MétaCan
Menu
Back to cohort
Record W2463949285 · doi:10.1038/srep29447

HCV RNA Activates APCs via TLR7/TLR8 While Virus Selectively Stimulates Macrophages Without Inducing Antiviral Responses

2016· article· en· W2463949285 on OpenAlexaff
Yuwei Zhang, Mohamed El‐Far, Franck P. Dupuy, Mohamed S. Abdel-Hakeem, Zhong He, Francesco A. Procopio, Yu Shi, Elias K. Haddad, Petronela Ancuța, Rafick‐Pierre Sékaly, Elias A. Said

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsMcGill University Health CentreUniversité de MontréalHôpital Saint-LucCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsTLR7Immune systemInterferonVirologyVirusToll-like receptorBiologyInnate immune systemImmunologyAcquired immune system

Abstract

fetched live from OpenAlex

The innate and adaptive immune systems fail to control HCV infection in the majority of infected individuals. HCV is an ssRNA virus, which suggests a role for Toll-like receptors (TLRs) 7 and 8 in initiating the anti-viral response. Here we demonstrate that HCV genomic RNA harbours specific sequences that initiate an anti-HCV immune response through TLR7 and TLR8 in various antigen presenting cells. Conversely, HCV particles are detected by macrophages, but not by monocytes and DCs, through a TLR7/8 dependent mechanism; this leads to chloroquine sensitive production of pro-inflammatory cytokines including IL-1β, while the antiviral type I Interferon response is not triggered in these cells. Antibodies to DC-SIGN, a c-type lectin selectively expressed by macrophages but not pDCs or mDCs, block the production of cytokines. Novel anti-HCV vaccination strategies should target the induction of TLR7/8 stimulation in APCs in order to establish potent immune responses against HCV.

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 score0.002
Threshold uncertainty score0.007

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)0.0020.001

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 head0.038
GPT teacher head0.332
Teacher spread0.294 · 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

Citations53
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicHepatitis C virus researchFrench-language works237,207