MétaCan
Menu
Back to cohort
Record W3087404059 · doi:10.1186/s12985-020-01408-9

Role of HVR1 sequence similarity in the cross-genotypic neutralization of HCV

2020· article· en· W3087404059 on OpenAlexaff
Alexander I. Mosa, Mounir G. AbouHaidar, Richard A. Urbanowicz, John E. Tavis, Jonathan K. Ball, Jordan J. Feld

Bibliographic record

VenueVirology Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsNeutralizationImmunogenVirologyEpitopeBiologyAntibodyNeutralizing antibodyImmunologyVirusMonoclonal antibody

Abstract

fetched live from OpenAlex

Despite available treatments, a prophylactic HCV vaccine is needed to achieve elimination targets. HCV vaccine development has faltered largely because the extreme diversity of the virus limits the protective breadth of vaccine elicited antibodies. It is believed that the principle neutralizing epitope in natural infection, HVR1, which is the most variable epitope in HCV, mediates humoral immune escape. So far, efforts to circumvent HVR1 interference in the induction and function of conserved targeting Ab have failed. Efforts to understand factors contributing to cross-neutralization of HVR1 variants have also been limited. Here, following mouse immunizations with two patient-derived HVR1 peptides, we observe cross-genotype neutralization of variants differing at 15/21 positions. Surprisingly, sequence similarity was not associated with cross-neutralization. It appeared neutralization sensitivity was an intrinsic feature of each variant, rather than emergent from the immunogen specific Ab response. These findings provide novel insight into HVR1-mediated immune evasion, with important implications for HCV vaccine design.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.085
GPT teacher head0.386
Teacher spread0.302 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueVirology JournalSame topicHepatitis C virus researchFrench-language works237,207