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Record W1990385611 · doi:10.1182/blood-2005-02-0629

Contribution of Hfe expression in macrophages to the regulation of hepatic hepcidin levels and iron loading

2005· article· en· W1990385611 on OpenAlexafffund
Hortence Makui, Ricardo Soares, Wenlei Jiang, Marco Constante

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsCentre Hospitalier de l’Université de MontréalHôpital Notre-Dame
FundersCanadian Institutes of Health Research
KeywordsHepcidinHemochromatosisHereditary hemochromatosisEndocrinologyInternal medicineSpleenBone marrowFerroportinChemistryBiologyAnemiaMedicine

Abstract

fetched live from OpenAlex

Hereditary hemochromatosis (HH), an iron overload disease associated with mutations in the HFE gene, is characterized by increased intestinal iron absorption and consequent deposition of excess iron, primarily in the liver. Patients with HH and Hfe-deficient (Hfe-/-) mice manifest inappropriate expression of the iron absorption regulator hepcidin, a peptide hormone produced by the liver in response to iron loading. In this study, we investigated the contribution of Hfe expression in macrophages to the regulation of liver hepcidin levels and iron loading. We used bone marrow transplantation to generate wild-type (wt) and Hfe-/- mice chimeric for macrophage Hfe gene expression. Reconstitution of Hfe-deficient mice with wt bone marrow resulted in augmented capacity of the spleen to store iron and in significantly decreased liver iron loading, accompanied by a significant increase of hepatic hepcidin mRNA levels. Conversely, wt mice reconstituted with Hfe-deficient bone marrow had a diminished capacity to store iron in the spleen but no significant alterations of liver iron stores or hepcidin mRNA levels. Our results suggest that macrophage Hfe participates in the regulation of splenic and liver iron concentrations and liver hepcidin expression.

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 score0.432
Threshold uncertainty score0.145

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 head0.257
Teacher spread0.246 · 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

Citations68
Published2005
Admission routes2
Has abstractyes

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