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Record W2064416623 · doi:10.1126/science.1256304

Proliferation of cells with HIV integrated into cancer genes contributes to persistent infection

2014· article· en· W2064416623 on OpenAlexfundno aff
Thor A. Wagner, Sherry McLaughlin, Kavita S. Garg, Charles Y. K. Cheung, Brendan B. Larsen, Sheila Styrchak, Hannah C. Huang, Paul T. Edlefsen, James I. Mullins, Lisa M. Frenkel

Bibliographic record

VenueScience · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchInstitució Catalana de Recerca i Estudis Avançats
KeywordsVirologyBiologyGenePersistence (discontinuity)Viral replicationVirusHuman immunodeficiency virus (HIV)Cell growthCancerGenetics

Abstract

fetched live from OpenAlex

Antiretroviral treatment (ART) of HIV infection suppresses viral replication. Yet if ART is stopped, virus reemerges because of the persistence of infected cells. We evaluated the contribution of infected-cell proliferation and sites of proviral integration to HIV persistence. A total of 534 HIV integration sites (IS) and 63 adjacent HIV env sequences were derived from three study participants over 11.3 to 12.7 years of ART. Each participant had identical viral sequences integrated at the same position in multiple cells, demonstrating infected-cell proliferation. Integrations were overrepresented in genes associated with cancer and favored in 12 genes across multiple participants. Over time on ART, a greater proportion of persisting proviruses were in proliferating cells. HIV integration into specific genes may promote proliferation of HIV-infected cells, slowing viral decay during ART.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.259
Teacher spread0.251 · 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 designObservational
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

Citations707
Published2014
Admission routes1
Has abstractyes

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