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Record W2810856200 · doi:10.3389/fimmu.2018.01480

Hepatitis C Vaccines, Antibodies, and T Cells

2018· review· en· W2810856200 on OpenAlexafffund
Naglaa H. Shoukry

Bibliographic record

VenueFrontiers in Immunology · 2018
Typereview
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchPublic Health Agency of CanadaNational Institutes of HealthAlberta InnovatesAlberta Innovates - Health SolutionsPublic Health Agency
KeywordsVirologyAntibodyMedicineImmunology

Abstract

fetched live from OpenAlex

The development of vaccines that protect against persistent hepatitis C virus (HCV) infection remain a public health priority. The broad use of highly effective direct-acting antivirals (DAAs) is unlikely to achieve HCV elimination without vaccines that can limit viral transmission. Two vaccines targeting either the antibody or the T cell response are currently in preclinical or clinical trials. Next-generation vaccines will likely involve a combination of these two strategies. This review summarizes the state of knowledge about the immune protective role of HCV-specific antibodies and T cells and the current vaccine strategies. In addition, it discusses the potential efficacy of vaccination in DAA-cured individuals. Finally, it summarizes the challenges to vaccine development and the collaborative efforts required to overcome them.

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)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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)0.0040.002

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 head0.032
GPT teacher head0.335
Teacher spread0.303 · 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

Citations74
Published2018
Admission routes2
Has abstractyes

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