MétaCan
Menu
Back to cohort
Record W2015416458 · doi:10.1002/iub.326

Expression and function of iron‐regulatory proteins in retina

2010· review· en· W2015416458 on OpenAlexaff
Jaya P. Gnana‐Prakasam, Pamela M. Martin, Sylvia B. Smith, Vadivel Ganapathy

Bibliographic record

VenueIUBMB Life · 2010
Typereview
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsTellabs (Canada)
FundersNational Eye InstituteNational Institutes of Health
KeywordsFerroportinHepcidinBiologyRetinaTransferrin receptorHemochromatosisCell biologyRetinal pigment epitheliumRetinal degenerationHomeostasisTransferrinImmunologyEndocrinologyGeneticsNeuroscience

Abstract

fetched live from OpenAlex

Iron is essential for cell survival and function; yet excess iron is toxic to cells. Therefore, the cellular and whole-body levels of iron are regulated exquisitely. At least a dozen proteins participate in the regulation of iron homeostasis. Hemochromatosis, a genetic disorder of iron overload, is caused by mutations in at least five genes, namely HFE, hemojuvelin, Transferrin receptor 2, ferroportin, and hepcidin. Retina is separated from systemic circulation by inner and outer blood-retinal barriers; therefore it is widely believed that this tissue is immune to changes in systemic circulation. Even though hemochromatosis is associated with iron overload and dysfunction of a variety of systemic organs, little is known on the effects of this disease on the retina. Recent studies have shown that all five genes that are associated with hemochromatosis are expressed in the retina in a cell type-specific manner. The retinal pigment epithelium, which forms the outer blood-retinal barrier, expresses all of these five genes. It is therefore clearly evident that iron homeostasis in the retina is maintained locally by active participation of various iron-regulatory proteins. Excess iron is detrimental to the retina as evidenced from human studies and from mouse models of iron overload. Retinal iron homeostasis is disrupted in various clinical conditions such as hemochromatosis, aceruloplasminemia, age-related macular degeneration, and bacterial and viral infections.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.023
GPT teacher head0.294
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations61
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueIUBMB LifeSame topicIron Metabolism and DisordersFrench-language works237,207