MétaCan
Menu
Back to cohort
Record W2066042133 · doi:10.3390/ijms11114741

Copper Transporter 2 Content Is Lower in Liver and Heart of Copper-Deficient Rats

2010· article· en· W2066042133 on OpenAlexafffund
Jesse Bertinato, Sébastien Duval, Mary R. L’Abbé

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2010
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsUniversity of TorontoHealth Canada
FundersHealth Canada
KeywordsIn vivoInternal medicineEndocrinologyCytosolTransporterMessenger RNACopperChemistryBiologyBiochemistryGeneMedicineEnzyme

Abstract

fetched live from OpenAlex

Copper (Cu) transporter 2 (Ctr2) is a transmembrane protein that transports Cu across cell membranes and increases cytosolic Cu levels. Experiments using cell lines have suggested that Ctr2 expression is regulated by Cu status. The importance of changes in Ctr2 expression is underscored by recent studies demonstrating that lower Ctr2 content in cells increases the cellular uptake of platinum-containing cancer drugs and toxicity to the drugs. In this study, we examined whether Ctr2 expression is altered by a nutritional Cu deficiency in vivo. Ctr2 mRNA and protein in liver and heart from rats fed a normal (Cu-N), moderately deficient (Cu-M) or deficient (Cu-D) Cu diet was measured. Rats fed the Cu-deficient diets showed a dose-dependent decrease in liver Ctr2 protein compared to Cu-N rats. Ctr2 protein was 42% and 85% lower in Cu-M and Cu-D rats, respectively. Liver Ctr2 mRNA was 50% lower in Cu-D rats and unaffected in Cu-M rats. In heart, Ctr2 protein was only lower in Cu-D rats (46% lower). These data show that Cu deficiency decreases Ctr2 content in vivo.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.326
Teacher spread0.292 · 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 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

Citations9
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Molecular SciencesSame topicTrace Elements in HealthFrench-language works237,207