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Record W2979172176 · doi:10.2741/4839

The role of iron in viral infections

2019· review· en· W2979172176 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.

Bibliographic record

VenueFrontiers in bioscience · 2019
Typereview
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHepcidinHemochromatosisHepatitis C virusVirologyVirusMedicineImmunologyBiologyInflammationInternal medicine

Abstract

fetched live from OpenAlex

Crucial cellular processes such as DNA synthesis and the generation of ATP require iron. Viruses depend on iron in order to efficiently replicate within living host cells. Some viruses selectively infect iron - acquiring cells or influence the cellular iron metabolism via Human hemochromatosis protein (HFE) or hepcidin. During infection with human immunodeficiency virus (HIV), hepatitis B virus (HBV) or hepatitis C virus (HCV) iron overload is associated with poor prognosis for the patient and enhanced progression of the disease. Recent findings still lack to fully describe the viral interaction with the host iron metabolism during infection. This review summarizes the current knowledge of the viral regulation on the host cell iron metabolism in order to discuss the therapeutic option of iron chelation as a potential and beneficial adjuvant in antiviral therapy.

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 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 score0.996
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.300
Teacher spread0.286 · 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