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Record W2033465391 · doi:10.1021/bi300752r

Mechanisms of Mammalian Iron Homeostasis

2012· review· en· W2033465391 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiochemistry · 2012
Typereview
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsMcGill UniversityJewish General Hospital
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteCanadian Institutes of Health Research
KeywordsIron homeostasisHepcidinHomeostasisCell biologyIntracellularCofactorFerroportinIron deficiencyChemistryBiologyMetabolismBiochemistryAnemiaEnzymeImmunologyInflammationMedicineInternal medicine

Abstract

fetched live from OpenAlex

Iron is vital for almost all organisms because of its ability to donate and accept electrons with relative ease. It serves as a cofactor for many proteins and enzymes necessary for oxygen and energy metabolism, as well as for several other essential processes. Mammalian cells utilize multiple mechanisms to acquire iron. Disruption of iron homeostasis is associated with various human diseases: iron deficiency resulting from defects in the acquisition or distribution of the metal causes anemia, whereas iron surfeit resulting from excessive iron absorption or defective utilization causes abnormal tissue iron deposition, leading to oxidative damage. Mammals utilize distinct mechanisms to regulate iron homeostasis at the systemic and cellular levels. These involve the hormone hepcidin and iron regulatory proteins, which collectively ensure iron balance. This review outlines recent advances in iron regulatory pathways as well as in mechanisms underlying intracellular iron trafficking, an important but less studied area of mammalian 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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.308
Teacher spread0.274 · 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