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Record W2979172176 · doi:10.2741/4839

The role of iron in viral infections

2019· review· en· W2979172176 on OpenAlexaff
Sophie Schmidt

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.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
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

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

Citations70
Published2019
Admission routes1
Has abstractyes

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