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Record W2950938257 · doi:10.1128/jvi.01757-18

HIV-1 Antisense Protein of Different Clades Induces Autophagy and Associates with the Autophagy Factor p62

2018· article· en· W2950938257 on OpenAlexafffund
Zhenlong Liu, Cynthia Torresilla, Yong Xiao, Phuong Nguyen, Clément Caté, Karina Barbosa, Éric Rassart, Shan Cen, Steve Bourgault, Benoı̂t Barbeau

Bibliographic record

VenueJournal of Virology · 2018
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversité du Québec à Montréal
FundersCanadian Institutes of Health ResearchCanada Research ChairsGovernment of Canada
KeywordsAutophagyBiologyImmune systemCell biologyAntigen presentationViral replicationPathogenesisMechanism (biology)Human immunodeficiency virus (HIV)VirologyVirusImmunologyGeneticsT cellApoptosis

Abstract

fetched live from OpenAlex

In the present study, we provide the first evidence that a new HIV-1 protein termed ASP derived from different clades acts similarly in inducing autophagy, an important cellular process implicated in the degradation of excess or defective cellular material. We have gained further knowledge on the mechanism mediating the activation of autophagy. Our studies have important ramifications in the understanding of viral replication and the pathogenesis associated with HIV-1 in infected individuals. Indeed, autophagy is implicated in antigen presentation during immune response and could thus be rendered inefficient in infected cells, such as dendritic cells. Furthermore, a possible link with HIV-1-associated neurological disorder (HAND) might also be a possible association with the capacity of ASP to induce autophagy. Our studies hence demonstrate the importance in conducting further studies on this protein as it could represent a new interesting target for antiretroviral therapies and 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 head0.011
GPT teacher head0.267
Teacher spread0.256 · 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

Citations21
Published2018
Admission routes2
Has abstractyes

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