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Record W2088903681 · doi:10.1182/blood-2004-12-4844

Hepcidin in iron overload disorders

2005· article· en· W2088903681 on OpenAlexaff
George Papanikolaou, Michalis Tzilianos, John Christakis, Dionisios Bogdanos, Konstantina Tsimirika, Julie MacFarlane, Y. Paul Goldberg, Nikos Sakellaropoulos, Tomas Ganz, Elizabeta Nemeth

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsXenon Pharmaceuticals (Canada)
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsHepcidinFerroportinHemochromatosisHereditary hemochromatosisHAMPInternal medicineEndocrinologyAnemiaMedicine

Abstract

fetched live from OpenAlex

Hepcidin is the principal regulator of iron absorption in humans. The peptide inhibits cellular iron efflux by binding to the iron export channel ferroportin and inducing its internalization and degradation. Either hepcidin deficiency or alterations in its target, ferroportin, would be expected to result in dysregulated iron absorption, tissue maldistribution of iron, and iron overload. Indeed, hepcidin deficiency has been reported in hereditary hemochromatosis and attributed to mutations in HFE, transferrin receptor 2, hemojuvelin, and the hepcidin gene itself. We measured urinary hepcidin in patients with other genetic causes of iron overload. Hepcidin was found to be suppressed in patients with thalassemia syndromes and congenital dyserythropoietic anemia type 1 and was undetectable in patients with juvenile hemochromatosis with HAMP mutations. Of interest, urine hepcidin levels were significantly elevated in 2 patients with hemochromatosis type 4. These findings extend the spectrum of iron disorders with hepcidin deficiency and underscore the critical importance of the hepcidin-ferroportin interaction in iron homeostasis.

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)0.001
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.237
Teacher spread0.231 · 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

Citations444
Published2005
Admission routes1
Has abstractyes

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