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Record W2001288674 · doi:10.1002/pmic.201400280

Quantitative proteomics reveals differentially expressed proteins in murine preneoplastic intestine in a model of intestinal tumorigenesis induced by low dietary folate and MTHFR deficiency

2014· article· en· W2001288674 on OpenAlexafffund
Daniel Leclerc, Kurt Dejgaard, Alexander M. Mazur, Liyuan Deng, Qing Wu, Tommy Nilsson, Rima Rozen

Bibliographic record

VenuePROTEOMICS · 2014
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsMcGill University Health CentreMontreal Children's Hospital
FundersCanadian Institutes of Health Research
KeywordsMethylenetetrahydrofolate reductaseCarcinogenesisBiologyColorectal cancerProteomeProteomicsDownregulation and upregulationCancerInternal medicineCancer researchEndocrinologyBiochemistryGeneticsMedicineGeneAllele

Abstract

fetched live from OpenAlex

Colorectal cancer risk is increased when dietary folate intake is low, with or without a deficiency in methylenetetrahydrofolate reductase (MTHFR). We have observed that intestinal tumors are induced in mice fed low-folate diets, and that tumor incidence is increased when these mice also have MTHFR deficiency. This study was undertaken to identify differentially expressed proteins in conditions favoring initial steps of murine carcinogenesis in normal preneoplastic intestine. We compared the proteome of BALB/c normal intestine in Mthfr(+/+) mice fed control diets for 1 year (low susceptibility to tumorigenesis) with the proteome of Mthfr(+/-) animals fed low folate diets (higher tumor susceptibility). Our data suggest that the NuRD complex, KRAS-related proteins, the protein synthetic machinery, and fatty acid-related metabolic proteins are upregulated in the early stages of tumorigenesis. These proteins may serve as biomarkers or targets for colorectal cancer diagnosis or therapy.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.289
Teacher spread0.253 · 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.

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

Citations9
Published2014
Admission routes2
Has abstractyes

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