role in mouse colorectal carcinogenesis

2013· article· en· W2066423573 on OpenAlexfundno aff
Antonella Fiore, Laura Carraresi, Angela Morabito, Simone Polvani, Angelo Fortunato, Elena Lastraioli, Angelo Pietro Femia, Emanuele De Lorenzo, Giovanna Caderni, Annarosa Arcangeli

Bibliographic record

VenueCancer Medicine · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsnot available
FundersBC Cancer AgencyIstituto Toscano TumoriAssociazione Italiana per la Ricerca sul CancroEnte Cassa di Risparmio di Firenze
KeywordsAzoxymethaneCarcinogenesisAngiogenesisDownregulation and upregulationCancer researchGenetically modified mouseColorectal cancerVascular endothelial growth factorTransgeneInternal medicineChemistryBiologyMedicineCancerVEGF receptorsBiochemistryGene

Abstract

fetched live from OpenAlex

The human ether-à-go-go-related gene (hERG)1 K(+) channel is upregulated in human colorectal cancer cells and primary samples. In this study, we examined the role of hERG1 in colorectal carcinogenesis using two mouse models: adenomatous polyposis coli (Apc(min/+) ) and azoxymethane (AOM)-treated mice. Colonic polyps of Apc(min/+) mice overexpressed mERG1 and their formation was reverted by the hERG1 blocker E4031. AOM was applied to either hERG1-transgenic (TG) mice, which overexpress hERG1 in the mucosa of the large intestine, or wild-type mice. A significant increase of both mucin-depleted foci and polyps in the colon of hERG1-TG mice was detected. Both the intestine of TG mice and colonic polyps of Apc(min/+) showed an upregulation of phospho-Protein Kinase B (pAkt)/vascular endothelial growth factor (VEGF-A) and an increased angiogenesis, which were reverted by treatment with E4031. On the whole, this article assigns a relevant role to hERG1 in the process of in vivo colorectal 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)0.000
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 score0.082
Threshold uncertainty score0.325

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 head0.010
GPT teacher head0.229
Teacher spread0.220 · 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

Citations22
Published2013
Admission routes1
Has abstractyes

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