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Record W2738794292 · doi:10.1182/blood-2017-04-777987

Immunoassay for human serum erythroferrone

2017· article· en· W2738794292 on OpenAlexaff
Tomas Ganz, Grace Jung, Arash Naeim, Yelena Ginzburg, Zahra Pakbaz, Patrick B. Walter, Léon Kautz, Elizabeta Nemeth

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsUniversity of Victoria
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood Institute
KeywordsImmunoassayPathophysiologyMedicineInternal medicineEndocrinologyImmunologyComputational biologyBiologyAntibody

Abstract

fetched live from OpenAlex

Erythroferrone (ERFE) is a glycoprotein hormone secreted by erythroblasts in response to stimulation by erythropoietin (EPO). We previously demonstrated that ERFE messenger RNA expression and serum protein concentration increase in mice subjected to hemorrhage or EPO therapy, that ERFE acts on hepatocytes to suppress hepcidin, and that the resulting decrease in hepcidin augments iron delivery for intensified erythropoiesis. We also showed that ERFE contributes to pathological hepcidin suppression and iron overload in mice with nontransfused β-thalassemia. We now report the development and technical validation of a rabbit monoclonal antibody-based sandwich immunoassay for human ERFE. We use this assay to show that blood loss or EPO administration increases serum ERFE concentrations in humans, and that patients with both nontransfused and transfused β-thalassemia have very high serum ERFE levels, which decrease after blood transfusion. The assay should be useful for human studies of normal and disordered erythropoiesis and its effect on 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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.007

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 head0.041
GPT teacher head0.325
Teacher spread0.284 · 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 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

Citations128
Published2017
Admission routes1
Has abstractyes

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